Klaus-Dieter Thoben

Marco Lewandowski

Information and Data Provision of Operational Data for the Improvement of Product Development

Keywords: Product Lifecycle Management, Internet of Things, Operation and Maintenance, Maintenance Concepts, Data Mining, Wind turbines

Today's usage of supporting technologies like RFID, condition monitoring or further embedded systems provides a huge amount of data to the operation and maintenance (O&M) phase of complex technical systems. While analyzing this data for the purpose of more efficient operation is already extensively adopted, the transfer of data to other lifecycle phases is most often lacking. This paper will analyze the obstacles and requirements for information and data provision from the usage phase in order to support the development of next generation products. This is carried out by analyzing sub-aspects of data provisioning for product development purposes thus leading to a comprehensive framework for the reorganization of information backflows from the O&M phase. The findings are discussed in the case of a windfarm. The paper gives a valuable insight regarding the derivation of targets and action fields for information and data provision to improve the product development process.

Introduction

In increasingly more complex technical systems, a broad range of supporting technologies nowadays characterizes the operation and maintenance phase of such systems. These include for instance condition monitoring, RFID or embedded controlling units: Condition monitoring to measure and access the operational conditions, RFID to identify components and record service actions as well as further embedded technologies to capture data from the field level of industrial I/O systems, often referred to as Supervisory Control and Data Acquisition (SCADA) [START_REF] Mobley | An introduction to predictive maintenance[END_REF]. Besides the technical systems, also service protocols of the maintenance staff are most often available in digital form. All sources in total lead to a huge set of structured and unstructured data that is directly related to the use phase of an individual product and provide in general a good impression about the individual operational environment and purpose.

Industrial investment goods face a shift from a product perspective to an integrated perspective on product service systems, which brings the entire product lifecycle of individual products into focus of improvements [START_REF] Wuest | Capturing, Managing and Sharing Product Information Along the Lifecycle for Design Improvement[END_REF]. With regards to the development of new product generations or the continuous improvement of existing machinery designs, it should be of uppermost interest for the designers and manufactures to deduce 'lessons learned' by letting data and information of actual products flow back to the design teams. Referring to the term 'Internet of Things', technological prospects to enable ubiquitous access to operational data exist and are recently on the way to emerging markets [START_REF] Wortmann | Internet of Things[END_REF]. However, to reorganize the general design methodology by means of encompassing operational data, the framework presented in this paper shows an overall approach addressing relevant fields of action.

The remaining parts of the paper are organized as follows. Section 2 states and analyses the overall objective, which is generally requested by design departments and breaks it down into sub-aspects. Section 3 sketches a general framework approach to solve requirements and accordingly discusses concepts for the sub-aspects. Section 4 briefly describes the application of the approach to the case of wind turbines, while section 5 finally sums up the findings and outlines possible further research.

Research Motivation and Fields of Action

From the perspective of product development, the deviated challenge of data provisioning of operational data is to transform data into information and actionoriented knowledge to support the development process. According to the considered case of improving next generation systems or topical designs, the objective encompasses that data of the individual product lifecycle will be stored and processed in order to link the information to the individual tasks.

The Product Design Process in the Context of Data Provision

For the question how designs are created in the field of mechanical engineering, a variety of prescriptive models has been investigated [START_REF] Finger | A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes[END_REF]. According to [START_REF] Finger | A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes[END_REF] a general understanding of the design process is an iterative process encompassing the steps illustrated in the following Fig. 1.

Recognition of need

Specification of requirements

Concept formulation

Concept selection

Exposition of design details

Production, sales, and maintenance Fig. 1. General iterative steps of the design process (according to [START_REF] Finger | A review of research in mechanical engineering design. Part I: Descriptive, prescriptive, and computer-based models of design processes[END_REF])

The recognition of needs describes the general and partly undefined idea of a product or technical solution to face a problem or challenge. A subsequent question for data provision of operational data accordingly addresses a possible support for finding new ideas for new designs or adaptive designs. It can be assumed that essentially systematic problems in topical designs are investigated so that these trigger ideas to adapt or vary designs.

Derived from that, the specification of requirements is a far more detailed description of required properties, which the later system should have. Again, the subsequent question for the data provision is: Does operational data of topical products help to deduce concrete requirements for future designs? The strength of material, the accessibility in case of maintenance actions or the causing of unexpected high operational costs, to name just a few examples, could be possible results of operational data analytics and directly address tasks under the considered step.

With a concept formulation, first technical solutions are generated which face the general need under consideration of the stated requirements. A broad range of tools support this tasks, namely solution catalogs, modelling software, 3D-CAD software, etc. By fulfilling the tasks summarized under this step, tasks have a particular context which might be "strengthening a mounting", "finding a locking mechanism", "lowering production costs", "substituting material" and much more. From the perspective of data provision, a highly aggregated key performance indicator for existent designs -taking into account the collected data -should support and simplify the selection processes for design principles. Aggregated and context-related information has to be integrated seamlessly in the engineering tools (i.e. engineering desktops, software or the like).

In particular regarding this sub-aspect of data and information provision, also the concept selection profits form an underlying fundamental experience for different design principles. Again, the challenge from the design perspective is to extract experience and practical knowledge from data and link it to design principles.

The exposition of design details addresses detailed requirements on calculations, computations or further verifications. The output of this step should be a concrete technical design. Anyway, verification methods even if done with modern methods like Finite Element Method (FEM) are still based on load assumptions or the like. An idea of data provisioning from operational use is also to have more realistic assumptions regarding loads or operational environments, as those are continuously monitored referring to the general concept.

Production, sales, and maintenance are the classical transition from the beginning of life phase to the middle of life or usage phase. While the design process is an iterative process, a continuous target-performance comparison should be intended essentially in this phase by means of comparing aggregated key performance indicators.

To sum up the individual sub-aspects, by and large a knowledge infrastructure has to be built in order to acquire, convert, process, apply and also protect the operational data, which was captured by the mentioned supporting devices. The capabilities of a data provision framework should include a) reporting on systematic problems, b) support on selecting appropriate design principles, c) support for realistic verification and d) continuous target/performance comparison. To establish this derived knowledge concept with design departments, technology, structure, and cultural or organizational acceptance is required [START_REF] Gold | Knowledge Management: An Organizational Capabilities Perspective[END_REF].

With respect to the latter, from the practical perspective, it is required to translate relevant issues to key performance indicators (KPIs) so that a continuous monitoring can take place. These could be 'new product introduction time', 'new products failure rate', 'new products success rate', etc. and should be significantly improved with respect to data provisioning. Furthermore, a crucial point includes the support on selecting appropriate design principles which could reach from manual design principles to decision support systems like case based reasoning (CBR).

Closed-Loop PLM as a Concept for the Technical Infrastructure

The concept of closed-loop product lifecycle management underlines essentially the challenges form the technical perspective. Closed-loop in this sense defined by [START_REF] Jun | Closed-loop PLM[END_REF] describes "as strategic approach for the effective management of product lifecycle activities by using product data/information/knowledge which are accumulated in the closed-loops of product lifecycle […]". This is further illustrated in the following Fig.

2.

The system, which is operational in the field, works additionally as permanent test rig and shares information driven from captured data all over the product lifecycle. Stakeholders from the design departments or the lifecycle engineering have the possibility to access, manage, and control the related information. Furthermore, when adopting the concept of closed loop PLM, information can also flow from the first life-cycle phases to the usage phase, which could be for instance 3D-models, failure mode analysis or simulations which could support service technicians in their work [START_REF] Jun | Xirouchakis P Research issues on closed-loop PLM[END_REF]. Jun et al. [START_REF] Jun | System architecture for closed-loop PLM[END_REF] describes an approach for a system architecture that enables the concept.

Beginning-of-Life

Middle-of-Life End-of-Life R&D usage recycling production e.g. 3D-models simulations e.g. for (predictive) maintenance Usage data i.e. processed information "more" cyber "more" physical Fig. 2. Closed-loop PLM as technical concept for data provision for the product development process

The Concept of the Product Avatar for Context-Specific Information Provision

A further concept encompasses the digital or cyber representation of the real product by means of the so called "Product Avatar". To manage the communication between the intelligent system and the different stakeholders in the design department, the stakeholder-specific digital representation called "Product Avatar" was introduced by [START_REF] Hribernik | The Product Avatar as a product-instance-centric information management concept. Product lifecycle management : emerging solutions and challenges for global networked enterprise[END_REF]. The core idea of the "Product Avatar" implies that each product has a digital counterpart by which it is represented. The different involved stakeholders are able to gain access to the information acquired within the lifecycle [START_REF] Wuest | Digital Representations of Intelligent Products: Product Avatar 2.0[END_REF]. Consequently, suitable interfaces have to be provided, which are for instance a common messaging interface, service or agent. For the design engineers these interfaces are for example dedicated desktop applications or plugins in existing software, web pages or mobile "apps" tailored to the specific information and interaction needs [START_REF] Wuest | Digital Representations of Intelligent Products: Product Avatar 2.0[END_REF]. In general, the concept of the "Product Avatar" promises to establish a greater acceptance in the organizational structure of design departments.

2.4

The Cyber-Physical System as the Vision for Autonomous Data Exchange

Acquiring and using data by the systems mentioned above is already practicable today. Regarding the technical progress on supporting technologies or enabling technologies for the concepts described before, so called "Cyber-Physical Systems" play an important role. The first definition was introduced by Lee [START_REF] Lee | Cyber Physical Systems: Design Challenges[END_REF] and later replenished by Broy [START_REF] Broy | Cyber-physical Systems: Innovation durch softwareintensive eingebettete Systeme[END_REF]. According to this, a cyber-physical system could be understood as a physical object, e.g. a wind turbine, which is equipped with sensors, a data processing module, a communication module as well as actuators, which determine and control physical processes and communicate with other systems [START_REF] Lee | Cyber Physical Systems: Design Challenges[END_REF][START_REF] Broy | Cyber-physical Systems: Innovation durch softwareintensive eingebettete Systeme[END_REF]. These modules are highly integrated and embedded into the product. The products detect their own status and their environment through sensors. The data processing module stores these data and the communication unit transfer the aggregated information, to the SCADA system for instance. Human actors or other technical systems analyse the data. They are able to effect the environment via actuators, e.g. actuate an axis, reset a system, change parameters, etc. Regarding the detailed technical challenges for the data analytics, i.e. to provide information and knowledge rather than raw data, integrated concepts are a not solved issue in research and practice. Anyhow, broadly investigated individual methods and tools include statistical techniques, artificial intelligence like fuzzy logic or neural networks, complex event processing, and others.

Framework for the Data and Information Provision

In order to reorganize the product development process in a way that topical system behavior of operating products is considered, we propose a general framework, which addresses fields of actions in three main pillars. These pillars are driven from the requirement statements as well as from known approaches described in the previous section. Fig. 3. General framework approach for the reorganization of the product development process by means of data and information provision of operational data Fig. 3 illustrates the three main pillars, which are technological prerequisites, methodical concepts and procedural implications. The sub-aspects are described in the remaining parts of the section.

Data and Information Provision for the Product Development Process

3.1

Technological prerequisites

The fundamental basis of the complete intention is that products or complex industrial systems gain "intelligence". Intelligence in this sense means, that physical products have the ability to act in an intelligent way. McFarlane et al. define the Intelligent Product as "a physical and information based representation of an item [...] which possesses a unique identification, is capable of communicating effectively with its environment, can retain or store data about itself, deploys a language to display its features, production requirements, etc., and is capable of participating in or making decisions relevant to its own destiny." [START_REF] Mcfarlane | Auto ID systems and intelligent manufacturing control[END_REF] The degree of intelligence may vary, but supporting technologies mentioned before generally provide products with this decision-making responsibility.

Storage and access of raw data ranges from decentralized to centralized approaches, while centralized Internet-of-Things platforms to store and exchange the acquired data build todays state of the art [START_REF] Wortmann | Internet of Things[END_REF]. They serve for a ubiquitous access to the data and accordingly are also required for the further data processing in the product development context. The same platform concept should be aware of integrating further data and information and providing a uniform access to these. As mentioned before, service protocols could be from great interest regarding the designer's requirements but are typically stored in other IT systems.

At least, today's most challenging part is to provide processing capabilities for the data analytics. As discussed before, rather information and knowledge instead of raw data is requested, so that a problem-specific toolset for data processing is required. Anyway, from the IT infrastructural point of view, the state of the art is prepared for the big data challenge in this context [START_REF]IoT and Big Data: A Joint Whitepaper by Bosch Software Innovations and MongoDB 15[END_REF].

Methodical Concepts

The central point of the methodical pillar is the design process itself. Referring to the general requirement analysis in section 2, it is necessary to adopt the general approach to the companies' individual design processes. This means, that starting a data provision project for the design department requires a deep analysis on the particular design steps and tasks of the individual design process. In each of these, one should raise the question, which is the expected output of the data analysis that supports und simplifies the considered task. Following this argumentation, another relevant aspect includes the contextawareness of information. We propose to consider and develop a semantic model that classifies design principles in order to link data categories as well as calculated results to product-related design principles. This is a necessary step to make designs comparable and to use data for enhanced verification procedures.

While this consideration of the context is focused on the considered technical system, the components or design principles, the general product avatar concept as described in section 2 is necessary for filtering information for the different stakeholders. As design tasks for complex systems differ from engineer to engineer, a product avatar-like stakeholder concept will be a success factor for the acceptance of a data provision project.

3.3

Procedural implication

By trying to focus more on experience in the design process by means of data and information provision, the design process itself will slightly change. Our general framework addresses three aspects in two categories. The first category implies the building of a specific application-oriented know how for the data processing tasks. While a broad range of algorithms from statistics or artificial intelligence are present in the scientific literature, design teams will need an aligned toolset for processing tasks and essentially for aggregation tasks, which are in general derived from the first processing.

The second category of implications focuses on human-machine interfaces, which means that information should seamlessly be integrated in the known working environments, e.g. the CAD software, so that the engineers do not have the trouble to use further tools. The aspect is referred to as context-specific interfaces and is essential for the acceptance and success of a data provision project.

First Pilot Implementation: The Case of Wind Turbines

In order to support the idea of the general concept described above, the application on the case of wind turbines is described briefly in the following.

According to Fig. 4, mainly raw data from the turbine itself is collected, but also further context-related data is part of the input. The following step has to encompass the processing of data to gain information for the design department and the life-cycle management. The maintenance team itself is an additional stakeholder but not considered further in this context. More detailed, the flow of data throughout this case is described regarding three pillars, namely input, processing and output.

4.1

Input

The main data sources are the SCADA system and historical data from past maintenance measures, e.g. orders, checklists, feedback forms, etc. Other known and also isolated data sources are historical weather data, weather forecasts, general and statistical reliability data, functional interconnections and possible failure modes.

To answer questions that focus on interlinked aspects of the different data sources, a uniform data access is necessary to enable joint queries. Exemplary questions are for instance:

• Are there specific patterns in the input data of a wind turbine that announce future failures in advance? • Are there specific designs that show a significantly higher failure probability? • Are the simulated or anticipated loads and the derived wear comparable to the measured situations in reality?

Processing

In order to process data, a centralized data cloud concept is proposed according to Fig. 4 and known IoT platforms [START_REF] Wortmann | Internet of Things[END_REF][START_REF]IoT and Big Data: A Joint Whitepaper by Bosch Software Innovations and MongoDB 15[END_REF]. The cloud enables further processing according to the requirements of the design process. To do so, transforming raw data to information requires

• methods that interlink data sources in the sense of data mining in order to search for conspicuous patterns (such as statistical techniques, clustering and pattern recognition, artificial intelligence like neural networks and fuzzy systems, etc.) and software tools for an uniform data access of different data sources, • methods that predict future developments through an algorithmic processing which encompasses pattern recognition, classification, time-series prediction, statistical residual life prediction, etc. also in the sense of an integration of several individually applied methods (e.g. scoring via TOPSIS, analytical hierarchical process or similar), and • methods that are able to apply above mentioned procedures on continuous data streams again taking the complexity of different data sources into account (i.e. aspects of complex event processing [15]).

Output

The information pool that derived from the processing is not specific to context or stakeholders. The described concept of the "product avatar" could filter information and provide communication channels and user interfaces according to the stakeholder's requirements. Each product avatar representation of the wind turbine is a tailored virtual representation of the real turbine.

Managerial Implications and Discussion

The paper described challenges and a general approach so that companies can deal with the question of integrating data and information from real products in the product development process. The lifecycle of a wind turbine reflects today's state of practice and illustrates the application of the conceptual frame.

While the motivation to share information along the lifecycle for the design departments was clarified, data-and information-driven concepts like the "product avatar" will show further progress on applicability to real-world scenarios. There are still obstacles in many companies, but it is necessary to change the access to productrelated data so that information processing can take place.

Within ongoing projects the technically related challenges (e.g. big data, processing times, etc.) of implementing the described concepts are currently investigated and lead to first prototypes. Having a functional proofed framework and performing infrastructure for data processing is the first step for further investigations. So as to be able to give an example, great effort is needed for experiments that deal with the tailored integration of all this information in the existing workplaces.

Fig. 4 .

 4 Fig. 4. Illustration of the case of wind turbines for data provision


Elias Xidias
email: xidias@aegean.gr

Zoi Koutkalaki

Panagiotis Papagiannis
email: papagiannis@aegean.gr

Paraskevas Papanikos

Philip Azariadis

Foot Plantar Pressure Estimation using Artificial Neural Networks

Keywords: Artificial neural network, foot plantar pressure, mechanical comfort

In this paper, we present a novel approach to estimate the maximum pressure over the foot plantar surface exerted by a two-layer shoe sole for three distinct phases of the gait cycle. The proposed method is based on Artificial Neural Networks and can be utilized for the determination of the comfort that is related to the sole construction. Input parameters to the proposed neural network are the material properties and the thicknesses of the sole layers (insole and outsole). A set of simulation experiments has been conducted using analytic finite elements analysis in order to compile the necessary dataset for the training and validation of the neural network. Extensive experiments have shown that the developed method is able to provide an accurate alternative (more than 96%) compared to the highly expensive, with respect to computational and human resources, approaches based on finite element analysis.

Introduction

During the last decades, one area that has attracted considerable attention by researchers in biomedical and sport-related applications is the analysis of foot plantar pressure distribution and its relation with mechanical comfort. Mechanical comfort is the interaction of the foot with footwear and the ground, mainly related to the upright stance and gait mechanics [START_REF] Kirtley | Clinical Gait -Analysis, Theory and Practice[END_REF]. Plantar mechanical comfort is concerned with interactions between footwear sole geometry and materials with the plantar side of the foot and the ground, for different environmental conditions and activities. On the other hand, dorsal mechanical comfort is limited to fitting and stability [START_REF] Fong | Cushioning and lateral stability functions of cloth sport shoes[END_REF]. A formal definition and a review of measurement methods of footwear plantar mechanical comfort can be found in [START_REF] Papagiannis | Footwear Plantar Mechanical Comfort: Physical Measures and Modern Approaches to their Approximation[END_REF].

In this paper, we focus on measuring the maximum foot plantar pressure, which is the pressure that acts between the foot and the support surface, in our case the shoe's sole. Information derived from such pressure measures is important for diagnosing lower limb problems, improving design for casual and professional footwear, determining sport biomechanics, estimating planar mechanical comfort, etc. [START_REF] Razak | Foot Plantar Pressure Measurement System: A Review[END_REF].

Traditionally, the systems which are used to measure plantar pressure vary in sensor configuration to meet different application requirements. Typically, the sensors' configuration is one of three types: pressure distribution platforms, imaging technologies with specialized image processing software and in-shoe systems. In designing plantar-pressure measurement-devices the key requirements are: spatial resolution, sampling frequency, accuracy and sensitivity [START_REF] Gefen | Pressure-sensing devices for assessment of soft tissue loading under bony prominences: Technological concepts and clinical utilization[END_REF].

Generally, platform systems can be used for both static and dynamic studies but in the most cases they are restricted to research laboratories. Furthermore, the main disadvantage of these systems is that the patient requires familiarization to ensure natural gait because it is important for the foot to contact the center of the sensing area for an accurate reading.

On the other hand, the in-shoe sensors are flexible and embedded in the shoe such that measurements reflect the interaction between the foot and the shoe. These systems are flexible and allow a wider variety of studies with different gait tasks, footwear designs, and terrains [START_REF] Macwilliams | Clinical Applications of Plantar Pressure Measurement in Pediatric Orthopedics[END_REF]. Their main disadvantage is that the sensors should be suitably secured to prevent slippage and ensure reliable results. A further limitation is that the spatial resolution of the data is low compared to platform systems due to the limited number of sensors.

Recently, new methods are moving towards numerical and simulation techniques in order to determine plantar pressure distribution. Finite Element Analysis (FEA) is one of the most popular techniques for analyzing complex structures by combining different materials, loading and boundary conditions. Accuracy of these methods depends on the quality of the geometric models, initial and boundary conditions, material properties and meshing density. FEA has been used for calculating stressstrain relationships on tissues due to the interaction of the foot with the sole and the floor [START_REF] Azariadis | Finite Element Analysis in Footwear Design, in the Science of Footwear[END_REF]. Despite the fact that FE analysis using detail biomodels provide high accuracy in estimating various parameters affecting foot planar mechanical comfort, these calculations are very time consuming and require intervention of experienced users.

In this study, an Artificial Neural Network (ANN) is introduced to estimate the maximum plantar pressure on the foot surface exerted by the sole structure for each one of the three main phases of gait cycle (i.e., heel-strike, mid-stance and toe-off). Although, more gait phases can be considered, the above three phases result to high maximum plantar pressures and therefore are of primary importance in biomechanical analysis. The input parameters of the proposed ANN are: (i) the material properties of the insoles, and (ii) the thickness of each insole layer. The output is the maximum plantar pressure.

ANNs are a family of statistical learning algorithms inspired by biological neural networks that have the inclination for storing experimental knowledge and making it available for applications. Usually, an ANN consists of simple processing units, called "neurons", which are linked to other units by connections of different weight. The neurons are typically arranged in a series of layers. The network receives one or more inputs and sums them. The output is generated by passing that sum through an activation function [START_REF] Hertz | Introduction to the theory of neural computation[END_REF].

ANNs have been introduced to biomechanics data-mining as an alternative approach to mapping and simulating the relationship between a set of input and output variables [START_REF] Carter | Minds and computers: An introduction to the philosophy of artificial intelligence[END_REF]. They have been applied successfully to various areas such as: (i) gait classification [START_REF] Kaczmarczyk | Gait classification in post-stroke patients using artificial neural networks[END_REF], where ANNs has been employed to classify people's movement. A typical task is the classification of healthy and pathological gait pattern on the basis of kinematic knee angle parameters. (ii) Biomechanical modelling [START_REF] Schöllhorn | Applications of artificial neural nets in clinical biomechanics[END_REF], where the sequence of input and output variables of ANNs follow common biomechanical ideas about movement control without having deterministic relationships of these variables on hand, like the force relationship. (iii) Estimation of gait variables and parameters [START_REF] Chau | A review of analytical techniques for gait data. Part 2: neural network and wavelet methods[END_REF], where the ANNs are used to estimate gait parameters about the patients' walking ability which are useful in many clinical applications (e.g., to diagnose impairments in balance control or to monitor the progress in rehabilitation) [START_REF] Kose | Bilateral step length estimation using a single inertial measurement unit attached to the pelvis[END_REF]. However, there is a limited number of works focused on the foot-sole system for the purposes of computing footwear comfort during gait.

A recent work related to the use of ANNs in footwear comfort can be found in [START_REF] Rupérez | Artificial neural networks for predicting dorsal pressures on the foot surface while walking[END_REF], where an ANN is developed to estimate the dorsal pressures of the foot surface while walking. To accomplish this task, a model based on multilayer perceptron is constructed [START_REF] Baum | On the capabilities of multilayer perceptrons[END_REF] due to its capacity to model the exerted pressure for most of the materials used for the shoe upper. The input of this ANN includes the properties of the shoe upper material and the positions during a whole step of 14 pressure sensors placed on the foot surface. In [START_REF] Twomey | A predictive model for slip resistance using artificial neural networks[END_REF], an ANN is used as a model to estimate the slip resistance. In [START_REF] Barton | Comparison of shoe insole materials by neural network analysis[END_REF], the authors compare the effect of two insole materials using neural network analysis. In [START_REF] Kirk | Using neural networks to understand relationships in the traction of studded footwear on sports surfaces[END_REF], an ANN is used to estimate the traction forces for any combination of stud variables within the limits of the training data. Although the above works are related to footwear design, none is focused on estimating the foot plantar pressure, which is of significant importance in many types of footwear.

The rest of the paper is outlined as follows. Section 2, describes the data-collection method for the training and validation purposes of our approach. Section 3, presents the proposed ANN, while Section 4 presents and discusses the results achieved by the introduced approach. Section 5 provides some conclusions and ideas for future work.

Data Collection

For the purposes of this work an extensive dataset of plantar pressure measurements is required in order to comply with the training and validation needs of the proposed ANN. This dataset is developed by running several analysis tests using different combinations of material properties and thicknesses with FEA software. The two thirds of the data are used for training while the rest one third is used for validation purposes.

2.1

The data-collection method

The data used for this study are provided by performing FE analysis using a detailed foot biomodel [START_REF] Koutkalaki | Towards a foot bio-model for performing finite element analysis for footwear design optimization using a Cloud infrastructure[END_REF]. Foot data are based on a set of CT scans taken on a foot of a healthy male subject with a resolution of 0.5mm. The solid models created by the reconstruction process are imported into the ANSYS commercial software. Using the macro-language of ANSYS, a parametric model of a flat sole is developed. The bone and soft-tissue structure as well as the sole are discretized using tetrahedral elements (SOLID285), as shown in Fig. 1. The model is able to handle sole structures consisted of one to three layers, with the thickness and material of each layer being the input parameters. Another parameter of the FE analysis is the gait position of the foot relative to the sole. Since the transfer of forces is done only at the regions of contact between the sole system and the floor, it is easier to assume that the sole system remains unchanged during walking and to rotate the foot according to kinematic data. In this way, three FE models are created to simulate the three major gait phases during walking, i.e., heel-strike, mid-stance and toe-off (Fig. 2). In this work, bones are assumed bonded together and to the soft tissue. Constant linear material properties are assumed for the bones (Young's modulus of 7.3 GPa) and the soft tissue (Young's modulus of 1.15 MPa). Contact elements are being used between the foot and the upper sole layer. The upper part of the foot is fixed and a step-wise displacement is applied at the lower sole surface. The two layers of the sole are assumed perfectly bonded. Young's modulus is a mechanical property of linear elastic solid materials that measures the force (per unit area) required to stretch (or compress) a material sample [START_REF] Vable | Mechanics of Materials, Online Book Second Edition[END_REF].

The results of the analyses are the applied force (reaction force) and the plantar pressure distribution. Typical plantar pressure results are shown in Fig. 3 for the midstance phase. Maximum plantar pressure is observed at the heel and metatarsal regions. Similar distributions are observed for all material and thickness combinations. For the case of the heel-strike phase, maximum plantar pressure is observed at the heel region and is much larger than in the case of the mid-stance phase. The analyses of the toe-off phase shows maximum plantar pressure at the metatarsal region.

The Proposed Artificial Neural Network

This section summarizes the basic steps that have been followed to model and train the proposed ANN using the aforementioned described dataset.

Overview

A Multi-Layer Perceptron (MLP) [START_REF] Dennis | Feature Selection Using a Multilayer Perceptron[END_REF] has been adopted to estimate the maximum plantar pressure on the foot surface. The MLP is the most widely used ANN due to its high capacity on relating an input space with an output space. Generally, the MLP is a feed forward artificial neural network model which is composed of successive layers which communicate through synaptic connections.

The structure of a multilayer network contains: (i) an input layer which is made of a number of perceptions equal to the number of data attributes, (ii) intermediate layers which are considered hidden and (iii) an output layer which includes one perceptron in the case of regression or more when it is a task of classification [START_REF] Haykin | Neural Networks and Learning Machines[END_REF].

The inputs of each neuron are multiplied by adaptive coefficients called synaptic weights, which represent the synaptic connectivity between neurons. The output of a neuron is a function (an activation function) of the linear combination between the inputs and the synaptic weights [START_REF] Haykin | Neural Networks and Learning Machines[END_REF]. Back-propagation has been used for the network training.

Experimental setup of ANN

The input parameters of the proposed neural model are Young's modulus and the thickness of the material of each sole layer. Thus, the overall number of input parameters is 4. The output is the maximum plantar pressure. Input and output parameters are normalized in the range [-1, 1]. This resulted to small training sizes and greater accuracy [START_REF] Sola | Importance of input data normalization for the application of neural networks to complex industrial problems[END_REF].

The proposed ANN is trained using the two thirds of the dataset collected as explained above. The remaining subset has been utilized for validation purposes and for determining the generalization capabilities of the developed model (crossvalidation technique) [START_REF] Haykin | Neural Networks and Learning Machines[END_REF]. The goal is to develop a model that works appropriately not only for the cases used to train the model but also for new cases that can involve new materials as long as, their Young's modulus is within the range of the current training dataset. Otherwise, new materials can be included without changing the ANN architecture as long as there is an appropriate dataset for training.

Furthermore, in this approach, we make use of incremental pruning [START_REF] Cassandra | Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes[END_REF]. This enables the ANN to autonomously select the optimal hidden layer structure based on its capacity to learn best. In this approach, the number of input and output layers is predetermined while a range of minimum to maximum numbers of hidden neurons and layers is provided. The algorithm incrementally increases the size of the neural network and retrains at each increment until it reaches the maximum limits. Then the best trained network is considered as the optimal network configuration.

An important element of the network structure is "the activation function" [START_REF] Karlik | Performance Analysis of Various Activation Functions in Generalized MLP Architectures of Neural Networks[END_REF]. In general, the activation function introduces a degree of nonlinearity that is valuable for most ANN applications. Due to the fact that the predicted output of our ANN is in the range [-1, 1] the hyperbolic tangent function is selected as an activation function for the hidden and output layers [START_REF] Gomes | Optimization of the weights and asymmetric activation function family of neural network for time series forecasting[END_REF].

A number of training algorithms has been tested including Back Propagation, Resilient Propagation and Levenberg Marquardt Training. Best training times were achieved with "Resilient back propagation" (RPROP). RPROP is based on the traditional back propagation method with just one difference: weight updating is done by evaluating the behavior of the error function. With RPROP, the value of the weight update is calculated by evaluating the partial derivative sign from one iteration to another, improving the learning process, eliminating some problems encountered in the back propagation algorithm and making the proposed method faster than the traditional one [START_REF] Souza | Comparison between back propagation and RPROP algorithms applied to fault classification in transmission lines[END_REF].

Results

Table 1, shows the Young's modulus of the sole materials used to train the proposed ANN. We have selected two of the most popular materials used in shoe industry for sole making. Using a varying Young's modulus it is possible to cover a large range of existing materials. The thickness of each layer takes a value in the interval [START_REF] Kirtley | Clinical Gait -Analysis, Theory and Practice[END_REF][START_REF] Rupérez | Artificial neural networks for predicting dorsal pressures on the foot surface while walking[END_REF] mm. To assess the effectiveness of the proposed approach, we have conducted experiments with data drawn independently from known distributions. For each of the three gait phases, there is a corresponding dataset produced using FE analysis as it is described in Section 2. Incremental pruning resulted to a structure with one hidden layer with 6 neurons. After training is completed, each ANN is evaluated by feeding it with the validation data.

For the evaluation of the proposed ANN we have used three different error metrics: (a) the mean error (ME), (b) the mean-absolute error (MAE), (c) the root mean square error (RMSE) and (d) correlation coefficient (r). ME is used as a measure of bias. Positive values mean that the predictor tends to yield maximum pressures lower than the actual ones while negative values stand for an over-biased predictor. Therefore, desired values for ME should be as close to zero as possible. On the other hand, since MAE uses absolute values, negative errors in the prediction do not compensate positive errors, and hence, MAE gives a fair idea of the accuracy of the predictor. In addition, the use of RMSE is often preferred to MAE as an accuracy measure. Finally, the correlation coefficient (r) between the desired plantar pressure and the predicted one is used as a measure of fit, since it gives the linear similarity between the two measurements. A value of r equal to 1 means that the desired plantar pressure and the predicted one contain the same information, whereas a value of r equal to zero means that no information is shared by the desired pressure and the predicted one. provide more than 96% accuracy in terms of maximum value deviation compared to the original FEA results. A more detailed analysis of the results obtained for each foot phase is shown in Table 2. All three accuracy indices show a very accurate evaluation of maximum plantar pressure (e.g., MAE ≤ 0.053; ME ≤ 0.041 and RMSE ≤ 0.069). In addition, the correlation coefficient is close to unit (e.g., r ≥ 0.953) confirming that the proposed approach is a reliable predictor of the maximum plantar pressure and can be used as an accurate alternative to the time consuming and tedious process of FE analysis. The computational time required for training the proposed ANN was approximately 20 minutes and it is considered as an offline stage of the proposed approach. The actual run time for the calculation of the maximum plantar pressure for each trained model is about 0.1 seconds. In contrast, the time needed for FEA to calculate the corresponding value is about 20 minutes using the same i5 CPU.

All the above results confirm that the proposed ANN is able to estimate the maximum plantar pressure in all three foot phases with high accuracy.

Conclusions

This paper has proposed the use of an estimator based on neural networks for use in the selection of shoe's sole materials. Given the properties and the thickness of the material of each sole layer, the maximum plantar pressure can be estimated with high accuracy for each one of the three gait phases. This reduces considerably the time and cost involved in the calculation of this comfort parameter compared to FE analysis approaches.

The research will be further extended to incorporate more input variables to the ANN model in order to address more parameters related to plantar mechanical comfort.

Fig. 1 .

 1 Fig. 1. The foot model and the two-layered sole.

Fig. 2 .

 2 Fig. 2. The three gait phases examined: (a) heel-strike, (b) mid-stance, and (c) toe-off.

Fig. 3 .

 3 Fig. 3. Typical distribution of plantar pressure.

Fig. 4 .

 4 Fig. 4. The actual and the estimated results of 40 random runs for the phase heel-strike.

Fig. 5 .

 5 Fig. 5. The actual and the estimated results of 40 random runs for the phase mid-stance.

Fig. 6 .

 6 Fig. 6. The actual and the estimated results of 40 random runs for the phase toe-off.

Figures 4 - 6 ,

 46 Figures 4 -6, show three sets of examples of 40 random runs for the heel-strike, mid-stance and toe-off phase, respectively. In all cases, the actual maximum plantar pressure is represented with red color and the estimated result is represented with blue color. For these runs, the thickness of EVA layer takes the discrete values { } 5, 6 and

Table 1 .

 1 The materials' Young's modulus

	Material	Young's Modulus (MPa)
	EVA	10-40
	PU double density	4-12

Table 2 .

 2 Error measurement

		MAE	ME	RMSE	r
	Heel-strike	0.051	0.037	0.063	0.963
	Mid-Stance	0.049	0.041	0.061	0.961
	Toe-off	0.053	0.028	0.069	0.953

Acknowledgments. This research has been co-financed by the European Union (European Social Fund -ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) -Research Funding Program "ARISTEIA".


Mohammed Taha

Elhariri Essamlali

Aicha Sekhari
email: aicha.sekhari@univ-lyon2.fr

Abdelaziz Bouras
email: abdelaziz.bouras@qu.edu.qa

PLM System Support for Collaborative Development of Wearable Meta-Products using SBCE

Keywords: Meta-Product, PLM, ALM, New Product Development, SBCE, Collaborative Design

Nowadays, in the era of Internet of Things, people tend to be more tied to connected products seeking to ease and improve quality of life and find new ways to make things such as daily tasks differently. In this context, the world of clothing has noticed a big advance that transformed the life of customers. Customers are no longer looking for a simple piece of garment, but more the services that they can benefit from. Wearable Meta-Products are intelligent hybrid products made of garments, sensor networks and applications, interacting with users and the environment capable of real time data processing and storage, with capabilities to extend functionalities by communicating with other things. The development of Meta-Products is complex and requires close collaboration of the multidisciplinary team. This work proposes the Set-Based Concurrent Engineering (SBCE) specification applied to Meta-Products supported by PLM system an innovation enabler.

Introduction

Nowadays, in the era of Internet of Things, people tend to be more tied to connected products seeking to ease and improve quality of life and find new ways to make things such as daily tasks differently. At the same time, the expansion and high availability of sensors and electronic components with new features covering increasingly users' requirements enable the development of smart products with new capabilities. This expansion makes the development of such products more complex than the traditional ones due to the difficulty to find rapidly the right match of components. Nevertheless, a plethora of smart products were released during these last years, with a high interest in healthcare, sport and games.

This paper addresses the intelligent wearable Meta-Products, main focus of the European FP7 project "EASY-IMP" (Collaborative Development of Intelligent Wearable Meta-Products in the Cloud). Meta-Products consist of products to which we add network of sensors that enable them to connect to the Cloud, allowing the user to capitalize on the collected data through specific applications. Meta-Products require more complex elements than generic products and need the involvement of teams with interdisciplinary skills (garment designers, sensor networks designers and application developers). We have previously established a high-level generic methodology of MP development to describe an overview of the phases, methods and tools in EASY-IMP project [START_REF] Essamlali | The methodology for collaborative development of intelligent wearable Meta-Products[END_REF].

This paper focuses on the details of Set-Based Concurrent Engineering (SBCE) which is practiced to tackle the design and testing of the alternatives. SBCE is applied for dealing with complexities of products and multi-disciplinary of involved stakeholders. It allows actors working in parallel to discover set of feasible MP designs and select the compatible set of feasible components to establish MP prototype. Customer's requirements, technical solutions, and manufacturability are considered as constraints for evaluating and selecting feasible alternatives. This paper provides the specification of SBCE from theoretical concept and principles to concrete (practical) specifications adapted to Wearable Meta-Products leading to a prototype implemented on top of a PLM system.

Next section explains the notion of "Wearable Meta-Product". A review of SBCE is given in section 3. Section 4 presents the SBCE specification for Meta-Products and implementation is specified in section 5. Finally section 6 concludes the paper.

2

What is a Wearable Meta-Product?

The product is shifting nowadays from a physical thing to a smart thing capable of sensing, processing, storing data and communicating with the environment. This tendency has led to a rapid growth, especially in the field of wearables technologies where the customer is no longer looking for a simple piece of textile but to have services in addition to that. These innovative products made companies to change their thinking to make a shift of trend from product-centric to service-centric and gave birth to a set of names to designate these products. Several names are available in the literature related to "Smart" [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF], "Intelligent" [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF], "Smart, Connected Products" [START_REF] Porter | How Smart, Connected Products Are Transforming Competition[END_REF], or "Meta-Wearables" [START_REF] Park | Chapter 1.1 -Wearables: Fundamentals, Advancements, and a Roadmap for the Future[END_REF]. However, these terms refer to the same range of products with sensing, computing, storing and communication features. The term "Meta-Product" was introduced first by S. C. Rubino et al. in 2011 as "web-enabled product-service networks" [START_REF] Córdoba Rubino | Meta Products -Meaningful design for our connected world[END_REF]. Meta-Product comprises physical and digital elements. The physical element consists of sensorial and actuating functions. The sensorial function can communicate with user or environment and serve input information to the digital element. The actuator function is initiated by the digital elements for notifying the user. The digital elements accomplish actions such as data computing, storage, and visualization [START_REF] Córdoba Rubino | Meta Products -Meaningful design for our connected world[END_REF].

In this sense, we define Wearable Meta-Products as intelligent hybrid products made of garments, sensor networks and applications, interacting with users and the environment capable of real time data processing and storage, with capabilities to extend functionalities by communicating with other things.

The Product Lifecycle Management (PLM) is used to manage the products during all the phases of the lifecycle "from cradle to grave" [START_REF] Stark | Product Lifecycle Management: 21st Century Paradigm for Product Realisation[END_REF]. As any product, the lifecycle of Meta-Products consists of three phases during which information must be tracked and knowledge capitalized:

• The Beginning-of-Life (BOL) phase comprises all the phases of development of the MP defined by T. Elhariri et al. [START_REF] Essamlali | The methodology for collaborative development of intelligent wearable Meta-Products[END_REF], from the concept definition to production and delivery to the customer. • The Middle-of-Life (MOL) phase mainly consists of the use of the MP by the customer, and eventually the maintenance, repair and overhaul (MRO). Also, the evolution and upgrade of the components of the MP (as instance: the change of a sensor by another one more accurate) or updates of related software or applications. • Finally, the End-of-Life (EOL) phase includes the re-manufacturing or disassembly of the MP into parts, and the reuse, refurbishing or recycling.

Set-Based Concurrent Engineering Review

The traditional Point-Based Concurrent Engineering (PBCE) is dealing with a single solution qualified as the best one [START_REF] Sobek | Toyota's principles of set-based concurrent engineering[END_REF]. The process of PBCE is iterating on one solution until reaching the expected result, otherwise another solution is selected (Fig. 1 -A). This practice generates a lot of waste due to exhaustive investigation of failing solutions that are spread apart. Unlike the PBCE, the Set-Based Concurrent Engineering (SBCE) is considering multiple alternatives simultaneously (Fig. 1 -B). The SBCE is characterized to develop set of alternative designs depending on an effective decision process which is significantly important. It focuses on the design convergence among set of design alternatives through using a variety of tools and techniques to eliminate weaker solutions and save time on a project development. The evaluations of alternative designs set gives a clear idea about each alternative solution and facilitate communication and selection of final design. Finally, alternative solutions are kept in the set until enough knowledge is gathered to eliminate them.

Fig. 1. Comparison between PBCE and SBCE [8]

The SBCE rely on three principles (Table 1) derived from an industrial company (Toyota) by Sobek et al. [START_REF] Sobek | Toyota's principles of set-based concurrent engineering[END_REF]. The principles constitute the framework for product development where all the involved stakeholders work concurrently to develop new products. However, these conceptual principles need to be adapted to company's specific product to practice effective SBCE.

Application of SBCE for Wearable MPs

To apply SBCE to the development of Meta-Product, we need to adapt its theoretical principles to the specificities of this new kind of smart product combining a physical part (garments and network of sensors) and a service part (applications and software). These specifications should ease the work of the multidisciplinary team aiming to develop the Meta-Products, by eliminating wastes, facilitating collaboration, maximizing value and delivering high quality Meta-Products that meet the customers' expectations.

The challenge at this level is to propose specifications based on the theoretical SBCE principles by proposing a strategy to manage a Meta-Product the project using the SBCE, by defining processes to pragmatic application and having in mind the PLM system support. By providing an effective solution, the development team will put more efforts on innovation at Meta-Product level.

The process to develop Meta-Products is presented as a sequence of activities targeting a high level of collaboration between the teams in charge of the development (Fig. 2).

Fig. 2. Meta-Product Development Process

Preliminary design

The Meta-Products consists mainly of assembled parts and components produced by various suppliers, thus it is recommended to apply the Design for Assembly (DFA) approach for the design. The preliminary design of the Meta-Product gives a first idea about the Meta-Product expected design and the main required parts. Based on the technical requirements extracted in the second phase of the MP Development methodology [START_REF] Essamlali | The methodology for collaborative development of intelligent wearable Meta-Products[END_REF], the designers are able to breakdown the Meta-Product into subsubsystems (i.e.: garment, sensors, computation unit…) and start the design. The design of mobile applications can also start at this level to identify what are the needs of the new Meta-product in terms of software. It can rely on such existing application by releasing an update or develop a new application with new features and capabilities to communicate, gather, and process the data to give accurate information to the user.

Technical solutions

In this second phase of the process, we need to identify the parts corresponding to the sub-systems identified previously. The parts database is the main data source of the available parts from the suppliers' catalogue. It should be kept updated and store all technical specifications of the parts. Starting from this database, we identify for each sub-system the parts that fit their needs. Based on the parts technical specifications and the knowledge captured during the previous MPs development, the stakeholder can use the compatibility matrix with the maximum of information about the compatibility and combination dependencies without imposing further constraints based on "guesses".

Fig. 3. Parts Compatibility Matrix

The parts compatibility matrix as shown in Figure 3, visually represent the relations between the all the Meta-Product candidate parts. For instance, the matrix shows that ECG1 is not compatible with GPS3, while it can be assembled with GPS1 and GPS2.

4.3

Technical alternatives

Once the compatibility between the identified parts is established, the Meta-Product alternatives can be generated and Investigated in parallel. Decisions and constraints should be delayed as much as possible till enough knowledge is gathered through testing, simulations, and integrations. The set of alternatives will be gradually narrowed by discarding the alternative combinations that are infeasible and in conflict.

Each Meta-Product alternative item can be linked to several objects and information:

• CAD design Design Engineering: This phase is assigned to the alternative when there's a need in designing the MP, changing, or detailing. The results of this phase are the CAD (Computer Aided Design) design and eBOM (Engineering Bill of Material) of the Meta-Product.

Manufacturing Engineering: This phase consists of the manufacturing feasibility analysis and the generation of the MBOM (Manufacturing Bill of Material) required for the physical prototyping of the MP. At this level the design of the Meta-Product should be evaluated by taking into account the manufacturing constraints. Starting from the eBOM that describes "What" is needed and using the Manufacturing Process Management (MPM), the manufacturing team can investigate the feasibility of the Meta-Product from a manufacturability perspective, plan for the manufacturing process and generate the MBOM that represents the "How" the MP will be produced and assembled [START_REF] Sly | Manufacturing Process Management (MPM), White Paper. Technology Trends in PLM[END_REF]. The manufacturing process is also investigated by the manufacturing team by defining a Bill of Operations (BOO) that contains all the steps to assemble the Meta-Product.

Virtual Testing: In this state the virtual prototype is created and tested before going deep in the design or manufacturing feasibility study of the Meta-Product.

It will be necessary to conduct for prepared testing activities, in terms of virtual prototype and Virtual Reality (VR) simulations to meet the production metrics and the level of customer satisfaction, when the Meta-Product design finishes.

In this Easy-Imp project, VR simulator is a stand-alone application with userfriendly interface that allows to select and position sensors, make simulations, obtain visualization of the results and make records (images and videos). Virtual reality simulations are used to evaluate the behavior of the designed Meta-Product configurations (interplay between garment, sensor network and Application) under different simulated real-life conditions following different test scenarios and helping to optimize sensor positioning. The test managers will take charge of the whole process of virtual testing and provide feedback. The results of the VR simulations (e.g. error characteristics for motion / activity capture) will be stored in graphical form or as videos for being reviewed by the MP development team.

Physical Testing:

In this state the physical prototype of the Meta-Product validated virtually is now physically assembled and tested.

At this level, the Meta-Product prototype is assembled using the parts from the mBOM, and following the BOO defined the manufacturing team. Real testing scenarios will be performed to confirm the efficiency of the final design configurations. The evaluation results will be analyzed and validated by respective experts (e.g. sensor / application experts) who can also help during the testing phase.

Validated:

This state means that the resulting MP has succeeded in the physical testing phase. The alternative is then validated.

Rejected: This state means that the alternative has failed in one of the steps before (Design engineering, Manufacturing engineering, Virtual testing, or Physical testing). The Project manager needs in this case either to re-assign the alternative to explore other solutions or to abandon it.

The change of states will be followed by the sending of notifications to project members to inform them about the progress of the alternative. Sharing new results and data about the alternative with work teams enables to get quick and useful feedback. While the alternatives are going through the defined lifecycle, the project manager is more and more able to make decisions to narrow the set of alternatives. The alternatives will be gradually narrowed based relevant information and additional constraints from the project stakeholders until reaching the best solution.

Final design

The best Meta-Product alternative is found; all the parts are clearly identified and the MP success through the testing phase proves that the results meet the customer requirements. Finally, the mobile application can be developed and tested with the real MP prototype. The Application Programming Interface (API) of the Meta-Product can be developed to offer to the mobile applications developers the opportunity to develop new applications. The knowledge from this experience should be capitalized and used for upcoming MP projects.

SBCE Prototype

To implement the SBCE prototype, we have decided to use PLM systems, which are largely considered as innovation enablers and best candidates to support product development methodologies. The development of this new kind of products is not supported by actual PLM systems [START_REF] Kiritsis | Closed-loop PLM for intelligent products in the era of the Internet of things[END_REF]. ARAS Innovator has been selected by the Easy-Imp project after the scoring of different PLM systems available in the Market, considering functional, technological, methodological, and commercial criteria. ARAS Innovator relies on SOA architecture with a web-based client, offers extensive functionalities, is fully customizable and exposes an API to ease integration with other systems. For this prototype, we have used the version 10 SP4 of ARAS Innovator with Microsoft SQL Server 2012 database.

Fig. 5. Meta-Product Project Data-Model

The process explained in the previous section will be implemented within ARAS Innovator and automated using the workflow feature. Before that, the standard PLM data-model needs to evolve to support the complex Meta-Product data structure and handle the Meta-Product project using SBCE. A Meta-Product data-model was established in order to have a common Meta-Product in all systems used during the product development. However, this data-model will not be discussed in this paper. Fig. 5 is the Meta-Product project data-model implemented. The data-model includes all the necessary items to handle the Meta-Product project using SBCE and technical alternatives with related prototyping, testing and manufacturing information.

Conclusion and Outlook

This paper provides the SBCE specifications related to the High Level Generic MP Development Methodology. This specification presents a transformation process towards Meta-Product development by defining the relying activities and the associated tools that implement the principles of SBCE. Therefore, this paper provides a novel approach to shift from SBCE theoretical principles towards a pragmatic (practical) application to Wearable Meta-Products and help the multidisciplinary team to deal with the complexity of Meta-Products, find the best design solution that fits customers' requirement and lower the time to market.

The automation and use of PLM system to implement the SBCE approach will contribute for a better understanding avoiding confusions and enable the stakeholders to focus on the level of innovation of Wearable Meta-Products.

The Product Lifecycle Management (PLM) is intended to manage the whole lifecycle of products. Meta-Products require a PLM that supports both the Hardware and Software. Since actual PLM systems are not managing software, our future

Fig. 4 .

 4 Fig. 4. MP Alternatives Lifecycle Description of the technical alternatives lifecycle states: New: This first state assigned to an alternative when created. Assigned: In this state the alternative is assigned to a team (Design, Manufacturing or testing…). The work on the alternative has not yet begun.

Fig. 6 . 7 .

 67 Fig. 6. Meta-Product Project in PLM Fig. 7. Meta-Product Alternative in PLM Fig. 6 is a screenshot of the Meta-Product project in the PLM, the grid lists the alternatives linked to this project with information such as the status, progress, current team in charge and related KPIs. Fig. 7 depicts one of the validated alternatives with all the associated information and tested prototypes.

Table 1 .

 1 Principles of Set-Based Concurrent Engineering[START_REF] Sobek | Toyota's principles of set-based concurrent engineering[END_REF]

	Principles of Set-Based Concurrent Engineering
	1. Map the design space:
	• Define feasible regions
	• Explore trade-offs by designing multiple alternatives
	• Communicate sets of possibilities
	2. Integrate by intersection:
	• Look for intersections of feasible sets
	• Impose minimum constraint
	• Seek conceptual robustness
	3. Establish feasibility before commitment:
	• Narrow sets gradually while increasing detail
	• Stay within sets once committed
	• Control by managing uncertainty at process gates

Acknowledgments. The authors would like to thank the European Commission for the financial support of the EASY-IMP project (G.A: 609078) and the project partners for their collaboration.


Xi Yu

Antoine Nongaillard
email: antoine.nongaillard@univ-lille1.fr

Aicha Sekhari
email: aicha.sekhari@univ-lyon2.fr

Abdelaziz Bouras
email: abdelaziz.bouras@qu.edu.qa

An environmental burden shifting approach to re-evaluate the environmental impacts of products

Keywords: Product Life Cycle, Printed Circuit Board, Life Cycle Assessment, Life Cycle Impact Assessment, Material Flow Analysis, Life Cycle Inventory, Pareto rule

Life cycle assessment (LCA) can help enterprises evaluate their product's environmental impacts through the entire product life cycle (PLC). On the basis of the evaluation result, phases which need to take activities to reduce the environmental impacts can be found out. However, the activities taken in a phase may influence the environmental impacts in other phases, so the effect of the activities should be re-evaluated. Under the pressure from the market, enterprises need to assess the effect of these activities quickly. Nevertheless, re-evaluate by using conventional LCA is a time-consuming work. This paper proposes a novel approach to re-evaluate the environmental impacts of product based on LCA and Pareto rule which can reduce the time of assessment. A printed circuit board (PCB) case study is conducted using this approach. The outcome shows that the new approach can re-evaluate the environmental impacts more efficiently without influence the validity.

Introduction

As any improvement activity taken in a phase may make the environmental impact reduce in a phase but increase in other phases. This so called environmental burden shifting phenomenon forces analysts to collect all the related data in every time when assess the activities' environmental effects. Then the re-evaluation is similar as evaluate again and still time-consuming. In order to make the re-evaluation more efficient and without influence the validity of the result, this paper proposes a novel approach based on LCA method to re-evaluate environmental impacts after some improvement activities taken. The Pareto rule is used to make the re-evaluation only focus on the materials which have most significant contribution to the special environmental impact. A graph-based model is proposed to make the environmental burden shifting analysis more intuitive and comprehensive. On the basis of the data collected at the first time before activities taken and the data after activities taken, the variation of the concerned materials between two times can be calculated. By checking databases or measure on site, the emission factors of each material in each phase can be acquired. Multiple the value of variations with the value of emission factors and then add the original environmental emissions, the new environmental emissions of these materials can be calculated out.

This paper is organized as follows: Section (2) describes the related works. Section (3) focuses on the main proposed model and approach. A case study is provided in Section (4) to illustrate the approach. Finally, some conclusions are presented. LCA is more accepted in industry because it can assess product's EIs associated with all the phases of the product's life from cradle-to-grave [START_REF] Rebitzer | Life cycle assessment: Part 1: Framework, goal and scope definition, inventory analysis, and applications[END_REF]). The importance of different life cycle phases and environmental releases can be evaluated by LCA (eHertwich and Hammitt 2001). The ISO 14040 standard defines the main stages of LCA as shown in Figure 1 (ISO14040 2006).

Literature review

Figure 1 Stages of an LCA

MFA is an excellent tool to analyze material flows and stocks, it is can also used to evaluate the results of analysis and control material flows in view of certain goals such as sustainable development [START_REF] Hendriks | Material flow analysis: A tool to support environmental policy decision making. Case-studies on the city of Vienna and the Swiss lowlands[END_REF]. It can be divided into two basic types of material flow-related analyses as shown in Table 1 (Bringezu and Moriguchi 2002). The type I can be called as substance flow analysis (SFA), the result of it can be applied to control the flow of hazardous substances. The procedure of MFA usually comprises four steps: goal and systems definition, process chain analysis, accounting and balancing, modeling and evaluation. The systems definition illustrate the formulation of the target questions, the scope and systems boundary. The process chain analysis defines the processes for which the inputs and outputs are to be determined quantitatively by accounting and balancing. The fundamental principle of mass conservation is used in this step. Modeling may be applied in the basic form of "bookkeeping". The evaluation of results is related to the primary interest and basic assumptions. The LCA and MFA can both support enterprises reduce EIs of products from the review of existing works. LCA can find out the main issues through the entire PLC, while it is not suitable for a quick re-evaluation because it is a time-consuming method. MFA can find out the main material flows or stocks which can bring severe EIs, yet it cannot illustrate the EIs of product directly.

Graph-based Model considering environmental burden shifting

The traditional LCA can provide useful information either to identify which phases of the product lifecycle have significant environmental impacts or to compare the environmental performance of two alternatives. Both of these objectives require the collection of massive amount of data from different enterprises involved in the whole product life cycle. [START_REF] Loijos | Making Primary Data Collection In LCA Faster And More Accurate[END_REF] points out that at least 70 hours are needed to collect primary data in traditional LCA. Even for some simplified LCA methods, 1-20 person-days of work are required [START_REF] Hochschorner | Evaluation of two simplified life cycle assessment methods[END_REF]. The time-consuming data collection is a big issue for enterprises to re-evaluate the environmental performance of a product. Any change at a specific phase may influence the environmental impacts of other lifecycle phases due to the burden shifting phenomenon. Moreover, a totally new data collection is required to re-evaluate the environmental performance of the updated product lifecycle.

Some researchers claim to consider the environmental burden shifting problem (Suyang and Liu 2010; [START_REF] Yang | Replacing gasoline with corn ethanol results in significant environmental problem-shifting[END_REF][START_REF] Kanth | Insight into quantitative environmental emission analysis of printed circuit board[END_REF]). However, this phenomenon has been avoided thanks to the re-collection of all environmental data after each change. In this section, a graph-based model is proposed to truly consider the burden shifting phenomenon, which leads to a new evaluation model of the environmental performance for product lifecycle. An activity impacts directly the amount of materials/energies used in a phase and impacts indirectly other lifecycle phases. On the basis of the variation of the mass of materials or energy used in a phase after an activity occurred, the new environmental release can be computed thanks to a simple reverse engineering. The reverse engineering using in this paper refers to the environmental release of a material or energy can be calculated by multiple the mass of the material or energy with the emission factor [START_REF] Eilam | Reversing: secrets of reverse engineering[END_REF]EPA 1995). Finally, the environmental impacts of each phase can be calculated and aggregated to determine the environmental performance of the whole product lifecycle.

Before describing the new model, some definitions are required. An "Activity" corresponds to the changes made by the user in order to reduce the environmental impacts. Activity can be described in terms of 5W1H (WHEN, WHERE, WHO, WHY, WHAT, and HOW) [START_REF] Matsuyama | Proposal of a Support Method for Identifying Design Requirements on Life Cycle Planning[END_REF], as shown in Table 2. For example, the upgrading process for a personal computer is defined in Table 3 [START_REF] Suesada | Development of description support system for life cycle scenario[END_REF]. An "Impact" is the relationship between different phases due to an activity at a specific lifecycle phase. Impacts between lifecycle phases are illustrated by dotted lines in Figure 2. The variations of energy and materials are used to quantify the impact. The proposed model considers direct impacts between two lifecycle phases as well as indirect impacts, which correspond to the secondary impacts generated by the direct consequences of an activity. An activity taken in a phase can make a material/energy change in different phases. Therefore, when calculate the variation of a material/energy in the specific phase, all the activities which have influence in the specific phase must be taken into account. The total variation of a material/energy in the specific phase corresponds to the accumulation of each variation due to different activities. It can be expressed by equations ( 2) and (3):

Table 2 Definition of 5W1H of a lifecycle activity

1 2 ... k k k k n jM jM jM jM δ δ δ δ = + + (2) 1 2
... n jE jE jE jE δ δ δ δ = + + (3)
k jM δ / jE δ denotes the total variation of materialk/energy in the specific phasej.

From the existing databases or statistic data, each material/energy has its own environmental emission factor. Enterprises can estimate the environmental emission factor of each material/energy used in their own phase based on their process and statistic data. For example, 2.56kg carbon dioxide is released by combustion 1kg gasoline [START_REF] International | LCA data bases[END_REF]). The value of the carbon dioxide emission is called the environmental emission factor of 1kg gasoline. Then the variation environmental emission can be calculated by multiple the variation of material/energy used in the specific phase with the corresponding environmental emission factor as formula(4). Since the original environmental emission of materialk and energy in the influenced phasej is already existing, the new environmental emission of materialk and energy can then be acquired by adding the original value with the variation value as shown in equations [START_REF] Gertsakis | A guide to EcoReDesign: improving the environmental performance of manufactured products[END_REF]. Then the new environmental emission of the environmental impact category in the influenced phasej can be calculated by summing all materials and energy which belong to the special environmental impact category.

The ' i jC em denotes the environmental emission of the special environmental impact categoryi in the influenced phasej after activities taken in other phases.

After the environmental emissions of each environmental impact category are calculated, users can choose different LCIA (Life Cycle Impact Assessment) methods to calculate the environmental impacts of the influenced phase.

In order to improve the efficiency of the environmental performance re-evaluation process, the time required to collect all data must be reduced, without affecting the validity of the results. The "Pareto principle" (also known as the 80-20 rule) provides a theoretical guide to achieve this objective. This rule states that only 20% of the time normally required to collect all data is sufficient to get 80% of these data [START_REF] Halog | Advancing integrated systems modelling framework for life cycle sustainability assessment[END_REF]. This Pareto principle can be applied to environmental impact assessment problems. For example, even if a user is interested in the global warming issue of a product, he does not need to collect all the environmental data related with this issue. Indeed, since the global warming is mainly caused by the greenhouse gases (GHG) such as carbon dioxide (CO2), methane (CH4), and Nitrous Oxide (N2O), a user only needs to collect the data of materials and processes which have significant contributions to these GHG emissions. In the practice, the Pareto principle can be introduced in our model during environmental burden shifting analysis.

Conclusion:

Enterprises take some environmental friendly activities in their own life cycle phase to improve the environmental performance of their product. However, through the entire product life cycle, any activities taken in a phase may bring some influences in other phases and make the environmental burden shift from one phase to other phases. Therefore, an approach is needed to evaluate the environmental effects of these activities. In the traditional LCA, the environmental burden shifting issues are dealt by re-collect all the related data every time in each phase. This make the traditional LCA becomes a time-consuming method. The approach proposed in this paper uses the Pareto rule to deal with the data collection issues. The time of data collection is decreased by only focuses on the most significant materials/energies which contribute the special environmental impact category. In order to make the environmental burden shifting analysis more intuitive and comprehensive, a graph-based model is proposed to help users analyze the interaction between different phases. On the basis of the existing data of input materials/energies before and after activities taken in some phases, the variation of these materials can be calculated. By multiple the environmental emission factors of these materials/energies in each phase, the new environmental emission in each phase is acquired. In the future, a case study will be conducted to test this new approach.

  Many methods have been proposed to evaluate the environmental impacts of products. Such as the Ten golden Rules (Luttropp and Lagerstedt 2006), MET-matrix (Materials, Energy, Toxic emissions) (Knight and Jenkins 2009) in terms of qualitative; the Environmentally Responsible Product/Process assessment matrix (ERPA)(Graedel et al. 1996), Environmental Product Life Cycle Matrix (EPLC)(Gertsakis et al. 1997), Product Investigation, Learning and Optimization Tool (PILOT)(Wimmer et al. 2004), in terms of semi-qualitative; The LCA (ISO14040 2006), material flow analysis (MFA) in terms of quantitative. As the qualitative and semi-qualitative methods cannot provide precise result of environmental impacts of product to satisfy the enterprises' requirements, this research focuses on the quantitative study and the literatures related with LCA.

  lifecycle activity WHERE Location of an lifecycle activity WHO Stakeholder, who treats a product or its components WHY The application of the activity WHAT A product or its components treated in an lifecycle activity HOW Treatment means in an lifecycle activity

Figure 2

 2 Figure 2 The characteristic of interaction among different lifecycle phases Figure 3 illustrates the graph-based model, based on the scenario in Figure 2. Each node in the graph represents a lifecycle phase. Each phase is characterized by mass of materials and energy. n ij δ represents the variation of mass of materials and energy in phasej due to the activityn occurred in phasei. The direct impacts and the indirect impacts are respectively depicted by doted arrows and dashed arrows. Each impact δ has to be defined specifically between each pair of phases.

Figure 3

 3 Figure 3 Graph-based model considering the environmental burden shifting caused by one activity In practice, many activities may occur at same time, each activity may have impacts on a specific phase. Then the model can be depicted as Figure 4. As shown in Figure 4, the final variation of mass of materials and energy in phasej is the sum of the variation of mass of materials and energy caused by different activities occurred in different phases.

Figure 4 δM

 4 Figure 4 Graph-based model considering the environmental burden shifting caused by multi activitiesThe mass of materials or amount of energy used in different phases can be directly acquired by companies. The variation of materialk or energy used in the influenced phasej caused by activityn can be calculated thanks to equations (1): '

  variation of environmental emission of materialk and energy in the specific phase respectively. The k jM factor and jE factor denotes the environmental emission factor of the materialk and energy in the specific phasej respectively.

  represent the original environmental emission of materialk and energy consumed in the influenced phasej respectively.' environmental emission of the materialk and energy consumed in the influenced phasej after activities are taken in several phases respectively.

Table 1

 1 Types of material flow-related analysis

	Type of analysis	a	b	I	c
	Objects of	Specific environmental problems related to certain impacts per unit flow of:
	primary	Substances	materials		products
	interest	e.g. Cd, Cl, Pb, Zn,	e.g. wooden products, energy	e.g. diapers, batteries,
		Hg, N, P, C, CO2,	carriers, excavation, biomass,	cars
		CFC	plastics
			within certain firms, sectors, regions
				II
		a	b		c
		Problems of environmental concern related to the throughput of:
		firms	sectors		regions
		e.g. single plants,	e.g. production sectors,	e.g. total or main
		medium and large	chemical industry,	throughput, mass
		companies	construction		flow balance, total
					material requirement
		associated with substances, materials, products

Table 3

 3 Example of description of lifecycle activity

	5W	Content	1H	Content
	WHEN	Upgrading
	WHERE	Retail shop
	WHO WHY	Retailer To upgrade PCBs	HOW
	WHAT	Personal computer

Acknowledgements

This project has been funded with support from the European Commission Project (EMA2-2010-2359) and Hubert Curien Partnership with Cai Yuanpei China program 2012-2014. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.