Robert Oates
email: robert.oates@nottingham.ac.uk

Daniele Scrimieri
email: daniele.scrimieri@nottingham.ac.uk

Svetan Ratchev
email: svetan.ratchev@nottingham.ac.uk

Accelerated Ramp-up of Assembly Systems through Self-Learning

Keywords: Self-Learning, Ramp-up, Assembly Systems 1

The ramp-up process of assembly systems has a huge impact on both the productivity of those systems and the quality of the output. In this work we present a new technique for accelerating the ramp-up process by automatically capturing knowledge about a machine and subsequently reusing it to inform an engineer performing ramp-up. This technique relies on a novel process called the Knowledge Object Algorithm. The technique is explained and demonstrated using synthetic data, designed to emulate a typical use case of such a system. The future direction for this work is also outlined and further experiments detailed.

Introduction

The ramp-up phase of a new assembly device is the time from when the build phase ends, to when the machine reaches maximum productivity. This process includes tuning the machine to reach optimum performance and incrementally enabling components of the machine. Decisions made during this period dramatically change the time it takes to deploy a machine to a site. As a result accelerating this process would represent a significant saving for manufacturing enterprises and systems integrators alike. During ramp-up, all adjustments, whether they directly improve the system or not, add information about the machine. Such data can be analysed and used to provide decision support for the machine they were gathered on and other machines with overlapping functionality.

The structure of this paper is as follows: In Section 2, ramp-up will be discussed in terms of the state-of-the-art in the field of manufacturing and relevant background from the field of machine learning will be introduced; in Section 3 a new technique for capturing and using knowledge from the ramp-up process is presented; in Section 4 the new technique is demonstrated using an artificial case study; and in Section 5, conclusions are drawn from this case study and a discussion about the future of this work is presented.

Related Work

Ramp-Up Acceleration and Performance Indicators

There has been a lot of interest from the research community in reducing the rampup phase of production systems. The aim is to bring to manufacturing enterprises a considerable economical advantage by reaching the desired production and quality level as soon as possible, thereby decreasing the time-to-market. A number of investigations have focussed on improving the efficiency of this process [START_REF] Fjällström | Information enabling production ramp-up[END_REF][START_REF] Ceglarek | Time-based competition in multistage manufacturing: Stream-of-variation analysis (SOVA) methodology-Review[END_REF][START_REF] Haller | Cycle time management during production ramp-up[END_REF][START_REF] Terwiesch | Learning and process improvement during production ramp-up[END_REF]. The ramp-up process is heavily human-driven and usually requires detailed knowledge about the manufacturing technologies and processes involved. Part of the knowledge is acquired only during the process itself, which is essentially executed by trial and error and, as such, is difficult to plan and predict. All the decisions are therefore taken by shopfloor operators based on their experience. Many researchers have emphasized the need for analysing the ramp-up process empirically, especially in early stages, and acquiring the resulting knowledge [START_REF] Terwiesch | Learning and process improvement during production ramp-up[END_REF][START_REF] Carrillo | Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time[END_REF][START_REF] Haller | Cycle time management during production ramp-up[END_REF]. Knowledge capture and formalisation would avoid human knowledge loss and favour its reuse and sharing. In [START_REF] Terwiesch | Learning and process improvement during production ramp-up[END_REF] it is advised that production should be reduced in the early ramp-up stages in favour of learning. This would rapidly increase the production once a proper understanding of the process has been built. In [START_REF] Fjällström | Information enabling production ramp-up[END_REF] the lack of systematic mechanisms for knowledge capture and reuse has been pointed out as one of the problems affecting ramp-up times in a case study in the automotive sector.

In order to minimise ramp-up time, it is helpful to be able to measure and compare the performance levels reached at specific points. This would permit the quantification of the quality of improvements obtained in each stage and guide the process. The use of measures in manufacturing systems has been analysed in numerous production contexts and many methodologies and techniques have been developed for quantifying different measurable aspects and for designing the appropriate measures [START_REF] De Ron | Measure of manufacturing performance in advanced manufacturing systems[END_REF][START_REF] Hon | Performance and evaluation of manufacturing systems[END_REF][START_REF] Kaplan | The balanced scorecard--measures that drive performance[END_REF][START_REF] Neely | Performance measurement system design: A literature review and research agenda[END_REF]. However, none of these methodologies have been specifically defined for rampup. In addition, most of the metrics are of a strictly financial nature and do not satisfy all manufacturing needs. In [START_REF] Hon | Performance and evaluation of manufacturing systems[END_REF], the importance of different factors, such as cost, time, quality, flexibility and productivity are highlighted, while in [START_REF] De Ron | Measure of manufacturing performance in advanced manufacturing systems[END_REF] aspects like availability, reliability and responsibility are proposed as candidates for the design of metrics. In [START_REF] Doltsinis | A Framework for Performance Measurement during Production Ramp-up of Assembly Stations[END_REF] a framework is presented for the systematic measurement of ramp-up performance in order to support a decision-making process which eventually will lead to ramp-up time reduction. The application of such a framework allows for the determination of performance targets and permits shop-floor operators and automatic learning and adaptation systems to get feedback on the effect of performed actions on both specific measured aspects and the overall system performance. The feedback obtained in this way supports the evaluation of the ramp-up process and gives an indication of how far the current system state is from the target.

Machine Learning and the K-Nearest Neighbour Algorithm

The field of machine learning (ML) is a collection of algorithms and techniques for solving problems which require inference and/or generalization about information. ML is vast and a full review is outside the scope of this paper. Instead a brief intro-duction to the theory behind the classification problem will be presented, with particular attention paid to the k-Nearest Neighbour algorithm (KNN). The interested reader is directed to [12] for a general introduction to ML.

The classification problem is the general problem of identifying which, of a group of pre-defined sets a given input vector belongs to. It is typically a "supervised" learning problem, i.e. it is assumed that an indicative "training set" of labelled examples for each class are available for analysis, to allow the generalized properties of the class in question to be inferred.

One algorithm designed to solve the classification problem is the KNN. KNN attempts to classify a given input vector using the k samples which are closest to it from the training set. When k is set to one, the class of the training example which is closest to the input vector determines the class assigned to the input. For higher values of k, the neighbours are used as votes, with the most frequent class determining the class assigned to the input. Draws are typically solved by weighting the votes by the inverse of the distances between them and the input vector.

There are a number of common adaptations of the KNN including the use of different distance measurements, different voting schemes and density-based heuristics for dynamically weighting neighbours [13]. As a result the algorithm is highly tuneable and has enjoyed success at a wide range of classification problems including shape recognition [START_REF] Belongie | Shape Matching and Object Recognition Using Shape Contexts[END_REF], protein structure prediction [START_REF] Kim | Protein β-turn Prediction Using Nearest Neighbour Method[END_REF] and database retrieval [START_REF] Basri | Approximate Nearest Subspace Search[END_REF].

Of note for this work are techniques for classifying data when the dimensionality (the number of parameters used to describe the vectors) of the input vector is different to the dimensionality of the training set. For the ramp-up problem, the dimensionality can rise, (as new parts are added to the machine) or fall, (as parts are taken offline for investigations). Work has been done in the field of support vector machines to handle moving from high dimensional training sets to lower dimensional cases [START_REF] Vapnik | A new learning paradigm: learning using privileged information[END_REF] but the process is one way. Basri et al. make use of a KNN algorithm combined with a surrogate space [START_REF] Basri | Approximate Nearest Subspace Search[END_REF]. For the database retrieval problem the dimensionality, of the database tables (used as a training set) and the input vectors are not always the same, making it difficult to find a metric to measure the distance between the two. The solution to this problem is to transform the input vector and the training set vectors into a surrogate space where both can be represented using the same dimensionality, thus allowing the use of standard distance metrics. The key assumptions being that at least some of the vector dimensions are not entirely orthogonal, (i.e. it is possible to project information from one axis to another) and that a transform exists to move the samples into such a space.

Methodology

The FRAME Project

The FRAME project is aimed at accelerating ramp-up through the development of software tools. Several techniques are being used to achieve this, but here only the techniques involving self-learning will be explored.

The FRAME project uses sensors to capture the current state of the assembly device being monitored. One of the novel features of the FRAME project is the use of a standardized Key Performance Indicator (KPI) component. This allows a manufacturer-specific measure of performance to be applied to the machine's state and provides crucial feedback to the rest of the system. The mechanics of this component are outside the scope of this paper, but are described in [START_REF] Doltsinis | A Framework for Performance Measurement during Production Ramp-up of Assembly Stations[END_REF]. An "experience recognition component" (ER) monitors adjustments made by the engineer working on the machine, either automatically, (for integrated software adjustments) or through the engineer manually entering the adjustment through the HMI. After the system has settled into its new state, the ER creates an "experience", a data structure which contains an aggregate of the system state prior to the adjustment, a description of the adjustment and an aggregate of the system state after the adjustment. All relevant experiences are stored in an experience base. A self-learning component is responsible for converting these experiences into knowledge, which can be used to support decision making about adjustments in the future. When presented with the current state of the machine, a ranked list of possible adjustments is provided to the engineer. If the engineer chooses to perform the recommended adjustment, information about that adjustment is fed back to the self-learning component, via the ER, allowing more informed decisions to be made in the future.

The Knowledge Object Algorithm

The challenge for the self-learning component is to provide decision support about the best adjustment to make, based on the provided experiences, and the current state of the machine. There are several features to this problem that make it challenging. Firstly, as the machine is undergoing ramp-up, the dimensionality of the data being provided to the system is constantly changing as sensors and actuators are brought on and offline whilst debugging the machine. Secondly the number of training samples is extremely low for an ML algorithm to generalise from. Thirdly, the problem must be framed in a way that allows the self-learning component to move between machines with little effort. In [START_REF] Doltsinis | A Model-Free Reinforcement Learning Approach Using Monte Carlo Method in Production Ramp-Up Policy Improvement[END_REF], for example, the ramp-up self-learning problem is treated as a reinforcement learning problem, mapping the adjustments as operations to navigate a space of system states. Unfortunately reinforcement learning techniques not only struggle when the dimensionality of the vectors is large, they also require the individual dimensions to be manually partitioned into states. Arguably, this is selfdefeating in terms of accelerated ramp-up, as savings in the commissioning of the machine, have to be balanced against the effort of manually partitioning the system parameters.

These three factors point towards the use of a solution based on the KNN algorithm as it can be adapted to deal with varying dimensionality, provides an answer from the moment it receives its first training example and does not require manual adjustments between problem domains. Framing the ramp-up problem as a classification problem means treating adjustments as classes, where the object is to classify the current state of the machine as being a member of the set of states which would benefit from a given adjustment.

The knowledge object (KO) algorithm is a novel algorithm, based on KNN, specifically designed for the ramp-up problem. The "before" state of each experience in the experience base is used as the training set for a KNN with k set to 1. Repeating the same adjustment, in the same dimensional space, adds training vectors to the class which represents that adjustment. For new adjustments and for adjustments that have been seen before, but in a different dimensional space, new classes are dynamically generated. Rather than using a simple distance metric, a new similarity metric, termed "affinity" is used, which implicitly performs a non-linear transform into a surrogate space. The affinity between two samples i and j, A ij is given in equation 1.

ܣ = ൜ ߙ -ܦ∆ߚ( + ߛ݈ ), ߙ ≥ ܦ∆ߚ + ߛ݈ 0, ݅ݓݎ݁ℎݐ ݁ݏ (1)
Where α is a starting constant which also defines the maximum affinity, ΔD is the difference in dimensionality between the two vectors, β is a constant which scales the penalty for having different dimensions, l ij is the Euclidean distance between i and j based on the dimensions which exist in both and γ is a constant defining the weight that distance has on the affinity. In cases where there is no overlap between dimensions, the affinity is automatically set to 0.

In addition to affinity, it is also necessary to include an estimate of the effect an adjustment has. Even if the input vector matches the stored state exactly it would be foolhardy to recommend an adjustment which is known to have a negative effect. To achieve this, when an input vector is assigned to a class, an average of the change in KPI caused by that adjustment is returned as an estimate of the reward. The average is weighted by the Euclidean distance between the recorded samples and the input vector. The estimated reward, R ic , for making the adjustment assigned to class c given the input vector i is given by Equation 2.

ܴ = ∑ ∆ܲ ݈ ୀ ∑ 1 ݈ ୀ (2)
Where ΔP is the difference in KPI caused by making adjustment c when in state j and n is the number of samples collected for class c.

In order to make the system as user friendly as possible, rather than displaying two separate figures, the classes are displayed ordered by a third metric, "Applicability". This is simply the product of the affinity and reward for a given class.

Classes are separated into "knowledge objects", software objects that encapsulate the training samples which represent a class and keep track of the difference in KPI caused by the adjustment for each sample. This is done partly for ease of implementation, but also because in the future, new KOs can be developed with other ML algorithms or Expert Systems at their core, and integrated into the KO framework with no changes to the interfaces between components. The final stage of the KO algorithm is a filter which automatically reduces the affinity of objects to zero if they are recommending adjustments to change parameters into a state they are already in.

Synthetic Case Study

For confidentiality reasons, it is not possible to present data from real manufacturing devices in this arena. Instead, to illustrate this methodology a synthetic case study was developed, designed to be indicative of real-world examples of using FRAME during manufacturing ramp-up. Throughout the case study, the KO algorithm is pa-rameterised using α =100, β=10 and γ=1, (illustrative parameterisation identified through trial and error).

Table 1. The steps of the case study. Bold Italics highlight steps recommended by FRAME.

Acknowledgements

This is research has been funded by the European Commission as part of the FP7 NMP FRAME project (CP-FP 229208-2 FRAME).

Step

The case study is based on a simple machine with two adjustable, binary parameters, P 1 and P 2 . It is assumed that the two parameters allow an engineer to move the KPIs of the machine from 60 (P1=0, P2=0) up to 75 (P1=1,P2=1). The two intermediate states of P1=1,P2=0 and P1=0,P2=1 give KPIs of 70 and 72 respectively.

Table 1 represents the case study as a series of parameter changes. The rest of this section is an explanation of how these values were reached using the KO algorithm.

It is assumed that the KPIs of the machine in various states are not known in advance and an engineer is attempting to commission the machine incrementally. Steps 1 through 5 represent the engineer exploring the different states of the parameters, but the optimum state, (P1=1, P2=1) is missed out. The FRAME system captures the information from making those adjustments. At Step 5 FRAME is asked to provide decision support. Table 2 contains the values of the knowledge objects at that step. In this case the highest applicability score is given to changing P2 to 2 as it has the best expected increase in reward and the system state is identical to the state FRAME saw the adjustment in last time.

Step 6 shows the engineer making the recommended change. In Step 7 FRAME is asked for support again. In this case the adjustment with the highest applicability is P1 to 1. This simple example demonstrates the Knowledge Object algorithm recommending a state that the system has never seen before, which in this case yields the best result for the system.

In Step 8 a transducer (T) is introduced. It has no effect on the KPIs as it is a passive sensor. As this is a structural change to the system, it is not counted as an adjustment and creates no knowledge object. In Step 9 the transducer falls to 0 as the KPI falls to 0. This is representative of a conveyor belt stall. When the engineer asks for support (see Table 2) in Step 9 all of the knowledge objects have applicabilities of 0 or less, indicating that FRAME cannot help in this situation. Note the fall in affinity as the dimensionality of the current system state is different to the previously encountered states. The engineer loosens the conveyor and records this manually as a fix, creating a knowledge object with different dimensionality to the other objects.

Step 11 shows a second conveyor stall. In this case the knowledge object with the highest applicability represents loosening the conveyor, restoring the system to full operation. Table 2. Knowledge Object States at various steps. A dash (-) represents that the object did not exist at that stage.

Desc

Step 5

Step 6

Step 9

Step 11 5

Conclusions and Discussion

The simple case study presented in Section 4 is intended to illustrate the operation of the proposed KO algorithm and the FRAME system. A more rigorous set of experiments need to be performed before any firm statements can be made about the validity of this technique, however, the nature of such experiments is difficult to ascertain. Tests using random data are not representative of the practical nature of the problem being examined. Tests using a real industrial machine pose confidentiality issues and will be limited to a single case. It is proposed that a simulated machine would allow the system to explore many different, practically-grounded scenarios that will allow fair comparisons to be made with other techniques, such as those presented in [START_REF] Doltsinis | A Model-Free Reinforcement Learning Approach Using Monte Carlo Method in Production Ramp-Up Policy Improvement[END_REF]. Other challenges for the future include providing KOs with the absolute values of the KPIs for a given state, to prevent large improvements which are only possible when the system has failed, (such as the loosening conveyor system) dominating smaller improvements that allow finer tuning at the upper range of the machine's operation. As the algorithm currently stands, this situation is easily rectified by the engineer by simply performing the erroneous adjustment and providing the system with an example of it yielding no improvement.

Future developments of the FRAME system can be separated into algorithm improvements and FRAME developments. In terms of KO algorithm improvements it may be possible to use a matrix calculation for the γl ij term of the affinity function to allow the effects of distance on affinity to be automatically scaled on a dimension by dimension basis. This will avoid problems when the vector includes both binary parameters and continuous variables with large ranges. In terms of developing the FRAME system, with this framework in place, it is now possible to both merge the results from the KO algorithm with a behavioural model component, allowing more advanced techniques to inform the reward estimates and intelligently interpolate between parameterised adjustments to allow the algorithm to make recommendations that have never been seen before.


K Agyapong-Kodua

Svetan Ratchev
email: svetan.ratchev@nottingham.ac.uk

A methodology for assessing the cost effectiveness of assembly processes

Keywords: Assembly processes, Enterprise Modelling (EM), Simulation Modelling (SM), Manufacturing Enterprises (MEs)

Assembly processes are undergoing frequent changes as a result of the current drive for agility and rapid product solutions. These changes induce complexities and dynamics in the survival of most Manufacturing Enterprises (MEs). To remain competitive, MEs have to continuously and flexibly adjust through the redesign and organisation of their manufacturing and assembly processes as well as resource elements, with the aim to improve 'cost' and 'values' generated. Cost and values are part of key performance indicators necessary for determining the economic viability of assembly processes. The paper therefore presents a methodology capable of capturing, modelling and using information related to cost and value generation for in-depth assembly process analysis. This form of analysis can help determine assembly process efficiency and therefore support the selection or redesign of assembly processes for maximum value realisation at minimal cost.

Introduction

In most assembly processes, different component types are required to flow through networks of resourced activities to enable final (or semi-finished) outputs of various forms to be achieved [START_REF] Agyapong-Kodua | Process cost modelling in Manufacturing Enterprises[END_REF]. This means that assembly processes and their associated resource elements must be designed and organised such that value is added to components along well defined process threads to obtain outputs meeting customer requirements. Best industrial practices recommend that competitive assembly processes should have less cost consumption and high value generation [START_REF] Agyapong-Kodua | Multi-product cost and value stream modelling in support of business process analysis[END_REF] . It is also necessary that values so attained are translated into equivalent monetary 'regard' in the form of prices customers are willing to pay. Currently, many MEs in view of the above reason, design their assembly processes to compete on the basis of their ability to realise economies of scope; by deploying a constrained set of people and technical resources to realise one or more product families [START_REF] Weston | On Modelling Reusable Components of Change Capable Manufacturing Systems[END_REF]. This is not simple to achieve because most MEs are composed of complex interrelated processes such that changes made to one process thread induce dynamics in the ME by having causal and temporal effects on other process threads [START_REF] Agyapong-Kodua | Cost and value stream modelling[END_REF].

Many methods for modelling cost and values have been provided in literature but clearly, none of these methods fully capture the dynamics that impact on cost and value generation in assembly processes. The paper therefore takes an initial look at current best methods for modelling cost and values associated with assembly processes and based on the strengths and weaknesses of existing methods, proposes an innovative modelling methodology capable of capturing aspects of dynamics impacting on processes. This methodology is applied in modelling the product realisation processes of an air-conditioning manufacturing plant based in China.

Modelling cost and values generated by assembly processes

Literature has shown that in broad terms, current best modelling techniques with potential to define, measure and utilise aspects of value and cost information in assembly processes can be classified into:

1. Process Mapping techniques (PMs) [START_REF] Bicheno | The Lean Toolbox[END_REF][START_REF] Womack | Lean thinking -Banish Waste and Create Wealth in Your Corporation[END_REF] 2. Enterprise Modelling (EM) techniques [START_REF]Enterprise integration -Constructs for modelling[END_REF][START_REF] Vernadat | Enterprise modelling and integration; Principles and Applications[END_REF].

3. Cost Modelling (CM) techniques [START_REF] Humphreys | Project and Cost Engineers Handbook[END_REF][START_REF] Akintoye | A survey of current cost estimating practices in the UK[END_REF] 4. System Dynamics (SD) Modelling techniques [START_REF] Forrester | Industrial Dynamics[END_REF][START_REF] Sterman | Business Dynamics: Systems thinking and modeling for a complex world[END_REF] 5. Business Process Simulation Modelling (SM) techniques [START_REF] Kosanke | Process oriented presentation of modelling methodologies[END_REF][START_REF] Carrie | Simulation of Manufacturing Systems[END_REF] PMs (for example: value stream mapping, process activity mapping, overall lead time mapping, product variety funnel, etc.) are not suitable for capturing aspects of complexities and dynamics in assembly processes [START_REF] Agyapong-Kodua | Multi-product cost and value stream modelling in support of Business Process Analysis[END_REF][START_REF] Scholz-Reiter | Modelling and control of production systems based on nonlinear dynamics theory[END_REF]. This is because most of the PM tools were designed for single product flows and do not reflect real-time dynamic instances of multiple assembly processes. It has also been reported that PM tools do not possess the ability to reflect causal impacts of activities on processes [START_REF] Agyapong-Kodua | Process cost modelling in Manufacturing Enterprises[END_REF]. EM tools (for example: ARIS, CIMOSA, GIM, PERA, GERAM, TOGAF, etc) relative to PM tools offer additional modelling concepts that enable the capture of semantically rich models of various aspects of processes [START_REF] Ajaefobi | Human Systems Modelling in support of Enhanced Process Realisation[END_REF][START_REF] Weston | A model-driven, component-based approach to reconfiguring manufacturing software systems[END_REF][START_REF] Vernadat | UEML: towards a unified enterprise modelling language[END_REF]. In theory enterprise mod-elling approaches facilitate the design and development of better assembly processes and systems, and can improve the timeliness and cost effectiveness of change projects in MEs, but full and industry-wide benefit in practice is yet to be realised [START_REF] Vernadat | Enterprise modelling and integration; Principles and Applications[END_REF][START_REF] Agyapong-Kodua | Multi-product cost and value stream modelling in support of Business Process Analysis[END_REF][START_REF] Bernus | Enterprise integration-engineering tools for designing enterprises[END_REF]. They can provide support to any of the modelling techniques explained above in estimating assembly process cost. Literature however shows that SD techniques offer a unique approach towards the modelling of complexities and dynamics in systems [START_REF] Sterman | Business Dynamics: Systems thinking and modeling for a complex world[END_REF][START_REF] Richardson | Reflections for the future of system dynamics[END_REF][START_REF] Wolstenholme | Qualitative verses quantitative modelling: the evolving balance[END_REF]. Later attempts have been made to use these techniques in support of the design of assembly systems [START_REF] Ajaefobi | Human Systems Modelling in support of Enhanced Process Realisation[END_REF][START_REF] Chatha | Multi-process modelling approach to complex organisation design[END_REF]. Little successes have been reported though and this may be due to the inability of these techniques to critically model processes at the elementary level.

Also

A methodology for assessing the cost effectiveness of assembly processes

Observing the strengths and weaknesses of current best modelling methodologies for assessing the cost effectiveness of assembly processes, the authors are of the view that the synergistic application and stage wise integration of enhanced aspects of the 5 modelling techniques would be appropriate. Through the integration, the weaknesses of the individual techniques will be marginalised. Figure 1 shows the various process stages involved in the proposed modelling methodology. At each process stage, the needed inputs are described. For example, to generate enterprise models of MEs, key ME information will be required. This can be derived through interviewing key knowledge holders in the ME and a complementary use of ME data sheets.

Case application of modelling methodology

The case study relates to an engineer-to-order air-condition manufacturing company based in China, herein, referred to as AirCon China. AirCon China specialises in the customised design, manufacture and assembly of air conditioners used at power stations, airports, hospitals, in trains and special environments. The major challenges related to their assembly processes included:

1. The high cost of realising assembly processes 2. Improper planning of assembly processes because of the random nature of their production orders

Modelling the assembly processes for cost and value analysis

Based on the modelling methodology presented in section 3, enterprise models were created to facilitate understanding and provide a basis for in-depth analysis of operations in AirCon China. Based on the Open Systems Architecture for Computer Integrated Manufacturing (CIMOSA) template, several context, interaction, structure and activity diagrams were created to show how Domain Processes (DPs), Business Processes (BPs) and resources interacted. This was considered necessary because the interconnections of activities in the company needed to be understood so that their causal impacts on the assembly process could be adequately modelled. This was considered novel because best literature understanding of process modelling, models in isolation and thus the implication of other activities on the segment of interest cannot be adequately visualised and controlled for ongoing management of businesses. To help reduce the complexities impacting on the assembly processes and to conveniently model the processes, a product-based 'Process-Oriented Configuration' (POC) was used to classification the products. This was based on routing the different products through the different sets of assembly processes and calculating their work content. Based on this exercise, 5 main product types were observed: mostly assemble air cooled A/Cs, mostly assemble water cooled A/Cs, mostly make small sized A/Cs, mostly make large sized A/Cs, and mostly assemble large sized A/Cs. Based on this classification, an initial top level cost and value stream model was created for first-off cost and value analysis (see figure 2). This has been reported in [START_REF] Agyapong-Kodua | Multi-product cost and value stream modelling in support of business process analysis[END_REF]. At the next stage, systems dynamics models were created to capture all the various factors which induced dynamics into the assembly processes. These models reported in [START_REF] Agyapong-Kodua | Multi-product cost and value stream modelling in support of business process analysis[END_REF] were achieved through the use of the causal loop and iThink simulation modelling techniques. Further to this, a discrete event simulation model was created to perform vari-ous experiments related to cost and value realisation. A snapshot of the simulation model created with Simul8 is shown in figure 3.

Fig 2: A top level static cost and value stream model

The operating assumption in this modelling methodology is that value is added to materials when introduced into production and assembly processes. During the value addition process, resources are consumed and therefore cost is incurred in the process.

The model shown in figure 3 is a top level cost and value stream model consisting of sub models representing the elementary activities in the various processes as depicted in the enterprise model.

Several results were obtained through running experiments with the model. For example in the as-is assembly process, the results shown in The authors conducted a number of experiments with the verified model of figure 3 to observe which combinations of resources and organisation of processes best generate high values and low process cost. In principle, many parameters can be used as levers to manipulate the behaviour of virtual production models, but the following were chosen as useful parameters in the context of the research:

1. The cost and values realised during the execution of as-is assembly processes 2. Changes related to product variance and their effects on cost and value genera- Through the experiments, it was observed that when orders related to the production of mostly assemble A/Cs was prioritised over the others, AirCon China achieved very high values. Another relevant observation was that when materials were assumed to be readily available, assembly was harnessed and overall throughput increased as shown in figure 4.

. The paper has presented a methodology for modelling assembly processes and performing various process improvement analyses related to cost improvement and value addition. This consists of the integration of techniques within the domains of enterprise modelling, cost modelling, process modelling, systems dynamics and business process simulation modelling. The case application of these methodology showed that assembly processes can be captured and analysed and based on specified performance indicators, processes can be redesigned and resourced to meet company requirements.

Further research is ongoing to establish how the transformation from one stage of the modelling process to the other can be automated to reduce the effort and rigour required in each of the modelling tools.

Fig 1 :

 1 Fig 1: Proposed modelling methodology The outcome of the Enterprise Model is transferred to the next stage of the modelling exercise together with other data sets on product types and assembly operation times to generate what in this paper is termed as a product-based 'Process-Oriented Configuration' (POC). The derived POC is supported with value and cost constructs together with various process parameters to form an enhanced static cost and value stream model capable of being used for various forms of static cost and value streamanalysis. The end result of this first stage of 'static modelling' can readily be translated into 'fit for purpose' simulation models which can underpin various forms of dynamic cost and value stream analysis. The finally derived model is useful for multiproduct flow dynamic analysis, causal impact demonstrations, dynamic cost and value analysis as well as providing a tool and specific case models for assembly process improvement and redesign.

Figure 3 :

 3 Figure 3: Top level dynamic cost and value stream model

tion 3 .

 3 Changes related to mechanical and human resources and their impacts on cost and values

Figure 4 : 5

 45 Figure 4: Value indications of 'to-be' and 'as-is' modelsAlso observed was that when production schedules were aligned with assembly shop models, inventories were minimised and production cost reduced. These obser-

  Table 1 was obtained. A set of key performance indicators such as inventory cost, operation cost, queue sizes, average queuing time and values generated were chosen to benchmark one experimental result against the others leading to the choosing of the best configuration of assembly processes and resources.

						Product payments, value
		Purchase orders		DP7
				Manage business
						DP3 and DP5
	All types of			Sales Officers/	Sales and Design
	DM2	A/Cs			Designers	cost/value
	Suppliers
		Orders, BOMs, drawings and specs		Computer
						Orders
		Production schedules	DP3 and DP5 Prepare contract and create designs
				and BOMs
			Overall process time: 2-7weeks
			No of H/R: 100
			Op cost and values: see tables
	Various varying					DM1 Customers
	delivery frequencies		Av value added per product			DP6
	Expected weekly production volumes: MAWC A/Cs: 1 MAAC A/Cs: 9 MMSS A/Cs: 3 MMLS A/Cs: 5	MAWC A/Cs: 100,200 RMB MAAC A/Cs: 139,900 RMB MMSS A/Cs:148,200 RMB MMLS A/Cs: 236,700 RMB MALS A/Cs: 278,800 RMB	Machines Operators cost/value	Cost/value MAWC A/Cs	Various varying delivery frequencies
	MALS A/Cs: 32				MAAC A/Cs
			DP6
	components BP6.5 Store materials		Produce A/Cs		MMSS A/Cs
	Initial material values MAWC A/Cs: MAAC A/Cs:		Overall process time = 10days-15weeks	A/Cs and parts	MMLS A/Cs
	MMSS A/Cs: MMLS A/Cs:		No of H/R = 280 Op cost per product:	Storage cost Movement cost	MALS A/Cs
	MALS A/Cs:		see tables
	Storage cost:
	Movement cost:
	1day -3months			10days-15weeks

Table 1 :

 1 Sample result for as-is air conditioners' manufacturing

	BP No.

Top level BPs Total number of operators Average utilization

		Fabricate metal sheet (Metal
	BP6.1	sheet shop)	83	40%
		Make air cooled heat
	BP6.2.1	exchangers	40	42%
		Make water cooled heat
	BP6.2.2	exchangers	26	65%
	BP6.3	Assemble small sized A/Cs	66	80%
	BP6.4	Assemble large sized A/Cs	60	81%
	BP6.5	Store materials	5	40%
		Total	280	58%
				Actual value
			Expected	realized	Expected value
		Actual throughput	throughput	(RMB)	(RMB)
	Mostly assemble
	water cool A/C	5	13	501,000.00	1,302,600.00
	Mostly assemble
	air cool A/Cs	50	118	6,995,000.00	16,508,200.00
	Mostly make
	small size A/Cs	12	42	1,778,400.00	6,224,400.00
	Mostly make large
	size A/Cs	50	78	11,835,000.00	18,462,600.00
	Mostly assemble
	large A/Cs	120	485	33,456,000.00 135,218,000.00
				54,565,400.00 177,715,800.00

Acknowledgement

The authors will like to thank colleagues of the Precision Manufacturing Centre, University of Nottingham, who supported in various ways to achieve the objectives of this research.


Daniel Zdebski
email: daniel.zdebski@nottingham.ac.uk

Shukri Afazov

Svetan Ratchev

Joel Segal

Model based planning of complex micromanufacturing strategies

In recent decades micro-manufacturing becomes increasingly important. Complex multi-material and multi-functional products are required in industries such as bio-medicine, transport, consumer electronics etc. These products, however, cannot be made by single process. Combination of various shaping and assembling processes is required. This paper, gives a systematic overview on the topic of process changing. Clear and systematic process classification system and appropriate design and modelling strategy are presented. Special emphasis is put on planning of complex manufacturing chains. This paper shall help process engineers to make their decisions easier.

Introduction

Our world is changing faster than ever before. The fast trains reach speeds over 500km/h; Aeroplanes fly on the boundary of stratosphere, people living over hundred years in good wealth are not exceptional any longer, we have internet connection in any instance nearly anywhere in the world etc. These all would not be possible without rapid scientific development which we have experienced in last decades. One of the research areas promising even more exciting development is micro engineering. Miniaturization of products is essential for better and accessible health care, sensors facilitating safer transport, low energy consumption systems, mobile communication devices and many others.

The micro-manufacturing processes nowadays are capable to produce very complex 3D features with dimensions of few microns and precision in nanometres level. The processes developed recently are capable of machining exotic materials such as shape-memory alloys, zero-thermal expansion materials, explosive materials, hard ceramics and even diamonds. Various micro-manufacturing processes have been developed and extensively researched in research institutions all around the world.

However, most of the micro-products nowadays are manufactured by single manufacturing process or by combination of very few processes. This practice, however, does not allow manufacturing of real multi-functional micro-systems which are still more and more demanded by various industrial fields. Therefore, chaining of various processes seems to be a key for further development of micro-products. However, the problem of processes' chaining is not well reflected in scientific literature.

Therefore, main scope of this paper is to introduce the problem of chaining of micro-manufacturing processes. New classification system covering all the main stages of micro-manufacturing chain is presented in the first part of this paper. These processes represent main blocks of complex manufacturing chains presented in the second part of this paper. Main logic and method of planning of complex micromanufacturing strategies are presented. This method helps to develop knowledge based micro-manufacturing strategies. It can be used for integration of micromanufacturing processes as well as for optimization of micro-manufacturing strategies.

Classification of micro-manufacturing processes

Vast number of processes can be used for manufacturing of micro components. For effective choice of right manufacturing process it is essential to understand the fundamentals of different processes. Sensible classification of the processes can be very helpful for further planning of manufacturing strategies. The processes can be classified in various manners. Masuzawa [START_REF] Masuzawa | State of the art of micromachining[END_REF] classified the micromachining processes according to the machining phenomena. He divided processes into eight groups; namely: mechanical force, melting/vaporization, ablation, dissolution, plastic deformation, solidification, lamination and recomposition. Alting et. al. [START_REF] Alting | Micro Engineering[END_REF] has extended Masuzawa's classification by description of the material interactions (subtractive, mass containing, additive and joining). At the other hand, Dimov et. al. [START_REF] Dimov | Roadmapping study in multi-material micro manufacture[END_REF] classified the processes based on process dimension and feasibility of the processes for machining of different materials. Another possible classification has been presented by Brinksmeier et. al. [START_REF] Brinksmeier | Machining of precision parts and microstructures[END_REF]. Birksmeier divided the micro-manufacturing processes between processes originally developed for manufacturing of MEMS (e.g. UVlithography, silicon micro machining etc.) and micro engineering technologies (micro milling, micro EDM etc.).

Each of the classification systems presented in literature gives some useful information about the processes. However, they all lack the sequence of the processes in the complete manufacturing chain. Therefore, the classification system introduced in this paper divides the processes into four sections representing the main phases of the micro-manufacturing chain. These phases are: 1. Design & Modelling, 2. Clamping & Referencing, 3. Shaping & Material processing, and 4. Assembly & System integration.

Fig. 1: Classification of micro-manufacturing processes

The whole classification system is illustrated in fig. 1. Obviously the final product quality is affected by each of these phases. Furthermore each phase directly affects the following ones and small errors in the early manufacturing phases can lead to disaster at the end of the manufacturing chain. Therefore, careful planning is fundamental for successful product design.

Processes of each phase classified to groups based on their properties. For example, the processes in shaping & material processing are classified into three main groups according to material interaction. Three main groups are: subtractive, additive and mass containing processes. Furthermore, the processes are grouped according to their physical similarities. This makes easier to choose right processes.

The assembling and packaging processes are then classified in similar manner. In this phase can be identified five main groups of processes. They are: Picking, Positioning, Releasing, Joining and Packaging.

Modelling in micro-manufacturing

Fig. 2: Strategy for successful design and modelling

Appropriate modelling helps to increase the manufacturing efficiency and decrease the total costs. It also helps to reduce danger of the project failure. Therefore, it is crucial to proceed with systematic modelling approach covering all aspects affecting micro-manufacturing process.

The frame of micro-manufacturing modelling is essentially the same as the one for conventional manufacturing. However, due to small dimensions of micro products new challenges arise. The main challenges are: different physical phenomena can be introduced in micro scale, increased level of uncertainties, resolution of the machine tools must be taken in account etc. The fundamental modelling frame is shown in fig.

2. It comprises of three groups representing three main areas of interest. They are: 1)

2) Process and 3) Resources. It is essential for successful planning of manufacturing capabilities and resources to gain comprehensive knowledge on all of these groups.

The product modelling consists of three main phases. In the first phase fundamental physical principles are studied. It is absolutely crucial to have good knowledge about the physical principles responsible for the product functionality. These principles may be different in micro-scale from those known from macro-world. Examples may be a capillary effect or effects of van der Walls forces. Majority of models used in this stage are relatively simple analytical or empirical models. However, often it is not possible to apply common macro-models and new models must be developed. This is because of increasing non-linearity due to various effects appearing in micro world.

Once the physical principles are well understood the product geometry can be designed. This must take in consideration limitations of available manufacturing processes, requirements for appropriate product function, assembly of various components, and many others. Therefore, in this stage the engineer has to use more advanced modelling techniques such as FEM, FVD, BEM, CFD, multi-body rigid dynamics and MD. Furthermore, geometric models must be used to study collisions between components and effects of tolerances on the assembly of the product.

Product life is studied in the last step of product modelling. This includes the product wear prediction and fatigue. Furthermore, this stage should also solve issues with products recycling.

The second big group of models solves issues of product manufacturing. This is represented mainly by process chaining and production flow. Both of them help to optimize the production in order to produce the desired possible product with lowest possible resources (time, human work, costs etc.). This group of models represents the most critical stage of the production planning and therefore it further discussed in following chapter.

Chaining of micro-manufacturing processes

As stated above the modelling of complex micro-manufacturing chains is not resolved yet. Vella et. al. have proposed an solution of the process chaining through solution of process interactions [START_REF] Vela | A Methodology for Technology Maturity Assessment of Micro and Nano Manufacturing Processes and Process Chains[END_REF]. However, this approach is not appropriate due to large number of interactions between various processes. Main difficulty is in necessity of solving interactions between individual processes. Furthermore, these interactions are not mutual.

Therefore, in this chapter the main strategy for successful process chaining is presented. The concept of this strategy is based on interchangeable blocks representing individual processes. Each process has many inputs and outputs. For example hardness and surface roughness of EDMed surface depends on workpiece and tool electrical resistivity and thermal conductivity, and process parameters. At the other hand mechanical properties do not influence the output properties of the EDMed surface any significantly. Similar examples can be found for all the other manufacturing processes. The relations between various input and output parameters are different for various process. However, some general rules must be kept in order to ensure changeability of the processes. These rules are:

 Each of the process blocks has same input and output parameters,  The parameters must be same for all processes,  Models used to solve the links between inputs and outputs must be fast and simple to use,  The models must take in account process uncertainties, General process block is illustrated in fig. 3. Three main groups of inputs and outputs are considered. They are: Information, Material and Energy. Each of these groups contains sequenced data. For example information contains data about shape, surface quality, machining time, costs, product location and many others. In same manner material contains information about material properties such as hardness, stiffness, density, thermal conductivity, electrical resistivity etc. Energy then contains data about energy magnitude, form, stability etc. The links between various inputs and outputs are solved by individual process models.

Fig. 3: Illustration of general process

A major issue in micro-manufacturing are high uncertainties. Electrical noise, geometrical tolerances, environmental noise, material impurities has much higher effects on the output parameters in micro scale than in the case of conventional dimensions. D. Zdebski et. al. have presented problem of dimensional tolerances on example of commercial micro end-mills [START_REF] Zdebski | A Numerical Analysis of Dimensional Tolerances Effects on the Failure of Micro End-mills[END_REF]. They found, that typical tolerances of Ø0.2 mm micro end-mills represent 15% of nominal dimensions. This variation of the tools has enormous effect on the tool life and product quality. Therefore, the models used for micro-manufacturing processes must take in account the issue of uncertainties. Especially if complex manufacturing chains are considered the final product uncertainties can be in order of output values. Therefore, all the inputs and outputs should be in form of probability distribution instead of single value (see fig. 4).

Hence, it is clear that this approach requires high number of calculations. Therefore, the models used to solve the links between inputs and outputs should be as fast as possible. Therefore, FEM and other numerically expensive methods are not the right solutions. Simple analytical models are preferable.

Fig. 4: Illustration of inputs and outputs as a probability distribution function, example of relation of micro end-mill's stresses as a function of tool dimensions

In fig. 5 is shown a typical process chain. The inputs of the chain are the product requirements and available resources. The requirements can be solve by the approach presented in previous chapter, and they are mainly depended on the product functionality and needs of system integration.

Example of micro-pump

In fig. 6 is shown micro-pump. The overall dimensions of this pump are 500 μm x 200 μm x 200 μm. Although the presented design does not include motor, valves and packaging, it represents relatively complex product. The product is assembled from 6 individual components. These components are made from different materials (inner cylinder, housing and stopper from aluminium, and stopper and gears from thermoplastic). It is clear that such product cannot be made by single process. At the reality each of the components can be made by various process chains. For example, one possible ways of manufacturing the stopper (one of the simplest components of the assembly) is shown in fig. 7. However, this stopper can be manufactured by other rotes based on additive processes or EDM processes.

Fig. 6: Micro-pump design

In the case of stopper there is only one critical dimension. However, in other cases there are much more critical surfaces (e.g. linear gear has 7 critical dimensions, and inner cylinder and rotational gear have both 5 critical dimensions). Also these components require much higher number of processes. This significantly increases the components' inaccuracies. For example if the rotational gear is manufactured by micro injection-moulding it requires at least 16 processes. Each of these processes affects dimensional errors. Hence, it is that after several processes it is not possible to predict any dimensions without assuming probabilistic approach. This, however, requires good prior knowledge of all individual processes as well as application of robust decision making module, which will help users to choose the most efficient and reliable process chain.

Conclusions

In this paper is presented a strategy for planning complex manufacturing chains. This strategy is based on interchangeable blocks instead of solving interactions between individual processes. The main advantage of this strategy is in its low computational expenses. Another advantage is in possibility of adding arbitrary processes independently on each other. The method is proposed to be used with simple analytical models. However, in contrast with commonly used models, it is proposed to use this method in probability domain. This is because of high effects of process uncertainties during micro manufacturing. These uncertainties often accumulate and the cause major problems of assembly of individual components. Also if the probability distribution is used instead of nominal value yield of the process can be estimated.

An example of micro-pump is used to illustrate the process chaining. Whole assembly consist of only six components. However, even this relatively simple product requires minimum of ~150 operations. Also this product may be manufactured by large number of different process chains.

This paper shows only the main logic of process planning. The future work must be conducted mainly on choice of appropriate models for individual processes and development of robust decision making algorithms. This will lead to development of software which will help to plan micro-manufacturing process chains on analytical (numerical)-statistical models.

Fig. 5 :

 5 Fig. 5: Example of typical process chain

Fig. 7 :

 7 Fig. 7: Process chain for manufacturing of stopperIn fig.8is shown an assembly chain for the presented micro-pump. This chain consists of five assembly steps. However, each of these steps contains five substeps: 1)

Fig. 8 :

 8 Fig. 8: Assembly chain

Acknowledgements

The authors wish to acknowledge the Nottingham Innovative Manufacturing Research Centre (NIMRC) and the Engineering and Physical Sciences Research Council (EPSRC) for their financial support of the work.


Markus Dickerhof
email: dickerhof@kit.edu

S Azcarate
email: sazcarate@tekniker.es

A Temun

Towards an European Approach for Characterisation of Multimaterial Micromanufacturing Process Capabilities

Keywords: Information and Knowledge management, Micro Manufacture, Nano Manufacture, Technological Interfaces, Process Ontologies, Technology Maturity

The paper describes a comprehensive approach for the modelling and description of technologies available within a European Research Infrastructure consisting of distributed research infrastructures all over Europe. The resulting knowledge management system integrates the partial approaches of several European research activities in this area to a comprehensive description format for technical capabilities as well as maturity and economic aspects

Introduction

High multidisciplinary and low standardization are still key characteristics of emerging technologies such as MicroNano-Manufacturing (MNT). This observation has origin in ongoing significant technological developments regarding technical capabilities and process maturity. As a consequence stronger dependencies between design decisions and chosen manufacturing processes can be observed than e.g. macro mechanical engineering, where the dependencies between product requirements and technological capabilities in general much lower [START_REF] Dickerhof | A cooperation model and demand-oriented ICT infrastructure for SME development and production networks in the field of microsystem technology[END_REF]. The fact that product development for NanoMicro-Systems requires expert knowledge from various areas such as sensor systems, biotechnology or electronic engineering increases the relevance of those dependencies and makes development much more challenging. When, maybe apparently despite the odds, a solution is reached, the acquired knowledge is highly dependent on the specific project and distributed across several data-mediums in heterogeneous formats, as such it is hard to reuse. Consequently, possible promising ideas in Nano-Micro-Manufacturing and design are often discarded as lacking potential, because there is no way to access required knowledge in a centralized, homogeneous, shared, collaborative and cost-effective manner. It may well be the case that solutions to problems arising from these ideas have been in part already addressed in previous projects, however the lack of documented information renders these solutions inaccessible. Besides an incomplete documentation of the technical boundary conditions project reports and publications often solely refer to successful results and do not describe the failures and setbacks. An excellent tool to overcome these issues can be found using knowledge management methodologies and techniques. This is the point where the MINAbase integrated Information and knowledge management approach, being further developed in the context of the European research infrastructure EUMINAfab [START_REF] Kautt | Facilitating Open Innovation in Micro and Nano Technology by Providing Open Access to a Pan-European Toolbox called EUMINAfab[END_REF] creates added value to the researchers work. MINAbase is designed to collect, store and interpret information along the Microsystems development process in a way that allows the researchers of the participating European organisations and -projects and later also external users to benefit from the reuse of well structured data, collected in previous activities. The paper describes the key modelling elements of the integrated EUMINAfab knowledge management system and gives an overview on the implementation status as well as an outlook to the activities, planned for the further filling of the database.

EUMINAfab Information and Knowledge management Framework

The overall objectives to be achieved are: -Provide a "solution neutral" but application specific interface between research groups when assessing user requests as well as along the prototyping or batch manufacturing. -Provide a mapping of micro device functional aspects and manufacturing capabilities. -Provide Design for Manufacture (DfM) generic rules that are EUMINAfab specific and open to the users of the infrastructure.

In the following we will explain how these objectives are addressed from the point of view. of a holistic process modelling.

Description basics and Technology Capability maps

It is important to stress that individual MNT are limited in their capabilities for producing 3D free-form micro structures in a wide range of materials. The way in which the key capabilities can be described was and still is a matter of discussion in many projects. In EUMINAfab seven categories of manufacturing processes have been classified, adapting and improving results such as from the European Network of excellence 4M. In EUMINAfab all technologies available within the consortium had been classified with respect to these seven categories. In addition a categorization scheme for characterization principles had been established (Figure 1). In addition a scheme for description of technology related aspects has been introduced, providing information on of the following elements:

-Contact information -Technological constraints (feature density, aspect ratio…) -Material classes and types (Polymers, ceramics,..) -Information about the technology (design rules, references,…) -Typical designs / structures (geom. Features, coatings, film,…) -Reference applications -Equipment (Machines, tools,…)

Assessment of Technologies

Assessment of technologies -allowing an oriented selection of appropriate manufacturing processes is essential for handling a user request. In practice, the maturity level of a given process is determined by characterizing it with a set of indicators that are representative of its overall behavior, performance and operational environment. The following indicators have been identified: Maturity Levels. Maturity is "the state of being fully grown or developed" . From a manufacturing perspective, some of the characteristics of a mature process are that it is well understood, the necessary documentation and operator formal training are readily available and also it is continuously monitored and improved. Consequently, the performance and the overall behavior of such a process are highly predictable. Therefore, the maturity of a specific process or process pair provides one with the means to predict the likelihood of achieving particular process outcomes when it is used to fabricate a given product or component. Maturity levels are well-defined evolutionary stages towards achieving a mature Micro-Nanomanufacturing process or process pair. The following two description mechanism had been adopted from [START_REF] Brousseau | Technology Maturity Assessment of Micro and Nano Manufacturing Processes[END_REF][START_REF] Fraser | The Use of Maturity Models/ Grids as a Tool in Assessing Product Development Capability[END_REF] for later implementation:

Key Process Capability Areas. The maturity of a given technology in regards to its key process capability areas (KPCA) can lie at any of the five levels described in Table 1. The KPCA maturity is a prerequisite for achieving or improving the overall maturity of a process. The maturity indicators can be used in determining whether different issues and goals related to the process capability parameters and their associated KPCA have been addressed or whether their key parameters metrics have been improved.

Process Maturity Indicators.

These indicators describe the typical benchmarking activities, characteristics and performance metrics for each maturity level. For example, an indicator for the maturity of the "Part Size & Complexity", "Material" and "Processing" KPCA could be the existence of a correlation between analytical modeling/simulation results and actual process performance in a given environment.

To implement the proposed methodology it is necessary to identify a set of maturity indicators, e.g. through Delphi-type workshops with MNT process experts. The proposed modeling approach was applied to assess the maturity of different state-of-the-art micro nano-manufacturing processes integrated within EUMINAfab.

Technical aspects for description of technical aspects

To ensure a reuse of process related information the methodology should, besides the maturity related parameters, allow for an order-independent description and storage of technology-oriented parameters. The ProWiDa methodology for classifying aspects of influences through taxonomies with inheritance bases on the assumption that for an adequate description of technological capabilities all influencing aspects (technical, economic, human, etc.) need to be considered [START_REF] Dickerhof | Increasing efficiency and transparency of micro system product and process modeling -from process documentation to process knowledge management in micro systems technology[END_REF]. For the technical section the following "aspects" have been identified as of relevance:

-Materials (substrates and layers) -Procedure -Most critical geometry -Machines -Tools

Integration of all aspects of influence on "competence" level

Competences represent at first hand a n-tuple of the above mentioned aspects (see Figure 2). Competences may contain -in addition to the underlying aspect-related parameters-on "compe-tence level" additional parameter sets representing the technical capabilities of an organisaton unit (Figure 3).

-Process parameters describe typical technical factors that can be achieved in a reliable manner, such as width, length, material parameters, milling tool size, feed, milling velocity, etc. Also economical parameters such as machine costs can be attached at this level. -Application parameters describe the resulting capabilities being aimed for, e.g. resulting minimum groove width, surface roughness, which again correlate with application specific requirements coming from physical effects. -Design rules are used to describe expert knowledge that cannot be reduced to a parameter level (e.g. design rules of a mold) -Besides specific application related technical parameters, maturity related and economic aspects can also be placed on competence level since they usually correspond not only to a specific technology but also to the material chosen, etc. In addition these combinations (competences) usually are dedicated to a specific group or company. Instantiations of these competences can be used to store information gained along the processing of a user request. The specific combination of aspects is the called "process step" and represents the smallest unit of a process chain described in the next chapter in a more detail. Through its full database representation the description methodology again allows for an automated generation of installation-, material, technology and feature-specific datasheets on a very detailed description level.

Modelling of Process Chains

In the ProWiDa methodology three major elements allow for the description of a complete production process (Figure 4).

-The process chain represents a more or less complete order / product specific set of process steps. Hence the resulting parameters on this highest aggregation level are of relevance for the application. -Process sequences represent a characteristic of a basic technology. The technology is represented by a set of process steps. -The process step itself builds the smallest modelling element, which represents a subtask that has to be executed when processing a technology.

An example sequence for such a generic microsystems related process sequence is shown in Figure 6. In EUMINAfab an implementation of a specific realisation of processes sequences based on the idea of using pairs in order satisfy specific application requirements will be implemented (see Figure 6). In this case the pre and post conditions of the ProWiDa methodology are equivalent to the technological interfaces.

Process chain ttt

Implementation and filling of the database

It is obvious to mention, that methodologies are only as good as their acceptance in the end user community. "Acceptance" means -besides the acceptance of the methodology itself-to ensure, that the users are set into the to handle underlying procedures and data in an as intuitive as possible manner. In EUMINAfab two systems had been developed/adapted and set up:

Information management system

The Information management system has three key functions in EUMINAfab -It supports the operational activities of the network management through an implemented semi collaborative workflow engine [START_REF] Dickerhof | INFOFLOW -a process oriented workflow and information system for micro system development and production[END_REF]. The engines allow for a user request-oriented provision of information and tools along the processing of a user request. This feature gains importance with an increasing amount of tasks being processed in parallel. -A set of internal organisational processes for intake, evaluation, work breakdown, realisation as well as procedures to escalations had been defined to ensure quality and repeatability of decisions. -Information systems also ensures a well structured collection of manufacturing related data in a traceable way, which is mandatory for a later analysis of decision processes regarding technological and economic relations.

Implementation in the MINAbase online-database

Since the MINAbase concept is based on the collection of hundreds of individual competences, a complex ontology has been built up, allowing for a highly flexible storage of technological competences. The retrieval of data in the different taxonomies of the MINAbase ontology is ensured through full text search functionality as well as through the implementation of filtering mechanisms (Figure 7). For the remaining time of the EUMINAfab project it is planned to expand this toolset by case based reasoning strategies for an improved retrieval and also by a modelling module to allow for the modelling of constraints between process steps in a micro manufacturing process chain.

Filling the database

Initial Datasets have been collected at the very beginning of the EUMINAfab project in the first version of the technology capability maps. However the ability to assess the appropriateness of a technology regarding a given user task requires an in depth knowledge on the technological capabilities according to the description methodology mentioned above.

Incorporation of benchmarking results

Benchmarking is used in EUMINAfab for ranking purposes, and therefore it could eventually also be applied for process chain selection when there are alternative competing solutions for the fabrication of a given micro component.

In MINAbase the production and analysis of special artefacts has been established to collect information on the process capabilities and especially on the quality constraints. A number of artefacts have been machined in JRA3.1 and will in early 2011 be analyzed by KTH using high performance metrology systems. [START_REF] Ekberg | Ultra-precision geometrical measurement technique based on a statistical random phase clock combined with acoustic-optical deflection[END_REF]. As an example, in the accomplished measurements we have found cases where machine accuracy was off by an order of magnitude. Deviations of such large extent definitely call for corrective actions to improve the quality of EUMINAfab installations. Laboratory audits have been performed by NPL personal at different installations with both good and bad results regarding traceability issues. An inventory of machine-to-machine substrate handling has been performed. It is the responsibility of each partner to update the installation performance in MINAbase. Still it is striking that universities can put lots of investment in machining and characterization equipment but hesitate to buy traceable test artefacts for a fraction of the equipment cost. Notwithstanding in the last few months the interest of the different manufacturing units to perform machining tests just to evaluate the performance of their equipment has been strengthened.

Results from first stage quality assessment

The establishment of a quality management system to collect parameters from user requests is getting close to being fulfilled. By evaluating the outcome of a certain number of user projects each year we can also identify and evaluate the selection principles used so far.

Moreover the EUMINAfab knowledge database will benefit from each user project. As an example of necessary feedback, the discrepancy between experts predicted time to fulfill a process task and the actual time needed to do it differs significantly at several installations. With that experience brought into the knowledge database the likelihood of better planning is obvious. A drawback of the relatively few fulfilled projects is the large spread among the process requests, however.

Outlook

The methodology presented in this paper is actually lacking of two important aspects: Application related aspects are only considered indirectly, e.g. by the provision of information about geometrical aspect or the maturity related aspects. To ensure the usability of the MINAbase approach aspects such as the specifc requirements of process scalability in the biosector, relevance of technologies for a certain industry need to be also collected. Secondly aspects being of relevance for the later assignment of technologies to a process chains and related procedural rules need to be implemented to ensure compatibility between consecutive process steps. Indirectly related to this is also the pending connection to design tools for modelling of physical behaviour of micro enabled parts.

Figure 1 :

 1 Figure1: Seven categories of Technologies[START_REF] Azcarate | Hybrid Tooling: A Review of Process Chains for Tooling Microfabrication in 4M[END_REF]

Figure 2 :

 2 Figure 2: Technology-oriented "aspects" of a process step

Figure 3 :

 3 Figure 3: Installation, technology and aspect specific Parameter sets in a competence

Figure 4 :

 4 Figure 4: Scheme of the major modelling elements in MINAbase according to the Matroshka Principle [7]

Figure 5 :

 5 Figure 5: Process sequences in the ProWiDa Methodology

Figure 6 :

 6 Figure 6: Process Pairs and Process Pair Technological Interfaces[START_REF] Vella | A Methodology for Technology Maturity Assessment of Micro and Nano Manufacturing Processes and Process Chains[END_REF]

Figure 7 :

 7 Figure 7: MINAbase retrieval page with EUMINAfab Competences

Table 1 :

 1 Process Capability Maturity Levels


R Caravita

S Aghion

C Amsler

A Ariga

T Ariga

G Bonomi

P Bräunig

J Bremer

R S Brusa

A Demetrio

L Di Noto

M Doser

O K Forslund

S Gerber

S Haider

H Holmestad

T Huse

I L Jernelv

E Jordan

T Kaltenbacher

A Kellerbauer

M Kimura

T Koetting

Towards a gravity measurement on cold antimatter atoms

( 1 ) Physics Department, CERN, 1211 Summary.

-The present status of the AEGIS experiment at CERN (AD-06), on the way of forming anti-hydrogen for a first gravity measurement, is reviewed.

Recent results in trapping and cooling positrons and antiprotons in the main electromagnetic traps are presented, including the storage time measurement obtained during the 2014 run with antiprotons, the observation of centrifugal separation of a mixed antiproton/electron plasma and positron accumulation and transfer results obtained during 2015.

-Introduction

One of the most recent and promising research avenues in physics is the antimatter research community's current effort to exploit achievements in producing and cooling down bunches of more than 10 7 antiprotons in the Antiproton Decelerator facility (AD) at CERN to produce significant amounts of thermalized anti-hydrogen atoms. As a result, systematic studies of atomic-physics scale properties of antimatter, such as gravity or atomic spectrum, would become feasible.

Measuring directly gravity on anti-hydrogen to 1% precision is the ambitious objective of the AEGIS collaboration [1]. The measurement scheme chosen by AEGIS is one of the simplest to detect gravity on massive objects, i.e. throw horizontally a massive body -in this case, a cloud of anti-hydrogen atoms -and observe its free-fall parabolic trajectory in a constant (Earth's) gravitational field. By measuring the time of flight of the atomic cloud and the vertical displacement using a a moiré deflectometer, g can be promptly worked out (see [2]).

-AEGIS anti-hydrogen production scheme

The scheme chosen by AEGIS to produce anti-hydrogen is positron capture by an antiproton that interacts with a Rydberg-excited Positronium atom, first obtained in [3]. This method, compared to the traditional positron/antiproton plasma mixing (see [4]), shows several advantages. First, being a pulsed scheme, allows to know the time of flight of the formed atoms. Secondly, since antiprotons are not actively moved (by shaping trap potentials) towards a high-density positron plasma, the resulting anti-hydrogen temperature is lower. Finally, the production cross-section grows as the fourth power of the principal quantum number of the positronium atom.

A sketched scheme of the experiment is shown in fig. 1, left panel. A positronium converter made of mesoporous silica placed about 1.7 cm above the anti-hydrogen production trap facing downwards towards the antiproton storage trap. Positrons implanted with keV energy are converted into positronium atoms, which thermalize in the nanochannels and get emitted in vacuum with an overall efficiency of about 9% (see [5]). Two prisms carry two pulses of laser radiation in front of the converter to excite the emitted fraction of Ps to Rydberg levels, using a two step excitation scheme 1 → 3 → n, as described in [6]. Rydberg excited positronium survive direct annihilation into gamma rays macroscopic time, allowing the excited atoms to fly towards the antiproton storage trap and perform charge exchange with the cold plasma of antiprotons. A positron plasma is first prepared in a 2.2 cm radius trap, then moved out of the main axis of the experiment by exciting its autoresonant diocotron mode via radiofrequencies (demostrated with electrons in [7]).

-Experimental apparatus

AEGIS consists in a cylindrical cryostat containing two sets of cylindrical Malmberg-Penning traps in different magnetic field regions. A first set of traps, in front of the AD beam line, is used to catch and cool antiprotons and positrons in a high magnetic field at 4.5 T (see fig. 1, right panel). After cooling, particles are transferred to a second set of traps in a lower magnetic field region at 1.0 T where the main manipulations described above are performed. Positrons are produced outside the main cryostat on a different beam placed vertically above the AD transfer line by radioactive decay of a 22 Na source. Subsequently, a magnetic buffer-gas accumulator stores the positrons using a well established technique (see [8]). A magnetic transfer line tilted by 45 degrees carries positrons down from the accumulator to the antiproton beam line, so to share the same catching hardware. Antiprotons are sent from AD in bunches of about 3.0 • 10 7 particles every 110 s, with an axial energy of 5.3 MeV. A degrading system made of several μm thick aluminum foils is used to lower the energy of antiprotons by means of multiple scattering in the degrading material. Subsequently, a Penning-Malmberg trap terminated by high-voltage electrodes captures a fraction of the degraded particles with energies lower than the catching voltage. Antiprotons were trapped by using a 75 cm long Malmberg trap with the final high-voltage electrode set to a constant negative bias and a pulsable high-voltage electrode as entrance endcap.

-Antiproton run 2014

The primary goal of the run period of 2014 was to develop and commission the procedures required in forming anti-hydrogen. Trapping conditions of the first commissioning AEGIS run in 2012 were reproduced quickly; a trapping potential of 9 kV was used routinely, which allowed to trap more than 10 5 antiprotons/shot. Electron cooling of antiprotons was also reproduced. A plasma of electrons was loaded before capture in the Penning-Malmberg trap by digging a small potential hole in the center of the trap. The source of electrons is the primary current emitted from a themoelectric filament. Cooling efficiencies close io 90% in about 60 s were routinely obtained, when the electron cloud almost completely overlaps that of the antiprotons.

A critical parameter to the lifetime of cold antiprotons in trap after electron cooling was the pressure of the residual gas in the trap. The vacuum level could not be directly measured with gauges: the best available diagnostics is the lifetime curve itself, since the number of antiproton annihilations per unit second depends on the pressure of the residual gas in the traps (see fig. 2, left panel). The radius of the antiproton plasma was kept monitored by imaging the plasma over a microchannel plate detector, to avoid annihilations on the walls due to the radial enlargement of the cloud.

In fig. 2, right panel, an example of antiproton image on an MCP after electron cooling is shown. Electrons are selectively removed from the trap before the antiproton ejection by using a sequence of fast pulses, with a delay time sufficiently short to be negligible with respect to the characteristic times of the antiproton cloud evolution. Formation of ring structures was reproducibly observed, as expected for a cold two species plasma with different mass at thermal equilibrium in the same trap. This effect, known in the literature as centrifugal separation and observed first for electrons and antiprotons by the ATRAP collaboration [9], is a direct indication that the antiproton plasma has a very low temperature. For a given electron density n, the minimal temperature necessary to observe centrifugal separation at a radius r is given by

T min = m p e 2 8k B 2 0 nr B 2 . ( 1
)
Measurements obtained with the method of centrifugal separation in 2014 run give a minimum temperature of 10-20 K, in agreement with the environment temperature around the trap region of 10 K.

-Positron run 2015

Significant progresses in storing and transferring positrons were carried out during 2015. Large number of positrons were collected within the buffer-gas accumulator (as shown in [10]). Positrons were kept accumulating for around 6 minutes, before being transferred to the main catching traps in the 4.5 T region. Losses positron transfer was achieved after optimization of the transfer line parameters: around 2.5 • 10 7 positrons were routinely transferred and trapped per shot. Positrons were transferred with a axial velocity corresponding to 300 eV: this energy was dissipated in a fraction of second if the number of positrons transferred exceeded a threshold -experimentally determinedaround 1.3 • 10 7 positrons per shot. Below this limit, efficient cooling conditions were not found anymore, probably due to the too low density of the plasma. With higher number of positrons, almost 100% cooling efficiency was routinely obtained, and no significant loss due to vacuum was observed, as shown in fig. 3.

In order to reach the reference value of 10 8 positrons for anti-hydrogen production, several shots from the accumulator had been collected and stacked into the catching traps. With the current 22 Na source, around 10 mCu of activity at the time of the experiment, 10 8 positrons were obtained stacking 8 shots, corresponding to around 45 minutes of accumulation time (see fig. 3). A new, stronger, source of 50 mCu will replace the present source soon, nominally allowing 10 8 positrons/shot to be reached in about 8 minutes.

-Conclusions

AEGIS entered its first data-taking period with both antiprotons and positrons at the beginning of 2015. Significant progresses towards a first experimental demonstration of anti-hydrogen formation for a gravity measurement were obtained in short time. Many goals to produce anti-hydrogen by charge exchange still have to be experimentally demonstrated, from off-axis recapture of the positron plasma to positronium formation in high magnetic field and its laser excitation to Rydberg levels. These goals, together with the spectroscopic studies of positronium carried out in a dedicated enviroment, constitues the rich scientific programme of AEGIS for the next years.

Fig. 1 .

 1 Fig. 1. -Left panel: schematic drawing of AEGIS experimental layout to produce anti-hydrogen by charge exchange with Rydberg-excited positronium. Right panel: a view of the Malmberg-Penning traps used to capture antiprotons and positrons.

Fig. 2 .

 2 Fig. 2. -Left panel: number of counts on external scintillators after the annihilation of the electron-cooled antiproton fraction, as a function of the storage time. The steepness of the exponential decay curve is a measurement of the vacuum quality within the storage traps. Right panel: image of an antiproton plasma recorded by a microchannel plate detector.

Fig. 3 .

 3 Fig. 3. -Left panel: measurement of positron number stored in the 4.5 T catching traps stacking several shots from the accumulator. Each stack corresponds to around 6 mins of accumulation (2000 pulses from the trap). Right panel: positron plasma lifetime measurement in the 4.5 T catching traps. No loss of positrons has been observed after 30 min of particle storage.

  Geneva 23, Switzerland ( 2 ) Department of Physics, University of Genova, via Dodecaneso 33, 16146 Genova, Italy ( 3 ) INFN, Sezione di Genova, via Dodecaneso 33, 16146 Genova, Italy ( 4 ) Politecnico of Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy ( 5 ) INFN, Sezione di Milano, via Celoria 16, 20133 Milano, Italy ( 6 ) Laboratory for High Energy Physics, Albert Einstein Center for Fundamental Physics and University of Bern, 3012 Bern, Switzerland ( 7 ) Department of Mechanical and Industrial Engineering, University of Brescia, via Branze 38, 25123 Brescia, Italy ( 8 ) INFN, Sezione di Pavia, via Bassi 6, 27100 Pavia, Italy ( 9 ) Kirchhoff-Institute for Physics, Heidelberg University, Im Neuenheimer Feld 227, 69120 Heidelberg, Germany ( 10 ) Department of Physics, University of Trento, via Sommarive 14, 38123 Povo, Trento, Italy ( 11 ) TIFPA/INFN Trento, via Sommarive 14, 38123 Povo, Trento, Italy ( 12 ) Laboratoire Aimé Cotton, Université Paris-Sud, ENS Cachan, CNRS, Université Paris-Saclay, 91405 Orsay Cedex, France ( 13 ) Department of Science, University of Insubria, Via Valleggio 11, 22100 Como, Italy ( 14 ) Department of Physics, University of Milano, via Celoria 16, 20133 Milano, Italy ( 15 ) Max Planck Institute for Nuclear Physics, Saupfercheckweg 1, 69117 Heidelberg, Germany Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0) ( 16 ) Czech Technical University, Prague, Bøehovà 7, 11519 Prague 1, Czech Republic ( 17 ) Institute of Physics and Technology, University of Bergen, Alleegaten 55, 5007 Bergen, Norway ( 18 ) Institute for Nuclear Research of the Russian Academy of Science, Moscow 117312, Russia ( 19 ) Department of Physics, University of Oslo, Semaelands vei 24, 0371 Oslo, Norway ( 20 ) Stefan Meyer Institute for Subatomic Physics, Austrian Academy of Sciences and Boltzmanngasse 3, 1090 Vienna, Austria ( 21 ) Joint Institute for Nuclear Research, 141980 Dubna, Russia ( 22 ) INFN, Sezione di Padova, via Marzolo 8, 35131 Padova, Italy ( 23 ) Institute of Nuclear Physics, CNRS/IN2p3, University of Lyon 1, 69622 Villeurbanne,

	France
	( 24 ) University of Bologna, Viale Berti Pichat 6/2, 40126 Bologna, Italy
	( 25 ) Department of Physics, University of Pavia, via Bassi 6, 27100 Pavia, Italy
	( 26 ) Department of Physics, University of Napoli Federico II, Complesso Universitario di Monte
	S. Angelo, 80126, Napoli, Italy
	received 7 January 2016


Christine Azevedo Coste
email: christine.azevedo@inria.fr

Milos R Popovic
email: milos.popovic@utoronto.cawinfried.mayr@meduniwien.ac.at

Winfried Mayr

International Functional Electrical Stimulation Society, IFESS

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Editorial

Ejtm Special: 20 th Conference of the International Functional Electrical Stimulation Society, IFESS

The International Functional Electrical Stimulation Society (IFESS, http://www.ifess.org/) is an inter-professional and collaborative organization, whose main objective is promotion of research, applications and understanding of electrical stimulation as it is utilized in the field of medicine. In this special edition of EJTM we are proud to present the latest studies and projects that are being conducted by the scientists, practitioners and trainees in the broader IFESS community. In this special issue you will find articles about  brain machine interfaces and how they can be used to control functional electrical stimulation devices,  electrical stimulation for regeneration and reinnervation following peripheral nerve injury,  sequential electrical stimulation,  electrical stimulation based cueing in Parkinson's disease patients, and  implantable gastrointestinal stimulators for obese patients to name a few.

We are confident that you will enjoy reading these articles as much as we have enjoyed reviewing and preparing them for this special issue. We also wish to thank all authors of the articles that have contributed to EJTM, and for making the Ejtm Special Section in this and next issues an exciting and intriguing read.

  One of the ways IFESS contributes to the field of electrical stimulation is by organizing annual conferences that are located in different continents. The IFESS community is composed of Academic Researchers, Medical Doctors, Physiotherapists, Occupational Therapists and Engineers. This year IFESS has had the privilege to have the conference organized by Dr. Christine Azevedo Coste from Inria (France). In her efforts Dr. Azevedo Coste was supported by CAMIN, an Inria team hosted by the Informatics, Robotics and Microelectronics Laboratory (LIRMM, Montpellier) and Prof. Milos R. Popovic from the Toronto Rehabilitation Institute and University of Toronto (Canada) as program cochair. The organization committee also included the board of directors of IFESS, which is directed by Dr. Thierry Keller (Tecnalia, Spain). This year's conference took place in La Grande-Motte, France from June 8 to 10. The topic of this IFESS conference was "Hybrid Approaches to Functional Electrical Stimulation". Since this is the 20 th anniversary of the IFESS Conferences, we wanted to make the conference articles available to a broader audience. Prof. Winfried Mayr, who is a foundation member of IFESS, from the Medical University Vienna, Center for Medical Physics and Biomedical Engineering, suggested that we approach the European Journal of Translational Myology (EJTM) to collaborate on this project. EJTM and its Founder and Editor-in-Chief, Prof. Ugo Carraro from Fondazione Ospedale San Camillo, Venice, and University of Padova, Italy, very kindly offered to our team to publish the conference articles in EJTM. EJTM, former Basic and Applied Myology (BAM), has a long tradition in integrating physiological, clinical and engineering aspects of muscle related multidisciplinary research. It has been pioneering open access strategies long before others discovered this new barrier-free quality of sharing knowledge within the scientific community, and further with application professionals and interested public. We are very grateful to Prof. Carraro and EJTM for giving us this opportunity, and we hope that this issue of EJTM will inspire new generations of scientists and clinicians to join the very exciting and rewarding field of Functional Electrical Stimulation.


Keoma Brun-Laguna
email: keoma.brun@inria.fr

Carlos Oroza
email: coroza@berkeley.edu

Ziran Zhang
email: zhangziran@berkeley.edu

Sami Malek
email: sami.malek@berkeley.edu

Thomas Watteyne
email: thomas.watteyne@inria.fr

Steven D Glaser
email: glaser@berkeley.edu

Demo: SierraNet: Monitoring the Snowpack in the Sierra Nevada

come

Introduction

Between 2012 and 2015, California suffered from the highest water drought since recordings started in this state. Up to 2/3 of its water resources are coming from the Sierra Nevada snowpack. Understanding the effect of the droughts on the mountain snowpack is crucial.

Until recently, snow melting and water displacement measurements were done by hand. This manual process is costly and not frequent enough to reflect the environment changes. There was a need for a dense (i.e. in time and space) monitoring solution to capture the spacial variability of the water.

The SierraNet project started in 2009 with the goal of monitoring the Sierra Nevada snowpack without human intervention. At the time of writing, 18 lowpower wireless networks are deployed in the Sierra Nevada. They measure snow level, solar radiation, soil moisture, soil temperature, air temperature and air relative humidity in different locations. The SierraNet project currently includes the American River Hydrological Observatory (ARHO) project [?] with 985 sensors, and the Southern Sierra project [?] with 300 sensors.

SierraNet

The Deployments

The ARHO project deployments [?] are located in the 2000 km 2 American River Basin, above 1500 m altitude, in the central Sierra Nevada, near Sacramento, CA, USA. The ARHO project is designed to provide representative measurements of the snowpack, solar radiation and soil moisture, to enable real-time management of the state's water supply. It is the core element of a new water-resource information system, and a platform for improving Hydro-Electric generation operations using real-time data. The project consists of 945 sensors, organized in 14 independent low-power wireless networks. These networks are deployed in locations strategically chosen to get representative spatial estimates of snow cover, soil moisture and other water-balance components. Data from these sensors are integrated with forecasting models and decision-support tools.

The Southern Sierra deployment [?] is a forested 1 km 2 headwater catchment area. This 300-sensor deployment measures snow depth, solar radiation, relative humidity, soil moisture and matric potential. On top of environmental monitoring, this deployment is used to investigate low-power wireless repeaters placement strategies.

The end-to-end System

Each network of the ARHO and Southern Sierra deployments share the same architecture. Sensor stations are placed in hydrologically-significant locations.

Repeater nodes are added to ensure good connectivity. The sensor data is relayed to the manager node, which is connected to a Linux computer. This computer connects to the Internet through a satellite or cellular link. Seconds after the generated data is produced in the deployment site, it appears in the database and can be visualized online. At the heart of each sensor station is a NeoMote, a low-power wireless platform commercialized by Metronome Systems, a UC Berkeley spin-off company. The NeoMote is a generic sensor platform, which features a Cypress PSoC microcontroller and a SmartMesh IP low-power wireless mote, in a hardened weatherproof design. The NeoMotes are using the SmartMesh IP technology to form low-power wireless networks with >99.999% end-to-end reliability and a over decade of battery lifetime [?].

The Southern Sierra devices were replaced by Neomotes in 2016; the network is now using the Sensor Object Library [?]. SOL allows the network to produce both sensor values and network statistics as augmented Type-Length-Value objects, facilitating data manipulation, query and visualization.

Related Deployment and Lessons Learnt

A similar network is deployed for monitoring and predicting frost events in Western Argentina [?]. The PEACH system measures air temperature and relative humidity, soil moisture and soil temperature over a 110 m × 50 m peach orchard. The collected data is then analyzed with machine learning techniques to predict frost events.

Wireless sensor applications went promising research to using off-the-shelf products implementing standard-based technology. Adapting the system from a frost monitoring in peach orchard application to a snowpack monitoring is now an easy task. Low-power wireless mesh technology successfully transitioned from the academic to the commercial world.

The SierraNet Demo

The purpose of this demo is to present a fully-working replica of the low-power wireless mesh network currently running in the Southern Sierra as part of the SierraNet project. We deploy Metronome System motes in the conference venue. They automatically form a low-power wireless mesh network around the Metronome System network manager. Motes are equipped with air relative humidity sensors. The network manager is connected to the Internet. Sensor measurements and network statistics are sent to the network manager and forwarded to the back-end system running at Inria-Paris. Conference participants can observe the data live at https://sol.paris.inria.fr/.

This demo presents the deployed system as a turn-key solution for monitoring the environment with a low-power and highly reliable technology. We are looking for partners, end users and integrators to replicate the SierraNet system to other deployments and applications.

Acknowledgment

The authors thank the Dust Networks product group at Linear Technology and Metronome Systems for their technical support. This publication was made possible by Inria, the University of Michigan and the University of California Berkeley through the REALMS associate team.

Figure 1 :Figure 2 :

 12 Figure 1: The setup of the SierraNet demonstration, including the motes, the manager, the sensors and the web interface of the back-end system.