Eric Benoit

Eric Chotin

Laurent Foulloy

PROCESSORLESS SMART SENSORS WITH DISTRIBUTED INTELLIGENCE

Keywords: intelligent sensor, distributed intelligence, field bus

In the proposed approach, smart sensors own the definition of the software functionalities but are no longer able to execute them locally. Thanks to the network, these software functionalities are sent to a smart sensor, to a smart actuator or to a common resource that has computation facilities. Due to the wide range of possible processing units, the exchange of software functionalities takes place at the source level. A dedicated language, has been especially developed to design smart sensor integrating fuzzy functionalities. In order to perform the remote execution of the code, each unit with computing capabilities owns a compiler. Processorless smart sensors send the source code to a computing unit which will execute the code and send back the results. This paper presents the concept of intelligent cells. Then processorless smart cells are introduced. The main topics of the dedicated language are presented. A hardware solution for the implementation is detailed. An illustrative example is provided.

INTRODUCTION

Since the eighties, the concept of smart cells communicating over a field bus network in order to drive an industrial process has been developed [START_REF] Siebert | Interworking of Fielddevices[END_REF][2][3] [START_REF] Navaro | Intelligent Distributed Control : A System Architecture[END_REF]. It is commonly admitted that smart cells (i.e. smart sensors or smart actuators) include functionalities such as measurement, communication, configuration and validation. Generally several functions are performed by hardware like basic measurement or network interfacing while the others are performed by software like signal processing or diagnostic. Usually, smart cells include a processing unit whose computing ability allows them to perform all software functions. However, including a processor into each smart cell is sometimes expensive especially when the cost of the processor is high compared to the cost of the sensing unit.

Our approach is to consider that an intelligent cell does not need to own the ability to compute its own functionalities if another cell can perform them instead of it over the network. In fact, an intelligent cell only needs to owns its functionality definitions assuming that another one can perform them. In order to implement this concept, we propose to code the intelligent functionalities of each cell into a source code written in a dedicated language called PLICAS. This code can contain configuration parameters and definition processes used to implement intelligence. This source code can then be transmitted to another cell to be executed.

The aim of this approach is to increase the interchangeability i.e. the possibility to replace a cell by another one, and the interoperability i.e. the possibility for cells to communicate with other cells [START_REF] Staroswiecki | Models and Languages for the Interoperability of Smart Instruments[END_REF].

INTELLIGENT CELLS

Functional description

An intelligent cell is a material unit which owns functionalities in addition to sensor ones or actuator ones. Common functionalities can be communication over a network, validation, diagnostic, measurement, signal processing, control.

In order to implement such cells, we will consider two distinct levels in the functional description of a cell, each one being implemented separately: The first level is the general behavioural description. It consists of a state machine describing the different functionalities to be executed by the cell according the occurring events. This state machine is implemented locally. The second level corresponds to the functionalities which are available in the cell, like signal processing, diagnosis. These functionalities are performed by software and are implemented as source codes containing configuration parameters and computing routines. This code is stored in a local memory and requires a processing unit to be executed. This processing unit can be local or somewhere else over the network. In a general way, the set of offered functionalities can be considered as a set of one or more services among the following :

Communication This configuration is relevant to all parameters which depend on the general use of the cell. The functional configuration is the in line configuration used to adapt the cell to a specified application, or to adapt it to a modification of the application context. Measurement, including basic sensing, signal processing, correction. The method chosen to implement these functionalities is to use a dedicated language. This language called PLICAS (Prototype of Language for linguistic Actuators and Sensors) is a language initially dedicated to fuzzy control applications [START_REF] Foulloy | Fuzzy components for fuzzy control[END_REF][7].

Processorless cell

Generally, the concept of intelligence is associated with the possession of a dedicated processing unit. We propose in our approach, to share the processing units over the network, and then to reformulate the intelligence concept as the ability for a cell to use a processing unit that could be its own or a delocalized one. Thus in the case of a processorless cell, the intelligence consists of a behavioural description of the cell that allows the use of the network resources to carry out the various cell functionalities. The PLICAS code will be sent by the state machine level to a computing resource over the network, and the execution results will be sent back.

The interest of storing the functionality codes locally in the processorless cell, is to allow easy changes in the process assignment on computing cells. This is in opposition with a scheme where the code would be resident on a pre-defined computing cell.

The structural description of a cell is mapped from the functional description: the general behaviour of a processorless cell is implemented using a FPGA carrying out the management system of the cell. The choice of a FPGA illustrates the weak processing resource such a cell needs. The functionalities of the cell are implemented as PLICAS code stored in a read only memory. These codes are transmitted through the network by the FPGA state machine, using a network interface implementing the data-link and the physical layers of the fieldbus protocol. When the cell used must be interfaced with the physical world, it also need transducers, conditioners and analog-to-numeric converters or numeric-to-analog converters.

Computing cell

The role of a computing cell is to carry out its own treatments as well as the treatments submitted by other cells. To do this, the functional description of the cell is similar to that of a processorless cell, except that the general behavioural description is implemented as a multitasking system, in order to execute simultaneously the local and the delocalized tasks.

These cells include computing resources like microprocessor or dedicated processors like DSP (Digital Signal Processor) or FFT circuits (Fast Fourier Transform). In this case, the cell can compute a piece or all of its functionalities. It can also use its computing resource to compute the functionalities of another cell. In order to perform functionalities coded in PLICAS, each computing cell contains an operating system which allows the management of PLICAS code, the execution of the code and the communication with other cells. This operating system is based on a usual multitasking operating system, a compiler of the PLICAS language and an executive manager. network. The new task compiles the code by creating a structure in memory which includes the sequence of all its actions. Then the task executes the actions by running the executive manager.

After the compilation phase, the executive manager performs actions coded in the structure. A PLICAS source code is a set of blocks of actions. It needs to own two special blocks called an "initialization block" and a "supervisor block". The initialization block is executed once at the beginning. After the initialization block is executed, the supervisor block is executed periodically. Each of these blocks can own network instructions to get or send values over the network. This is especially the case when the code is issued from a processorless cell which is waiting for a result. After each execution of the supervisor block a variable called "stop" is tested. If this variable is set then the task ends.

The language

The PLICAS language owns basic functionalities of sequential programming. In order to minimize the size of the compiler code, the language uses only global variables and does not allow new function definitions. It's a language created to manage the sequencement of predefined functions. It includes basic arithmetic and logical functions needed to perform this sequencement. Specialized functions like FFT or rule based controller can be added to the language during a technological configuration. Added functions are then used as primitive functions of the language. Each networked variable is managed by a number. A program can then get a variable on the network or diffuse another one. The function "get(var_number)" can perform two actions depending on the localization of the value of the variable. If this value is already present, the function simply returns it. If the value is not present, the function sends a request for this variable over the network.

APPLICATION

This concept is applied on a VAN fieldbus (Vehicle Area Network). A computing unit based on a PC104 is connected to this network. On this unit the multitask operating system RTC is implemented in order to compute simultaneously 16 source codes.

A processorless cell is implemented as a colour sensor, owning a diagnostic functionality. In normal mode, the sensor performs the colour measurement based on three photometric transducers which return three basic measurements X,Y and Z. In most applications, colour measurement is expressed in the Lab coordinates instead of the XYZ ones. In this way, usual colour sensors are composed with a perceptive head measuring X,Y,Z and a deported calculator performing the mapping between these two colorimetric coordinates : In our application, the perceptive head is included in a processorless cell. During initialization, the cell performs the Xn,Yn,Zn value measurements, which are characteristic values of the light source integrated in the perceptive head. Then these values are sent to the computing cell together with the PLICAS code of the Lab transformation.

Once initialization is done, the processorless cell enters into normal mode consisting of cyclic measurement of the X,Y,Z values, and their transmission to the computing cell.

The diagnostic functionality of the processorless cell is the following : when the characteristic values of the light source change, the new values of Xn,Yn,Zn are sent to the computing cell. This is done by the cyclic measurement of the most characteristic value, Yn, and its comparison with the previous memorized value.

The state machine implementing the general behavioural description of the processorless cell is very simple, even with the network management. This is due to the VAN network functionalities which allow a very convenient mechanism for the response to a network request. This mechanism is call "reply request with immediate reply" or "in frame reply". It consists of including the response in the request frame, and is completely managed by the data link layer network interface component (29C461A by MHS). The cyclic transmission of the X,Y,Z values is done with this scheme. Thus it only requires storing the responding value together with the frame request identifier in the RAM of the network interface component.

The general state machine of the processorless cell is shown in figure 4. conclude that one or more of its transducers is out of order. In another example, the sensor can calculate a fuzzy description of the colour. The way to add these functionalities is to implement the PLICAS source code into the memory of the processorless module, making sure that another cell can perform these functionalities.

L 116f Y Y n ------     16 - = a 500 f X X n ------     f Y Y n ------     -     = b 200 f Y Y n ------     f Z Z n ------     -     =

CONCLUSION

The proposed approach shows the advantage of separating the concept of intelligent process and processing resource. Actually, the confusion between these two concepts implies making considerable changes on an intelligent cell when a new functionality is added on to it. The processorless approach allows the increase in cell functionnalities without any hardware modification. A second advantage of this approach is to improve the interchangeabilty of cells.

Figure 1 .

 1 Figure 1. Structural description of intelligent cells

Figure 2 .

 2 Figure 2. Example of two simultaneous tasks created to manage different PLICAS codes

Figure 3 .

 3 Figure 3. Example of a PLICAS code

Figure 4 .

 4 Figure 4. State machine of the processorless sensor

  diag : diagnosis (Yn != Yn-memorized) va : X,Y,Z values availables

Proc. of the 14th IMEKO World Congress, vol. V, Tampere, Finland, June 1997, pp. 60--65.


Léo Le

Pallec Marand

Yo Sakata

Daisuke Hirotani

Katsumi Morikawa

Katsuhiko Takahashi
email: takahashi@hiroshima-u.ac.jp

An Adaptive Kanban and Production Capacity Control Mechanism

Keywords: Adaptive production capacity, Adaptive kanban system, Markov analysis

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Just-In-Time ordering systems, such as the Kanban and Conwip mechanisms, are used for reducing the costs associated with inventories and work-in-process (WIP) while maintaining a high level of service for customers. These systems authorize the release of a production order only when a demand is received from the customer and buffers are allocated to each inventory point to absorb changes in demand or production. However, this is for absorbing stable changes only, if unstable changes appear the allocated buffers cannot absorb them. Therefore pull systems are most efficient in environments with stable demand and processing times. The traditional approach is to optimize the number of cards used to control the release of production orders over a period based on demand forecasts. However, using forecasts contradicts the Just-In-Time principles and setting the number of cards over a period prevents timely readjustments if environmental conditions change quickly. Therefore, various mechanisms have been proposed to dynamically adjust WIP levels in order to increase the performance of pull systems in environments facing demand variability [START_REF] Lage | Variations of the Kanban System: Literature Review and Classification[END_REF].

The dynamic card controlling mechanism for CONWIP systems [START_REF] Framinan | Dynamic Card Controlling in a Conwip System[END_REF] adds or subtracts production cards when the manufacturing system's throughput (in make-toorder environments) or service level (in make-to-stock environments) is less or more than a production target. The reactive kanban system [START_REF] Takahashi | Reactive JIT Ordering System for Changes in the Mean and Variance of Demand[END_REF] dynamically adjusts the number of kanbans, using control charts for monitoring demand inter-arrival times and detecting unstable changes in the demand distribution.

The adaptive kanban system (AKS) was proposed [START_REF] Tardif | An Adaptive Approach to Controlling Kanban Systems[END_REF] for a single stage manufacturing process. It does not monitor inter-arrival or output times but finished goods and backorders levels. Based on the monitored levels, additional kanban cards are released and captured in addition to initial kanban cards. For setting the AKS, Genetic Algorithm-and Simulated Annealing-based heuristics were compared [START_REF] Shahabudeen | Algorithm for the Design of Single-stage Adaptive Kanban System[END_REF]. Also, the AKS was adapted for a two-stage production line, and the benefits of simultaneously or individually adjusting the number of kanbans at each stage were compared [START_REF] Takahashi | Adaptive Kanban Control Systems for Two-stage Production Lines[END_REF].

These systems dynamically adjust the number of production cards, but the amount of WIP is not the only parameter impacting service levels and operating costs. The amount of production capacity also impacts service levels (by conditioning the manufacturing system's throughput) and operating costs (having more capacity than needed is a waste). The mechanism presented here, based on the AKS, dynamically adjusts not only the amount of WIP but also the throughput by changing the amount of active resources. It is expected to minimize inventories, WIP and operating costs while maintaining a high level of service and be more resistant to changes in the demand.

Section 2 presents our Adaptive Kanban and Production Capacity Control system and how to evaluate its performance. We study its performance compared to the Adaptive Kanban system in Section 3 and conclude with brief comments in Section 4.

Adaptive Kanban and Capacity Control System

For simplicity we restrain our attention to the case of a single-stage, single product manufacturing process MP with S identical parallel servers. We assume demand to follow a Poisson process of rate λ D and an exponential processing time at each server with rate  P . These assumptions are quite restrictive but will allow us to use Markovian analysis for estimating the performance of the system and utilized in the literature [START_REF] Framinan | Dynamic Card Controlling in a Conwip System[END_REF][START_REF] Shahabudeen | Algorithm for the Design of Single-stage Adaptive Kanban System[END_REF][START_REF] Takahashi | Adaptive Kanban Control Systems for Two-stage Production Lines[END_REF][START_REF] Tardif | An Adaptive Approach to Controlling Kanban Systems[END_REF]. We further assume there is no shortage of raw parts, which can be considered true for sufficiently strict service level requirements on the preceding production stage. With the assumption of exponential processing times, parts exit MP with S identical parallel servers according to a state-dependent Markovian process of rate μ P (n), where n is the number of orders in MP;

if n smaller than S and if n greater than S. For the single-stage, single product manufacturing process, we propose an adaptive kanban and production capacity control system (AKCS) based on the AKS. The complete mechanism of AKCS is described in Fig. 1. The decision variables are the number of kanban cards K in original kanban system, the number of additional kanbans E, a release threshold R and a capture threshold C in AKS and the number of flexible servers M. By setting the decision variables, the total cost Z minimized. The total cost Z consists of the cost for holding inventories and Work-In-Process I, the backorders cost B, the cost for switching flexible servers F, and the cost for operating the manufacturing process O, and the unit costs are defined as h, b, f, and d.

Fig. 1. Adaptive Kanban and Production Capacity Control mechanism

As in the kanban system, all of K kanbans are attached to finished parts stocked in queue P one by one. When a finished part is delivered to the customer, the attached kanban is removed and sent to MP as a production order. If P is empty, new demands are stocked in queue Q as backorders until a part is processed through MP and delivered to the customer.

Furthermore, as in AKS, in addition to K initial kanbans, up to E additional cards can be released into the manufacturing system or captured whenever inventories reach a release threshold R or a capture threshold C. When a demand arrives and inventories are equal to R or less, not only an initial kanban but also an additional kanban are released in the manufacturing system as production orders if it is available. If inventories are greater than C when a demand arrives and some additional cards have already been released, the kanban attached to the finished good delivered to the customer is captured.

In our Adaptive Kanban and Capacity Control system (AKCS), not only the number of kanbans but also the production capacity of the manufacturing process is adjusted, by increasing or decreasing the number of active servers in MP. Then, in addition to the initial number of S fixed servers, M flexible servers can be switched on and off to adjust the capacity of M. In the AKCS, finished parts level and on-off of flexible server will be controlled, and costs not only for inventory holding and backorders but also for operating flexible servers will be suppressed.

Increasing capacity is synchronized with demand arrivals while reducing capacity is synchronized with finished parts exits from MP. The flexible servers are switched on when inventories reach R but all additional kanbans have already been released. Flexible servers are switched off when inventories outnumber C but all additional kanbans have already been captured or when the number of busy servers becomes too small (equal to s). For the system to be able to return to its initial state K must be greater than C, and C equal or greater than R. For suppressing costs for inventory holding and backorders, adapting flexible servers is more effective than adapting additional kanbans, however, it leads to an increase in the cost for switching flexible servers. Then, adapting flexible servers only after adapting additional kanbans is considered for suppressing the cost. This system is equivalent to the AKS if M=0, and to the traditional kanban system (TKS) if M=0 and E=0.

Let i be the amount of inventories minus backorders, s the number of active flexible servers and x the number of additional kanbans released at the time. The proposed control rule for adjusting the number of kanbans and flexible servers is shown as follows.

Control rule for additional kanbans: Under the assumption of exponential processing times and Poisson demand arrivals, we can model this system as a Markov Chain in terms of states (i, x, s). An example of the Markov Chain of the proposed AKCS is shown in Fig. 2.

{ - - (
If the maximum throughput of MP is greater than the average demand rate then the steady state probability P(i, x, s) of each state exists and can be calculated. Summing the balanced equations of all states (i, E, M) where i < R-E-M, we can prove Eq. ( 1).

Therefore the balanced equations of all states (i, x, s) where K-s+x < i < R-s-x form a finite linear system of N independent equations with N unknown variables P(i, x, s). This system is easy to solve and once the steady probability of each state (i, x, s) has been calculated, and we can estimate the following total cost Z of the system.

Because finished parts and WIP are attached to a kanban, the inventory holding cost I is equal to the average number of cards in the ordering system multiplied by the unit cost h set to 1.

∑ (∑ ∑ )

The backorders cost B is equal to the average amount of backorders multiplied by the unit cost b.

∑ ∑ ∑

The cost F is equal to the average number of times production capacity is adjusted, multiplied by the unit cost f. The first part of the formula is the number of times a server is switched on. The second and last parts are the number of times a server is switched off when i >C-s and x=0 or when i=K+x-s.

∑ ( ∑ ∑ ∑ )

The cost O is equal to the average number of active servers, multiplied by the unit cost d.

∑ ( ∑ ∑ )

Performance Study

Expected Performance under Stable Demand.

We consider the single stage, single product manufacturing process described in section 3, with μ P = 0.15, λ D = 0.2 and the following cost parameters: b=1000, f=1000, d=1. Table 1 presents the best results obtained with the AKCS for S+M varying from 4 to 6 compared to the best result obtained using the TKS and AKS. Each time, parameters K, E, R, and C were chosen for fixed values of S and M after conducting an exhaustive search over the space of possible values. Here, the optimum number of servers with the AKS and TKS is 4. The AKCS yields better performances only if more than 4 servers are available and the gains are very small. However, cost parameters have a strong impact on performance and this is only a result under the condition of d=1 and f=1000. Cost parameters could be changed to advantage the AKCS (smaller f, bigger d). Also Table 1 shows results under stable environment while the AKCS performs much better in unstable environments where there is a great need for adjusting both WIP and capacity (see Section 3.2). For a fixed total number of servers, using more flexible servers leads to bigger inventories, WIP and backorder costs but smaller operating costs. Using Markov analysis we study the impact of the cost ratios f and d on the performance of the AKCS. Demand is considered stable with λ D =0.2. Fig. 3 presents the space (f, d) where the local optimum configuration of the AKCS uses M flexible servers, when b=1000 and S+M=5. As d increases Op increases and using more flexible servers becomes more interesting. As f increases the cost for adjusting capacity increases and performance is reduced. Therefore, if the penalty for switching servers is not prohibitive and d is expensive using more flexible servers may reduce the total cost and improve performance, but if f is too high and d too small, then increasing M reduces performance. Fig. 4 illustrates the impact of parameter M on the total cost, for b=1000 and various values of f and d, when the total number of servers is constant. We observe that the expected gain from the AKCS compared to the AKS is very dependent on the cost ratios. For f=900 and d=0.5, using more flexible servers is counterproductive as the gains from the reduced O are too small to balance F and the increased I and B. As d increases so does O but the expected gain from using more flexible servers becomes significant and the effectiveness of increasing M increases. When d=3.5, if f varies from 900 to 300 then F decreases and the effectiveness of using more flexible servers increases. We observe that the AKCS outperforms the AKS only if the reduced O outweighs the increase in I and B and if F is not too high, but if these conditions are met, it is able to reduce the total cost.

Expected Performance under Unstable Demand

The objective of this paper is to propose a Production Planning and Control system more resistant to changes in the demand than existing mechanisms. To assess its performance under unstable demand we conducted a discrete event simulation of a random walk through the Markov Chain. We considered the same manufacturing process and cost parameters as in the preceding study but unstable demand was introduced as follows: the mean of the demand inter-arrival times is set to 3 during the first 25 time units, 5 for the next 25 time units and 7 for the next 25 time units. This pattern repeats itself for the duration of the simulation. We considered a simulation length of 1,000,000 time units after a warm up of 100,000 time units. Table 2 presents the results of the best settings from Table 1 and new best settings. Parameters K, E, R, and C were chosen for fixed values of S and M after conducting an exhaustive search over the space of possible values. We observe that our proposed mechanism is much more interesting under unstable demand, as the performance gain compared to the AKS is now 6.9% with the same total number of servers and 10.7% with one more server.

Conclusion

This paper considered unstable changes in product demand and proposed an Adaptive Kanban and Production Capacity Control system (AKCS) that adjusts both Work-In-Process and production capacity for minimizing the total operating costs while maintaining a high level of service. The AKCS is able to outperform existing ordering systems under certain conditions and is especially interesting in environments facing demand variability where the cost for adjusting production capacity is acceptable and operating costs relatively high. It would be interesting to assess its performance on multi-stage, multi-product environments. The control rule relies only on monitoring inventories and therefore should be easy to implement.

In the present version, production capacity is adjusted only after adjusting the number of kanbans. Instead, releasing a different number of additional kanbans for each capacity level may increase the global performance by better adjusting the amount of WIP to the current manufacturing process capacity and reducing the risk of switching resources on and off in a short succession.

  -) Control rule for flexible servers: { f an an hen a eman arri es s itch on a ser er f an an hen a part e its s itch off a ser er f an hen a part e its s itch off a ser er -

Fig. 2 .

 2 Fig. 2. Markov Chain for E=2,M=2, C>R

Fig. 3 .

 3 Fig. 3. Impact of the cost ratios f and d on the choice of M, for S+M=5 and b=1000.

Fig. 4 .

 4 Fig. 4. Impact of M on the total cost for S+M=5, b=1000 and various f and d.

Table 1 .

 1 Performance under stable demand; d=1, f=1000, b=1000

Table 2 .

 2 Simulation under unstable demand mean; b=1000, f=1000, d=1

Acknowledgements. This work was partially supported by the Grant-in-Aid for scientific research of Japan Society for the Promotion of Science in 2010-2012.


Antonella Meneghetti
email: meneghetti@uniud.it

Luca Monti
email: luca.monti@uniud.it

How energy recovery can reshape storage assignment in automated warehouses

Keywords: energy efficiency, energy recovery, automated storage and retrieval systems, storage location assignment, picking time

In automated storage and retrieval systems energy in descending and deceleration phases of cranes can be recovered into the power supply system instead of being dissipated as waste heat. Such technological opportunity should be exploited by properly modifying control policies in order to improve energy efficiency of warehousing operations. In this paper the impact of energy recovery on the storage assignment process is analysed. A model of energy consumption with recovery is proposed so that each location within a rack can be associated with energy required to be served in a storage or a retrieval cycle. Shape and distribution of zone for energy-based dedicated strategies are analysed. Energy and picking time performances of different storage policies when AS/RSs machines are equipped for energy recovery are analysed and compared.

Introduction

While in the process industry energy efficiency has been pursued for several decades due to its energy-intensive nature, in the discrete one the current level of control in energy use is very poor or absent [START_REF] Garetti | Sustainable manufacturing: trends and research challenges[END_REF]. However, the growing of green consciousness from one hand and energy resource scarcity from the other are inducing all manufacturing firms to pay attention to energy efficiency as a way to pursue sustainability in all its components. This trend is confirmed by results of the 2011 Material Handling Association of America survey on sustainability in warehousing, distribution and manufacturing [START_REF] Mhia | Sustainability in warehousing, distribution & manufacturing[END_REF], which have highlighted how for more than 60% of respondents the greatest accomplishment is becoming energy efficient. As asserted by Elkington [START_REF] Elkington | Cannibals with Forks: The Triple Bottom Line of the 21st Century[END_REF] in his triple bottom line model, in facts, at the intersection of social, environmental, and economic performance, there are activities that organizations can engage which not only positively affect the natural environment and society, but which also result in long-term economic benefits and competitive advantage for the firm. Reducing energy consumption is one of such activities, since it allows to mitigate greenhouse gas emissions related to energy generation, to reduce natural resource exploitation, and to reduce energy supply costs. Moreover it can be associated with a green imagine of enterprise that can attract and consolidate client fidelity.

Automated warehouses have been seen as intrinsically energy efficient solutions for warehousing. The ability of automated storage systems to store inventory more densely eliminates, in fact, the need for energy to heat, cool, light and ventilate excess square footage [START_REF] Mhia | Automated Storage Systems Make a Play for Sustainability[END_REF]. However, automated storage and retrieval systems (AS/RSs) require energy for movements of their cranes to serve locations within racks. Therefore, new attention is claimed to be paid to control policies of AS/RSs in order to minimize energy requirements also for their storage and retrieval operations, so that the sustainable perspective can be fully embraced.

Storage assignment is the control policy that can more strongly affect picking time, which has been the primary performance traditionally pursued in warehouse management for decades, since it is directly linked to service level perceived by customers (see [START_REF] De Koster | Design and control of warehouse order picking: A literature review[END_REF], [START_REF] Gu | Research on warehouse design and performance evaluation: a comprehensive review[END_REF], and [START_REF] Roodbergen | A survey of literature on automated storage and retrieval systems[END_REF] for comprehensive literature reviews on warehousing). Recent studies have highlighted how it can affect also energy consumption for crane movements and therefore belongs to such activities that can foster sustainability. In particular, when adopting the energy-based full turnover strategy [START_REF] Meneghetti | Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems[END_REF], thus allocating more frequently moved items to convenient locations in order to optimize energy requirements for storage and retrieval operations, the shape of dedicated zones changes from the well known time-based rectangular or L shape [START_REF] Hausman | Optimal Storage Assignment in Automatic Warehousing Systems[END_REF] to a step-wise one, with vertical positions becoming more attractive to exploit gravity in descending phases. These results are based on the assumption of fully dissipating energy when torque becomes negative during descending or deceleration phases and energy flows from the AS/RS machine to motors and is converted into waste heat.

Manufacturers are offering the option of equipping cranes with energy-recovery modules so that energy otherwise dissipated can be re-generated into the power supply system. This technological opportunity should be fully exploited by properly modifying AS/RSs control policies in order to minimize net energy requirements. The challenge becomes to adequate operations management to technological development so that they can enforce each other towards higher and higher sustainability levels. As regards storage assignment, energy recovery can change the relative convenience of each location within a rack, in particular higher locations can become even more attractive, since their gravitational energy can lead to higher electrical energy flows to be supplied to the grid.

Thus, the first question to be answered is if and how energy recovery can change the shape of dedicated zones in comparison to both the traditional time-based zones and the energy-based ones with full dissipation. The second question is how much energy saving can be achieved with the energy-based full turnover strategy associated with energy recovery in comparison to the same storage policy with full dissipation. Furthermore, comparisons with the time-based full turnover policy, where the most frequently moved items are assigned to locations requiring the least picking time, and with other common storage assignment strategies as the random one, can give insight on how much energy can be saved by properly changing the assignment process.

The paper is organized as follows: in sect. 2 the energy model adopted to associate energy consumption to each location in a rack is described, while the shared storage policy is analysed in sect. 3. Zone distributions are analysed in sect. 4, and simulation experiments among different dedicated storage policies are reported in sect. 5.

The Energy Model with Recovery

To adopt an energy based storage assignment, energy related to crane movements along vertical and horizontal axes must be evaluated. Movements along z axis to pick up and drop off loads are independent from locations in the racks and therefore they can be ignored.

AS/RS machines are equipped by a different A.C. 3-phase inverter duty motor per axis; the energy required to reach a given location is the sum of energy provided for the x-axis and the energy provided for the y-axis (time, instead, is calculated as the maximum of the values along the two axes due to simultaneity of movements).

Assuming that crane movements can be described as a rectilinear motion with constant acceleration, speed profile for both horizontal and vertical axes can be either triangular, if maximum allowed speed isn't reached due to limited shifts, or a trapezium one if an acceleration phase, a constant speed phase, and a deceleration phase must be considered in sequence [START_REF] Hwang | Travel-time models considering the operating characteristics of the storage and retrieval machine[END_REF].

Being torque C constant during the acceleration phase of the crane due to the inverter duty motor type, mechanical energy provided by motors can be calculated by integrating the product of the torque and the angular speed at the shaft over time. The torque provided by each motor has to counterbalance inertia of motor and masses to be moved (crane and unit load), friction and gravity (vertical motion). These forces strictly depend on design specifications, maximum speed and acceleration of the AS/RS machine, so we used actual data provided by System Logistics S.p.A., to compute energy values for a given AS/RS configuration New generation cranes are controlled so that horizontal and vertical movements end simultaneously, differing from traditional cranes where an additional torque must be applied to keep in position the load while completing the slowest movement. This means that speed profile of the fastest motion should be modified in order to complete the required shift along the rack in a time as long as the other axis one. We suppose to travel with nominal acceleration until a speed lower than the maximum one is reached and extend the constant phase in order to complete the shift in the same time of the slowest motion. By recalculating for each location the new acceleration/deceleration time and the constant speed time of the fastest motion, it is possible to evaluate the actual energy required to perform a storage or a retrieval cycle in a given position within the rack (energy differs since load is on board during different shifts).

The I/O point is co-located at the lower left corner of the rack, which represents also the optimal dwell point location (i.e. the optimal location where the crane should wait when idle) from the energy perspective, as shown in [START_REF] Meneghetti | Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems[END_REF].

We suppose that the y-motor is equipped for energy recovery in order to exploit gravitational energy. An energy recovery factor is introduced whenever the torque becomes negative, generating negative energy flows that partially balance the positive ones related to the other shifts of each single command cycle. Based on actual data from manufacturers, we assume a prudential overall recovery factor of 26% on the energy otherwise dissipated.

Energy Savings with Shared Storage Policies

We can evaluate energy savings associated with shared storage policies when energy recovery is performed by analysing the rack energy potentials.

The rack energy potential (REP) can be defined as the sum of the energy values required to complete a single storage plus a single retrieval cycle for all the locations in the rack, i.e. 2 idle travels and 2 travels with load on board per location [START_REF] Meneghetti | Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems[END_REF].

Multiplying REP by the overall turnover (total demand divided by rack storage capacity), we obtain a measure of the energy requirement for a given time horizon, when the random storage policy is adopted and every location has equal probability of being visited. Therefore, given a rack storage capacity, energy requirements are proportional to REPs, so we can compare REPs among different rack shapes and different unit load weights to get insight on potential energy savings with shared allocation.

In Table 1 rack energy potentials for a rack storage capacity of 990 unit load locations are reported. We considered two alternative configurations: a 22 levels and 45 columns rack (22×45) and a 10 levels and 99 columns rack (10×99). Since energy recovery depends mainly on gravitational energy, which is related to location height from floor, the rationale is to evaluate the impact that vertical development of the rack can have on energy recovery with respect to horizontal development.

Energy values were evaluated by associating the proper crane specifications with each configuration as suggested by manufacturers.

Unit load weights of 1000 kg, 600 kg and 200 kg were considered in order to evaluate energy savings associated with different classes of product weight.

Recovery factor

Energy saving for a 1000 kg unit load is 13.76% when energy recovery is performed in the 22×45 rack, while it is 6.03% for the 10×99 one. In the more horizontally laid rack, in fact, less energy can be generated during descending phases and recovered into the power supply system. This is enforced when the energy recovery factor grows as shown in Fig. 1, where the curves diverge. As also expected, for a given rack configuration, energy saving grows with increasing unit load weight.

From the design perspective, it comes that the benefit of AS/RSs of allowing better use of vertical space can be further exploited for energy recovery during operations, thus leading to more energy efficient facilities.

Dedicated Zones Shape and Distribution

When a dedicated storage policy is adopted, each item is associated with a given number of fixed locations within the rack. The full turnover policy consists in assigning the most convenient locations to items sorted by their turnover frequency (number of visits per unit load location in the planning horizon) in decreasing order.

If the traditional time-based turnover policy is adopted, the most frequently moved unit loads are assigned to locations requiring the least picking time to be served, so that response time to clients can be minimized. In this case, it is a well-known analytical result [START_REF] Hausman | Optimal Storage Assignment in Automatic Warehousing Systems[END_REF] that dedicated zones have a rectangular or L shape (see Fig. 2). When the energy-based turnover policy is adopted, instead, the most frequently moved items are associated with locations requiring the least energy to be served for a complete storage plus retrieval single command cycle. For full dissipation, dedicated zone shape is step-wise, as shown in Fig. 3. Thus, we wonder about the impact of energy recovery on both shape and positions of dedicated zones. Energy recovered during descending phases is subtracted to the total amount of energy required to complete a storage plus a retrieval single command cycle, and the net value for each location is used for storage assignment.

Results for the first part of the 22×45 rack are shown in Fig. 4. The step-wise shape is confirmed to be a peculiarity of energy-based assignment. The shift towards higher positions from the time-based to the energy-based allocation is even more marked when energy recovery is performed (compare Fig. 234). The most convenient positions occupy, in facts, higher and higher levels which assure a favourable net balance of energy requirements and energy recovery.

If dedicated zone distribution for the 1000 kg unit load (Fig. 4) is compared to the 600 kg one (Fig. 5), it can be noticed how lighter items tend to compensate their lower mass by occupying higher positions in order to enhance energy recovery.

Performance of Dedicated Storage Assignment Policies

In order to analyse the effect of different dedicated energy policies on energy consumption, simulations over 5 time windows for a total amount of 53090 retrievals per period and related reorder point based storage operations were performed.

We considered 100 different items with strictly decreasing demand. As in the fundamental study by Hausman et al. [START_REF] Hausman | Optimal Storage Assignment in Automatic Warehousing Systems[END_REF] we adopted a reorder point policy based on EOQ. An equal ratio of inventory to order costs was selected for all products, to avoid considering different supply policies other then demand rates when establishing the number of locations to be dedicated to each item. Since REPs have highlighted how the shape of the rack significantly affects energy performance we considered both the vertically laid 22×45 rack and the horizontally laid 99×10 one in our simulations. Moreover, to study how a different distribution of items among aisles affects energy saving, we considered also the ABC demand distribution within a single aisle comparing 20-50 and 20-80 distributions of the 100 stored items.

We took as reference the traditional time-based full turnover policy which assure the best time performance as demonstrated by Hausman et al. [START_REF] Hausman | Optimal Storage Assignment in Automatic Warehousing Systems[END_REF] and has been adopted for decades. Then, we adopted the sustainable perspective, which leads to pursue energy efficiency in automated warehouses and therefore to take energy saving rather than time as the primary performance when dedicating zones within a rack. The energy-based full turnover strategy was therefore adopted. We computed performances of the analysed configurations in two cases: when the crane is not provided with energy recovery and when it is, so that benefits of upgrading AS/RS machines with energy recovery can be assessed.

We are interested on creating energy efficient facilities and processes, but we cannot neglect the importance of time performance in warehousing for its direct link with the service level perceived by clients. This is the reason why we computed picking time other than energy requirements for each simulation run, in order to get insight into potential trade-off between time and energy performances.

Results in Table 2 and Table 3 show how relative variations in time and energy performance when moving from the time-based allocation to the energy-based one are of the same magnitude, since energy is the integral of power over time. Therefore a trade-off between picking time and energy consumption arises and a firm should choose what performance leads to major competitive advantage in order to apply the proper allocation policy. However, it should be noticed how in the presence of energy recovery, even the time-based allocation gains energy saving with respect to both time-based and energy-based turnover policies with full dissipation (12.7% for the 22×45 rack and 20-50 demand curve). This means that the energy recovery option can trigger hybrid approaches as a good compromise between time and energy performances, such that of allocating by a time-based policy in order to optimize service level, while still pursuing energy efficiency due to re-generation into the power supply system.

Table 4 allows to analyse, instead, the effect of a radical change of perspective, from the traditional time-based approach associated to time-based assignment with full dissipation to the sustainable perspective of energy-based assignment enforced by energy recovery. In this case improvements on energy efficiency is significantly higher than worsening on time-performance, reaching 16% of energy saving for the 22×45 rack and the 20-50 curve with picking time worsening of 3.5%. More insight on energy recovery can be gained by performing a 2 3 factorial design of experiments, which is obtained by considering the rack shape (99×10 as low level, 22×45 as high level), the ABC demand curve (20-50 as the low level, 20-80 as the high one), and the full turnover policy (the time-based one as the low level, the energy-based one as the high level). Main and interaction effects are reported in Fig. 6 as relative percentage with respect to the basic solution with lower levels. It can be noticed how the rack shape becomes the major factor in the presence of energy recovery, overcoming the demand distribution which was highlighted as gaining the major effect for cranes with full dissipation [START_REF] Meneghetti | Sustainable storage assignment and dwell-point policies for automated storage and retrieval systems[END_REF]. Rack shape effect is even enforced by the adoption of the energy-based storage policy, as the significant related interaction effect underlines.

From a design point of view, results suggest that exploiting vertical space when building automated warehouses together with energy recovery option for cranes should become the preferred practice to enhance sustainability. Attention should be paid also to distribute items among racks so that skewed demand curve can be obtained and therefore advantages related to dedicated policies can be maximized.

Finally, from the control point of view, we underline the energy saving achievable by dedicated policies with respect to the common random policy. If energy recovery is performed, for the 22×45 rack and 20-50 curve 22% of relative decrease in energy consumption is gained by the energy-based full turnover policy, reaching 26% for the 20-80 demand curve.

Fig. 2 .

 2 Fig. 2. Dedicated zones for the time-based full turnover policy for the first part of the 22×45 rack.

Fig. 3 .

 3 Fig. 3. Dedicated zones for the energy-based full turnover policy with 1000 kg unit loads and full dissipation ( 22×45 rack, first part).

Fig. 4 .Fig. 5 .

 45 Fig. 4. Dedicated zones for the energy-based full turnover policy with 1000 kg unit loads and energy recovery (22×45 rack, first part) Fig. 5. Dedicated zones for the energy-based full turnover policy with 600 kg unit loads and energy recovery ( 22×45 rack, first part).

Table 1 .

 1 Rack Energy Potentials

						% REP	22x45	10x99
	Rack	Unit load	REP [MJ]	Recov. REP	% Δrel	30 40
		[kg]		[MJ]		20
	22×45 1000	1,262	1,089 13.76	10
	10×99 1000	1,265	1,189	6.03
	22×45	600	1,189	1,033 13.15	0	0.26 0.30 0.35 0.40 0.45 0.50 0.55 0.60
	10×99	600	1,219	1,150	5.65
	22×45	200	1,115	976 12.46
	10×99	200	1,173	1,112	5.23	Fig 1. Relative % decrease of Rack Energy Potentials for different energy recovery factor

Table 2 .

 2 Time and energy performances of the time-based full turnover strategy (TB) and the energy-based one (EB) with full dissipation.

	Rack	Demand	Time	Time	Energy	Energy	Δ rel	Δ rel
		curve	TB [h]	EB [h]	TB [MJ]	EB [MJ]	Time	Energy
	10×99	20-50 20-80	1019.7 949.7	1033.9 970.7	59036.8 55801.9	58340.8 -1.39% 54878.5 -2.21%	1.18% 1.65%
	22×45	20-50 20-80	1136.8 1084.1	1164.4 1123.5	59779.6 57057.6	58194.4 -2.42% 54779.4 -3.63%	2.65% 3.95%

Table 3 .

 3 Time and energy performances of the time-based full turnover strategy (TB) and the energy-based one (EB) with energy recovery.

	Rack	Demand	Time	Time	Energy	Energy	Δ rel	Δ rel
		curve	TB [h]	EB [h]	TB [MJ]	EB [MJ]	Time	Energy
	10×99	20-50 20-80	1019.7 949.7	1028.6 962.5	54982.9 51744.2	54552.1 -0.87% 51202.1 -1.35%	0.78% 1.05%
	22×45	20-50 20-80	1136.8 1084.1	1176.2 1143.6	52196.5 50121.7	50220.2 -3.47% 47289.6 -5.48%	3.79% 5.65%

Table 4 .

 4 Time and energy variation (relative %) from no-recovery time-based allocation to energy recovery based one. Main and interaction effects on energy requirements of the rack shape (A), demand curve (B), and the full turnover policy (C).

					8%
					6%	Rack	Demand
	Rack	Demand	Δ %	Δ %	4%		Policy
		curve	Time	Energy	2%
		20-50	-0.87	7.60	0%
	10×99	20-80 20-50	-1.35 -3.47	8.24 15.99	Fig. 6. -2%	A	B	C	AB AC BC ABC
	22×45	20-80	-5.48	17.12


Nicola Gramegna
email: n.gramegna@enginsoft.it

Iñigo Loizaga
email: iloizaga@cieautomotive.com

Susana Berrocal
email: sberrocal@cieautomotive.com

Franco Bonollo
email: bonollo@gest.unipd.it

Giulio Timelli
email: timelli@gest.unipd.it

Stefano Ferraro
email: ferraro@gest.unipd.it

The multidisciplinary virtual product development integrates the influence of die casting defects in the mechanical response

Keywords: Numerical simulation, HPDC, casting defects, mechanical properties, integrated approach, process optimization

The performance of an Al alloy component when tested under dynamic conditions is defined on the basis of the amount of the absorbed energy during impact and the dampening rate of the striker. These tests can be complex and costly, and sometimes difficult to be realized for some specific components. An useful and "ideal" approach could be the use of numerical simulation tools for virtually testing, but this objective actually remains an ambitious approach, since it requires a deep research of the factors that determine the elasto-plastic material behavior up to fracture. Even more difficult is the characterization of the material in the case of Al alloy diecastings, where the mechanical properties strongly depend on casting defects. By linking mechanical results with numerical simulation data of filling and solidification, a through-process model is developed to predict the defects' location and amount, thus the structural behavior of die cast components. Furthermore, with particular reference to one demonstrator component, an high pressure die cast steering housing, the innovative correlation between defects (e.g. air inclusion, shrinkage porosity etc.) and mechanical properties has been implemented in MAGMASOFT ® simulation tool in order to transfer the realistic local ultimate tensile strength to LS-Dyna FEM code. The multi-objective optimization strategy has been applied to minimize the air entrapment and maximize the local mechanical properties of Al alloy. The final full integrated, and more realistic, approach permits to estimate the single effect of proper Al diecasting design, remaining defects and residual stress on the absorbed energy under impact condition.

Introduction

In the automotive sector, high-pressure die-casting (HPDC) is the preferred foundry process; it is versatile and appropriate for highly engineered components. Unfortunately, a limit to large diffusion of HPDC remains the final integrity of castings. The high injection rate of the metal causes turbulences, which form internal and surface defects, such as gas entrapment, and the variable thicknesses of the component itself can lead to a reduced effect of the intensification pressure on the solidification shrinkage [START_REF] Apelian | High Integrity Aluminum Die Casting: Alloys, Processes and Melt Preparation[END_REF][START_REF] Timelli | Quality mapping of Aluminium alloy diecastings[END_REF][START_REF] Gariboldi | Handbook of defects in high pressure diecastings, Associazione Italiana di Metallurgia[END_REF]. Although the classification of diecasting defects is clear, an advanced study is required to understand the correlations between the type, dimension and form of defects and the mechanical properties of diecastings. The first model from this study is described elsewhere [START_REF] Apelian | High Integrity Aluminum Die Casting: Alloys, Processes and Melt Preparation[END_REF][START_REF] Timelli | Quality mapping of Aluminium alloy diecastings[END_REF].

In the present paper, the final quality model is described and applied to a Steering Pinion, i.e. a safety component requiring high quality standards in order to guarantee that it stays oil tight under pressure, and to assure proper fatigue resistance and ductile behavior in case of impact.

Simulation and optimization of the die casting process

The design and review of the die cast steering pinion followed the work-flow reported below and acting on a virtual level, before the final physical dynamic testing:

1. process simulation of the existing version of the pinion, and optimization of the HPDC process in order to enhance the quality of the component; 2. identification of the local mechanical properties and residual stresses, and transfer of these information to FEA code for impact analysis; 3. impact simulation using the new model, the pre-stress condition due to residual stresses, and the local mechanical properties at the end of the manufacturing process, in order to determine the most realistic amount of absorbed energy.

Process simulation of the original version of the casting

The first virtual analysis was performed by using the actual manufacturing parameters and the operating thermal regime of the die. This evidenced the possible causes of filling and solidification defects, with the resulting localized reduction of the mechanical properties. The CAD models, the materials, the lubrication and the movement of the die parts in the production cycle were imported in the software to simulate the thermal behavior of the die. The virtual results were compared with thermal maps acquired by IR camera.

In particular, the analysis showed that the most evident porosity in the casting is due to air/gas entrapped in the center of the area with the greatest thickness. Crosssections made of the parts and X-ray NDT investigations identify the defect distribution and morphology. The experimental results were in accordance with numerical simulation ones (Fig. 1). Despite a noticeable variation of thicknesses in the original casting design, the central area of the component, which undergoes the maximum stresses under impact testing, showed some minor interdendritic porosity, mainly located far from the beneficial compacting action resulting from the applied overpressure. An accurate simulation of the cooling rate and the prediction of positions and dimensions of the aforementioned defects represent the basis for working out the local mechanical properties, as further explained.

Optimization of the die casting

The initial objective of optimizing the mechanical properties of the component can be achieved by modifying the geometry of the component or by varying the diecasting system and the process parameters. Firstly, the thickness of the central cylindrical area was reduced. Furthermore, by using an automatic optimization technique, which allows an advanced research of a multi-objective problem [START_REF] Gramegna | Capabilities of new multi-objective Casting Process Optimisation tool[END_REF][START_REF] Linares | iDP approach to quality assessment and improvement of a diecasting process[END_REF], the effect of a single geometric modification to the component was analyzed. The approach adopted is divided into two optimization steps:

• the first step considers the variations of the section of the two gates without any change in the shape of the existing gating and using the original process parameters (solution named ID241); • the second step analyzes a wide range of all possible modifications and variants in order to search out the highest possible quality of the die-casting component (solution named ID882) while respecting the constraints imposed by the already existing die, i.e. extractors, orientation, cooling channels, etc.. The design configurations close to the axis of origin minimize even more the aforementioned objectives, but do not respect the minimum temperature of the molten metal at the gate. Fig. 2 evidences the substantial improvement obtained both in comparison with the original version and with the introduction of the new component with a thickness reduction in the central area (green point in Fig. 2).

Fig. 3. Integration of the optimized configurations, ID882 and ID241, in the existing die

The two main objectives of the optimization process are the minimum quantity and pressure of gas entrapped in the casting, and the reduction of the gating system volume, with the simultaneous reduction or absence of turbulence in the same gating. Among the possible designs analyzed by numerical simulation, the design identified as ID882 was chosen, being the best compromise between the dimension and concentration of the residual gas bubbles in the component (measured by the critical volume of the overpressure) and the maximum pressure and dimension of these (the air overpressure) (see Fig. 2). The suggested configurations, namely ID882 and ID241, were integrated in the same die as shown in Fig. 3. The experimental investigations carried out on the castings confirmed the results expected from numerical simulation.

Determination of the local mechanical properties and residual stresses

The effects of process parameters on material properties were experimentally evaluated by using a multicavity die, which enables to cast specimens for mechanical testing. The multicavity die was used to produce castings with different injection parameters and constant geometry model. Microstructural analysis of cross sections shows the presence of several defects, such as cold shut, segregation bands, oxide films, iron-bearing phases and air/gas bubbles entrapped during mold filling. These defects are typical of the high pressure die casting process, and all specimens contain them, but the amount and distribution vary with the process parameters used [START_REF] Timelli | Quality mapping of Aluminium alloy diecastings[END_REF][START_REF] Timelli | A new reference die for mechanical properties evaluation in die-casting[END_REF]. The bending tests were performed on specimens drawn from a plate produced by the above-described multi-cavity die and the mechanical results were directly correlated with numerical simulations without performing any determination of defect amount. The configuration for the three point bending test is schematized in Fig. 4a., according to ASTM E290-09. As observed in Fig. 4b, the process condition named P5 produced the worst mechanical properties. This is due to the highest metal velocity produced, which causes a turbulent flow and greater gas entrapment. The same variation of process parameters has been applied at virtual level by using the MAG-MAhpdc module in order to simulate the filling and solidification behavior of multicavity die. Fig. 5 shows air-pressure and air-entrapment images of a region of the plate diecast with P5 process parameters. The X-ray image, acquired from the same location, evidences a good agreement between numerical simulations and experimental data. For each specimen the simulations results were correlated each other. The aim was to find an empirical relationship between them and the mechanical properties (particularly UTS). The final suggested model, with normalized coefficients, is:

UTS = A -B×meanairp -C×meanflowlen -D×meanaircontact -E×diffcoolrate (1)
where UTS is Ultimate Tensile Strength [MPa]; meanairp is the mean value of air pressure [mbar] and it identifies the position, severity and dimension of gas entrapment; meanflowlen is the path length of the melt [mm] and it shows the possible cooling of the alloy and the impurities due to a less homogeneous fluid-dynamic behavior; meanaircontact is the time of metal front in contact with air [sec] and it indicates the degree of risk of oxide formation; diffcoolrate is difference between maximum and minimum value of cooling rate in the thickness [°C/s] and it identifies the possible regions of shrinkage porosity.

A "quality mapping" approach [START_REF] Timelli | Quality mapping of Aluminium alloy diecastings[END_REF] was then applied to the steering pinion in order to support advanced design (Fig. 6). By means of a proper tool, this approach can elaborate results from numerical simulation (mold filling, thermal field, defect prediction criteria), with the aim of visualizing the final quality of the casting in terms of defect localization and criticality, local distribution of residual stress and mechanical properties. In comparison to the original cast component which was used as a reference, the last optimization of the shape and process parameters shows that the maximum value of ultimate tensile strength of the material is always in the range of 296-298 MPa. The innovative integrated approach

In order to appreciate the effect of the methodology of integration of process simulation and impact simulation, the results of the impact simulation, performed using original geometry and process (curve A) and assuming homogeneous mechanical properties and no residual stress, are compared in Fig. 7a with three different design approaches: process, without applying the integrated approach; ─ curve C) represents the original model and die casting system, but considering the prediction of the localized mechanical properties and the residual stress state; ─ curve D) represents the modified model and optimized die casting system, using the integrated approach.

In other terms, the similarity of curves B and D (+3% energy absorbed) is backed up by the local UTS values which are very close to the values normally used in "sound" material.

Conclusions

The present work describes an integrated approach between process simulation and impact test evaluation of a diecast component, in order to perform a virtual, but realiable, verification of the life of the component from the production stage up to the impact test. The methodology illustrated is specifically directed at the die casting field of light alloys, as it was demonstrated in the practical application on the component studied, and addresses particularly the stages of product design and development which take place in engineering and design offices, in die casting foundries and in end-users industrial companies. The innovative aspect of the proposed method does not consist in the use of simulation software for the process or the mechanical response, both highly adopted in their fields of application. It consists in the prediction of the mechanical properties as a function of the process, in the prediction of the quality distribution in diecastings and finally in the communication between the different software applications. This makes possible to visualize the realistic mechanical behavior of the cast product and consequently allows the validation and prediction of the mechanical response of the diecast component at in-service conditions.

Fig. 1 .

 1 Fig. 1. a) X-ray image in comparison with b) defect prediction by numerical simulation

Fig. 2 .

 2 Fig. 2. Possible designs for steering pinion plotted as function of the critical overpressure in the casting and the maximum overpressure in the gating system

Fig. 4 .

 4 Fig. 4. a) Sketch of three point bend test adopted in this work; b) experimental measurements of force (F) versus displacement (W) obtained in bending tests

Fig. 5 .

 5 Fig. 5. Coherence verification of the numerical simulations of process P5 : a) X-ray images of diecast plate; b) air-pressure criteria; c) air-entrapment criteria; d) air-pressure representation with a value greater than 1850 mbar

Fig. 6 .

 6 Fig. 6. Mechanical properties (UTS) and residual stresses transferred from a CV to a FEM program: a,b) local UTS properties prediction; c,d) residual stress distribution

Fig. 7 .

 7 Fig. 7. a) Comparison between the results of different impact simulations; b) result of impact test simulation

Acknowledgements. This work was developed with the financial support of the European Project NADIA (New Automotive components Designed for and manufactured by Intelligent processing of light Alloys, NMP-2004-SME 3.4.4.5, contract n.026563-2).


Malamati Bitzidou

Dimitrios Chrysostomou

Antonios Gasteratos

Multi-camera 3D object reconstruction for industrial automation

Keywords: object assembly, multi-camera system, octree decomposition, 3D reconstruction, industrial automation

In this paper, a method to automate industrial manufacturing processes using an intelligent multi-camera system to assist a robotic arm on a production line is presented. The examined assembly procedure employs a volumetric method for the initial estimation of object's properties and an octree decomposition process to generate the path plans for the robotic arm. Initially, the object is captured by four cameras and its volumetric representation is produced. Thereafter, a quality check with its respective CAD model is performed and the final details of the 3D model are refined. An octree decomposition technique is utilized afterwards to facilitate the automatic generation of the assembly path plans and translate them to a sequence of movements for the robotic arm. The algorithm is fast, computationally simple and produces an assembly sequence that can be translated to any major robotic programming language. The proposed algorithm is assessed and preliminary experimental results are discussed.

Introduction

The use of traditional manufacturing machinery such as robotic systems as a way of rapid manufacturing has attracted growing interest in recent years. Intelligent automation is one of the outcomes of this interest and its major impact enables a significant reduction in production time and cost, machining work and product quality. One of the manufacturing processes, in which intelligent automation is applied, is assembly, which constitutes an important stage in product development and accounts for a large proportion of the manufacturing costs. However, assembly still remains one of the least understood manufacturing processes [START_REF] Medellín | Automatic generation of robot and manual assembly plans using octrees[END_REF]. In this paper an object assembly sequence is planned that incorporates the production of its volumetric model by a multi-camera system, its three-dimensional representation with octrees and its construction implemented by a robot arm of 5 degrees-of-freedom and a gripper. The final goal is to plan a robot arm path consisting of predetermined motions for the assembly of everyday objects.

The rest of this paper is organized as follows. Section 2 provides a review of the related works regarding multi-camera vision systems, automatic assembly planning processes and 3D object representation methods. In section 3 the overview of our system is described and the details about each one of the steps are discussed. Finally, the paper concludes in section 4 where the final remarks about the performance of the algorithm are given along with discussion for future research endeavors.

Related work

Technological advances in sensor design, communication, and computing are stimulating the development of new applications that will transform traditional visual systems into pervasive intelligent camera networks. Applications enabled by multi-camera networks are very common in industry. [START_REF] Aghajan | Multi-camera networks: principles and applications[END_REF]. Systems utilizing multiple visual sensors are required in many manufacturing applications. Over the last few years the state of the art concerning multi-view stereo is improving rapidly. The goal of multi-view stereo is to reconstruct a complete threedimensional object model from a collection of images taken from known camera viewpoints. A number of high-quality multi-view stereo algorithms have been developed, compared and evaluated [START_REF] Seitz | A comparison and evaluation of multi-view stereo reconstruction algorithms[END_REF]. In order to achieve multi-view stereo, systems consists of many visual sensors are usually needed. The uses of multicamera systems in a variety of tasks premise the determination of optimal sensors configurations. Each system has its own set of requirements, but there is a generic formulation that can be customized to find optimum sensor configurations for all the systems [START_REF] Mittal | Generalized multi-sensor planning[END_REF].

Sensor planning has been researched quite extensively and several approaches have been developed in the last decades, so there are many different variations depending on the application. A popular set of methods, called next-view planning, attempt to built a model of the scene incrementally by successively sensing the unknown world from effective sensor configurations using the information acquired about the world up to this point [START_REF] Hager | Computational methods for task-directed sensor data fusion and sensor planning[END_REF], [START_REF] Cameron | A bayesian approach to optimal sensor placement[END_REF], [START_REF] Kutulakos | Recovering shape by purposive viewpoint adjustment[END_REF], [START_REF] Maver | Occlusions as a guide for planning the next view[END_REF], [START_REF] Ye | Sensor planning for 3d object search[END_REF], [START_REF] Miura | Task-oriented generation of visual sensing strategies[END_REF]. A related set of methods [START_REF] Sing | Visual tunnel analysis for visibility prediction and camera planning[END_REF] have focused on finding the ideal sensor positions for capturing a static scene from desirable viewpoints assuming that some geometric information about the scene is available. Bordering on the field of graphics, the main contribution of such methods is to develop efficient methods for determining the view of the scene from different viewpoints. Methods that are directly related to ours are those that assume that complete geometric information is available and determine the location of static cameras so as to obtain the best views of an object. This problem was originally posed in the computational geometry literature as the art-gallery problem [12]. The traditional formulation of such problem assumes that two points are called visible if the straight line segment between them lies entirely inside the polygon. Even with such simple definition of visibility, the problem is NP-complete.

The continuous demand for reduced costs but increasing speed and quality in product manufacturing has led to the requirement for the rapid prototyping of solid models prior full-scale production. This is the reason why automatic assembly planning is essential. The series of technical issues addressed in the process of automated assembly planning are the assembly sequence representation at first, the generation and evaluation provided by the assembly assumptions, the calculation of the accuracy and efficiency of the planning process, the integration of the CAD program and finally the motion planning [START_REF] De Mello | Computer-aided mechanical assembly planning[END_REF]. Numerous algorithms have been exploited for the rapid and effective production of feasible assembly tasks. The vast majority of assembly sequence generators, namely ASG, convert the problem of employing assembly sequences into disassembly ones. During the testing of their feasibility several issues must be considered such as geometrical and mechanical feasibility, manipulability, stability, visibility and used material.

An essential preprocess step of the assembly planning procedure is the threedimensional representation of the object. The state of the art has changed dramatically over the last years, warranting a new overview in the field. Multiple approaches for assembly, utilize 3D geometry on a regularly sampled threedimensional grid (volume), either as a discrete occupancy function [START_REF] Slabaugh | Methods for volumetric reconstruction of visual scenes[END_REF] or as a function encoding distance to the closest surface [START_REF] Jin | Multi-view stereo reconstruction of dense shape and complex appearance[END_REF]. The popularity of threedimensional grids is increasing due to their simplicity and their ability to estimate various kinds of surfaces. A surface can be represented as a set of connected planar facets using polygon meshes, whilst in case of multiple views one can represent the examined scene as a set of depth maps. Among the multitude of existing methods, one can distinguish the use of octree approximation as an approach of compact visualization of computer-aided design models, which was recently reported [START_REF] Medellín | An automated system for the assembly of octree models[END_REF].

System Overview

The proposed method lies within the category of intelligent assembly path planning methods. The main concept of the algorithm is to produce the volumetric model of the object, represent it using octrees and then create an assembly sequence of predetermined motions. The block diagram of the proposed algorithm is shown in Fig. 1 and the details for every step are given below.

Image Acquisition and Camera Calibration

The system's cameras topology is an application depended task and, therefore, it is based on the desired coverage of the working space taking into account the best combination of number of visual sensors and available bandwidth. In order to provide visual data for accurate 3D object assembly, a four-camera system was assembled inside our lab. The visual sensors are thus, mounted on the four corners of a cube processing 90 • rotation along their optical axis, 60 • rotation on the x axis and 150cm translation between them. Fig. 2 depicts the system's architecture and shows the position of the cameras inside the cubical structure. The first step of the whole process comprises an initialization phase where the calibration of the multi camera system occurs. The calibration procedure is fast, straightforward and results the registration of the cameras to the room coordinate system, employing a classic method [START_REF] Tsai | A versatile camera calibration technique for high-accuracy 3d machine vision metrology using off-the-shelf tv cameras and lenses[END_REF] realized within the Camera Calibration Toolbox for Matlab [START_REF] Bouguet | Camera calibration toolbox for Matlab[END_REF]. The multi-camera rig acquires static snapshots of the object in a relatively controlled environment. The cameras viewpoints are carefully chosen in a way that will let us acquire the maximum possible shape information from a set of objects images. Hardware configuration. The proposed vision sensorial framework consists of a four F lea2 cameras, manufactured by Point Grey Research Inc [19] and are able to capture images of up to 1288×964 pixels resolution transmitted at a frame rate of 30Hz. They are manufactured using the industrial form factor of similar analog cameras and their size is exceptionally small. All cameras are equipped with 6mm lenses, which is equivalent to 34.25mm in the 35mm format, resulting in a working 61.36 • angular field of view. All four cameras are connected through IEEE 1394b interface to a dedicated master PC, whilst the data transmission is established with the IEEE 1394b transfer protocol using two Point Grey Research PCI Express cards.

Initial 3D model construction

Following the camera calibration and image acquisition steps, the obtained image set is then processed in order to produce the first estimation of the 3D model of our examined object. Initially, each image is projected into an array of pixels and an accurate image thresholding approach is employed to result the first black-and-white silhouette of each source photograph as shown in Fig. 3. Having the introductory 2D approximation of the shape of the model consisted of pixels, a background subtraction algorithm isolates the object from its background information. Thereafter, the maximal shape that yields the same silhouettes as the actual object for all views, the so called Visual Hull [20] of the object, is approximated. The Visual Hull is the maximum possible shape that can be retrieved by the object's views and is consistent with its respective silhouettes as seen from any viewpoint in a given region. Once the visual hull of the test object is created it is then merged with its respective 3D CAD model to further refine any discrepancies appeared in the surface of the visual hull. The outcome of this algorithmic step is an initial approximation of the 3D model of the object that will be used as an input for the OcBlox system, analyzed in the next section.

Octree decomposition

In order to achieve the assembly of the model, a system heavily inspired by the OcBlox one, introduced by [START_REF] Medellín | An automated system for the assembly of octree models[END_REF] is used. It also consists of the octree decom-Fig. 3. The everyday objects used as an input for the assembly process and their respective black-and-white silhouettes. position technique, the assembly planning and an assembly cell. The proposed system applies octree decomposition to the 3D visual model in order to convert it into approximate octree models. The octree decomposition technique results to hierarchical structure, that subdivides a volume into cubes of varying sizes, named octants. Among the several cubes a hierarchical tree relationship holds, whereat, each branch is associated to the relative position of the octant in the respective parent node. Octants are distinguished as full, empty or boundary, depending on their relative location in the 3D CAD model: inside, outside or partially inside, respectively. For maximum approximation, full and boundary octants are included in the rough model. The final octree is checked against different criteria, such as the maximum level of decomposition or the minimum size of octant. By concluding the decomposition process, the list of octants form an approximate representation of a 3D CAD model. In this system, octant sizes are limited to particular sizes according to the capability of the manufacturing process. An example of such an octree representation is depicted in Fig. 4.

Assembly planning

An assembly cell that is used to build cube-based models is developed as shown in Fig. 5 The 3D data extracted from the previous step are the input for the assembly planning system. Thereafter, an estimation of the quantity and dimensions of the wooden cubes needed to assemble the real object is calculated. Their respective coordinates for their correct positioning inside the scene are then computed as well. The outcome of this process is the assembly instructions for the construction of the real prototype from the robotic arm.

Conclusion

In this paper, a method that performs object assembly using an intelligent vision network is presented. The proposed procedure is based on a volumetric approach for the objects reconstruction resulting to an initial 3D model. After the merging of this 3D model with its 3D CAD design, it can be represented using octree decomposition technique. The final 3D model prototype is constructed by a block of solid cubes positioned inside the assembly cell by the robotic arm. An assembly planning process that generates a sequence of predetermined motions specifies the exact positions of the cubes. The main contribution of the algorithm is the ability to reconstruct an ordinary object using only a system of four cameras. The algorithm is fast, computationally simple and the assembly sequence that is produced can be translated to any robotic programming language. Besides, the computational burden is directly linked to the complexity of objects geometry. The proposed technique is applicable to industrial smart manufacturing systems, to rapid prototyping cells and to standard robotic and machine vision applications.

Fig. 1 .

 1 Fig. 1. Block diagram of the proposed method.

Fig. 2 .

 2 Fig. 2. The multi-camera system's architecture.

Fig. 4 .

 4 Fig. 4. Example of an octree representation.

Fig. 5 .

 5 Fig. 5. The robot arm and the workspace used for the assembly cell.

  comprising:1. The SCORBOT-ER 9 Pro (5(a)) industrial robot arm with great accuracy of nominal repeatability of ±0.05mm, maximum operation speed: base rotation 140 • /sec, shoulder rotation 123 • /sec, elbow rotation 140 • /sec, wrist pitch 166 • /sec, wrist roll 300 • /sec and a USB Controller-B with 16 I/O that can be read and generated via an Advanced Control Language (ACL). 2. A workspace where the configuration of the assembly cell is maintained and the cubes that constitute the prototype models are stored. (5(b)). 3. Raw material. Solid cubes of 10, 20 and 40 mm, made of wood with texture additions are used. 4. A camera with frame rate of 25 fps and resolution of 640x480 pixels is also used to recognize each wooden cube's coordinates and project them to the robot arm's coordinate system.