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Memristor, the missing fourth element predicted by L. Chua, has recently been in the research focus since HP Lab reported the first TiO 2 thin film memristor realization. The nano-scale geometry size of the memristor makes it difficult to control its dimensions due to the process variation incurred in the fabrication process. This process variation results in yield degradation in the memristor-based memories. This yield degradation is more severe when the memristor device is used as a multi-valued memory element. In this paper, the impact of the process variation on the memristor-based memory yield is investigated for the 1-bit, 2-bit, and n-bit memristor memory element. In addition, two approaches are proposed to improve the memory yield. Therefore, the main objective of this work is to introduce a statistical yield simulation flow to calculate the memory statistical yield under process variations and investigate the effect of different design knobs on this statistical yield regardless of the memristor models and the process variation models used. Simulation results reveal that for 1-bit memristor-based memories, the nominal write voltage should be increased by 30% and the nominal threshold value (i.e., the midway memristance value between the memristor ON resistance and the memristor OFF resistance) should be increased by 65% to achieve the maximum yield. Finally, the paper lists the minimum memristor size that should be used to achieve a 99.9% memory yield for n-bit memories. These results show how the process variation imposes limitations on the minimum memristor device size when multi-valued memories are to be designed. [ABSTRACT FROM AUTHOR]

Geometric and dimensional variations in objects are caused by inevitable uncertainties in manufacturing processes and often lead to product quality challenges. Failing to model the effect of object shape errors, i.e., geometric and dimensional errors of parts, early during the design phase inhibits the ability to predict such quality challenges. This consequently leads to expensive design changes after freezing of design. State-of-art methodologies for modelling and simulating object shape error have limited defect fidelity, data versatility, and designer centricity that prevent their effective application during the early design phase. To overcome these limitations, this paper presents a novel Morphing Gaussian Random Field (MGRF) methodology for object shape error modelling and simulation. The MGRF methodology models the spatial correlation in the deviations of the part from its nominal design using Gaussian Random Fields and then, utilises the modelled spatial correlations to generate non-ideal parts by conditional simulations. The MGRF methodology has (i) high defect fidelity enabling it to simulate various part defects including local and global deformations, and technological patterns; (ii) high data versatility allowing it to simulate non-ideal parts under the constraint of limited data availability and to utilise historical non-ideal part data of similar parts; (iii) designer centric capabilities such as performing 'what if?' analysis of defects of practical importance; and; (iv) the ability to generate non-ideal parts conforming to statistical form tolerance specification without additional modelling effort. The aforementioned characteristics enable the MGRF methodology to accurately model and simulate the effect of object shape variations on product quality during the early design phase. Practical applications of the developed MGRF methodology and its advantages are demonstrated using sport-utility-vehicle door parts and compared against state-of-art methodologies. • Models object shape error with high defect fidelity and data versatility. • Simulates non-ideal parts under the constraint of limited data availability. • Models and learns manufacturing process signatures from historical data. • Performs 'what if?' analysis of practically relevant defects scenarios. • Simulates statistical form tolerance conformance without additional modelling effort. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we apply a stochastic differential equation (SDE) as a mathematical model that describes the manufacturing process from input of materials to the end of the process. We utilize the risk-neutral principle in stochastic calculus based on the SDE. We extend the mathematical model to allow for objective evaluation. In order to improve the lead time in a manufacturing process, we introduce an evaluation equation that satisfies a certain compatibility constraint with respect to the lead time; this compatibility constraint with lead time refers to a strategy that eliminates irrelevant processes from the overall process, and results in improvements to the process. We assume a probability density function of log-normal type, which is the probability density function from the input materials to the end of the process. The present paper evaluates a certain compatibility constraint with lead time by applying risk neutrality and the martingale measure of the stochastic integral under this assumption. After the evaluation value is calculated by the evaluation equation, a process control system evaluates each subprocess and performs rescheduling to improve the utilization efficiency of the entire manufacturing process. Thus, a front-end process is used to cancel requests to the process, and follows the instructions in the process control systems. However, the present paper gives only a simulation evaluation in which the parameters are calculated, and does not discuss compatibility between an evaluation value based on the calculated manufacturing lead time compatibility constraint and a real system. However, the paper offers an idea for finding bottleneck subprocesses in a manufacturing process, and for use as a software tool for process synchronization. [ABSTRACT FROM AUTHOR]

This paper presents a research that investigates solutions and algorithms for determining the optimum machine layout served by autonomous material handling system, like mobile robot or automated guided vehicle. Unlike previous works which solved the layout problem by optimizing the distance between facilities, in this paper the machine layout is addressed based on optimizing the travel time of the material handling system. The proposed approach can include boundary kinematic constraints of vehicle while optimizing the objective function such as velocity, acceleration, orientation, and trajectory curvature. The nonlinear constrained model is transformed to unconstrained problem using penalty method. Then, a simulated annealing-based algorithm is used to search for the optimum locations of machines among all possible feasible layouts. The simulation results showed that the proposed approach was efficient enough to use in real factories due to including a vehicle path planner integrated in an overall layout design scheme that involves searching of vehicle control parameters. [ABSTRACT FROM AUTHOR]

In the minimultimedia market, functional diversity, high performance, and design are given top priority. Lightweight mobile phones of unique design especially attract customer interest and are usually considered to be highly competitive items in the marketplace. Therefore, mobile phones need wider and thinner liquid crystal displays and smaller microspeakers with high acoustic performance. There are two main directions for development of slim microspeakers. One is reducing the thickness, and the other is reducing the width. In the case of reducing the width, the total magnet volume is maintained through extending the length of the magnet circuit [S. M. Hwang et al., IEEE Trans. Magn. 39, 2003 (2003)]. In contrast, reducing the thickness results in a reduction in total magnet volume unless width is increased, which causes many difficulties in the manufacturing process. In this paper, we introduce a new magnetic circuit for slim microspeakers. This new magnetic circuit, which excludes the top plate, makes it possible to manufacture slimmer microspeakers more cost-effectively without any loss of acoustic performance. [ABSTRACT FROM AUTHOR]

Simulations are widely used in manufacturing system design, production planning and decision making. The aim of this paper is to present the possibility of using Monte Carlo simulations in the production plan optimizing and in the project risk management. Optimization is accomplished through two different approaches which principles and results are mutually compared. According to the first approach, production optimization is performed via a deterministic model using the Generalized Reduced Gradient algorithm. The second approach is based on the stochastic model. The optimized production plan is submitted to risk analysis. Two approaches are demonstrated in order to reduce the rate of risk. The first way is modifying the production plan to increase the forecast reliability; the second approach is limiting the uncertainty of key variables. The detailed methodology enables implementing presented approaches in solving various optimization tasks. [ABSTRACT FROM AUTHOR]

Currently, robotic manufacturing cells entail complex decisions concerning sequencing issues due to uncertainty which arises in different parameters such as time to failure, time to repair and cycle times that can be effectively supported by computer simulation models. The paper is focused on part sequencing of a two-machine robotic cell in a flow shop which produces different parts. The process is supported by a single gripper robot to load/unload products and also in displacement within the system. This study considers machine failures and repair such that S2 cycle time and total production cost should be minimized. In this study, simulation facilitated input part sequence and also data envelopment analysis method is applied to trace the optimum sequence for satisfying the objective functions. Results through some numerical examples showed some simulation advantages specially to model many uncertainties and what if analysis. [ABSTRACT FROM AUTHOR]

FinFET technology is one of the most promising candidates in replacing planar MOSFET beyond the 22 nm technology node. However, the complexity of FinFET manufacturing process has caused challenges in reliable device testing. Gate oxide short (GOS) is one of the dominant defects that has significant impact on circuit reliability. In this paper, we present a GOS defect model for FinFETs by introducing the defect as a pinhole in the gate oxide of a triangular fin shape structure. The pinholes are represented by small cuboid cuts of various sizes on the fin top and sidewalls along the channel. The 3D Sentaurus TCAD simulation results in the development of an analytical GOS defect model that can be used in circuit-level fault modeling, which leads to generating more realistic test patterns. [ABSTRACT FROM AUTHOR]

In this paper, a hierarchical clustering selection based weighted random forests scheme is proposed for fault classification in complex industrial processes. Model diversity and the strength of each model are deemed to be two key issues for the performance of ensemble learning method. To improve the diversity between classification trees and the performance of individual classification trees in random forests, the hierarchical clustering method is applied for offline model selection in random forests, which can simultaneously reduce the online fault classification complexity. Meanwhile, the weighted voting rule is used in random forests instead of majority voting, in order to boost the good performance models and weaken the bad ones. Detailed comparative studies between proposed method and conventional methods have been carried out through the Tennessee Eastman (TE) benchmark process. [ABSTRACT FROM AUTHOR]

TheKarlsruhe Institute of Technology (KIT) agreed to construct and test the high-temperature superconductor (HTS) current leads for the tokamak JT-60SA presently under construction in the frame of the Broader Approach agreement between Europe and Japan. In total 6 HTS-CLs for 25.7 kA for the TF coils and 20 HTS-CLs for 20 kA for the PF and CS coils are needed. In addition, the acceptance tests of the HTS current leads were performed in the test facility CuLTKa at KIT. The manufacturing started in 2014 and the last current leads were tested in July 2017. The main parts of the acceptance test are the determination of the heat load at 4 K and the 50 K He mass flow rate through the heat exchanger as well as the simulation of a loss of flow accident. One test of the PF current leads also included a pulse test at 20 kA to demonstrate their PF operation capability. In this paper, an overview of the manufacture and acceptance tests of the HTS current lead is given. The results for the different current leads are summarized and compared to the specifications. [ABSTRACT FROM AUTHOR]

From Germany's Industry 4.0 mission to Made in China 2025 and Make in India mission to British Factory of the Future in 2050, digital manufacturing (DM) is promoting in the world's major industrial countries as a technology foundation of the future manufacturing. At the same time, in the different segments of the DM realm, different forms of information technologies (IT) are flourishing such as the following: computer-aided manufacturing, robotics control in manufacturing, and process simulation. This paper is aimed to review the latest initiatives ofDMin the leading universities and major industrial countries. Along with, a critical literature review of various initiatives in the area of DM-assisted hybrid additive manufacturing (DM-HAM) has also been carried out. DM-HAM seems to be very promising for next generation multi-operational manufacturing as it is time saving and economical. The highlights of this review will provide a guide for the upcoming research activities in the area of DM-HAM. [ABSTRACT FROM AUTHOR]

With technology scaling, a common and efficient strategy to improve the soft error vulnerability of sensitive nodes is to place well/substrate contacts frequently. This paper reports a revised method to integrate the impact of well contacts on SEE response with the bias-dependent SE compact model for circuit simulation. After modifying the SE sub-circuit with resistors and current source placed between the n-well and p-well contacts and then calibrating the parameters by layout-level TCAD simulation results, the resulting model is able to evaluate the SEE vulnerability of devices and circuits with various well contacts. Besides, it is able to evaluate the hardness performance of well contact optimization before fabrication. [ABSTRACT FROM AUTHOR]

It is known that optimizing the investments on safety to reduce probabilities of blowout fire is very challenging due to the complexity of operational systems which involve varieties of potential contributors and ranges of safety measures. In this paper, a new method to evaluate blowout fire prevention and control measures is proposed in a cost effective m anner. Firstly, a dynamic probability prediction model for blowout fire is proposed through the system dynamics (SD) method. The dynamic probability of blowout fire on offshore drilling platform is predicted according to the proposed model. Secondly, cost-benefit analysis of the corresponding safety measures is carried out using SD simulation via Vensim software. Thirdly, a case study of blowout fire probability prediction and the cost-benefit analysis of safety investments are demonstrated for some offshore drilling platform. The simulation results show that the predicted probability of blowout fire on offshore drilling platform increases from 2.41E-6 to 3.997E-6. The probability prediction formula of blowout fire is nonlinear fitted to obtain the intrinsic relationship between the probability of blowout fire and time. The case study shows that the built cost-benefit analysis model can be used to optimize the allocation of safety investments. [ABSTRACT FROM AUTHOR]

Silicon content prediction is quite significant for supervising the state of blast furnace and is usually selected as the indicator to represent the thermal state. In practical industry, the fluctuation exists in the operation of blast furnace all the time. What's worse, it is inaccurate to build the predictive model with many outliers. To solve these problems, this paper has developed a model to predict the silicon content using support vector regression (SVR) combined with clustering algorithms, including hard C-means (HCM) clustering and fuzzy C-means (FCM) clustering. Through data processing, the data points are clustered based on the similarity, and then different SVR models are established. In order to make full use of FCM, a new method using multiple SVRs and FCM based on membership degree (MFCM-SVRs) is proposed where the membership degree is applied to eliminate the outliers. Simulation results verify that the multiple SVRs based on HCM (HCM-SVRs) and MFCM-SVRs possess superiority in terms of accuracy and speed, which makes the method serve better for practical production. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) maturity allows diffusion of this technology in conventional production environments. In the decision to adopt a new technology, production costs are one of the most important factors to analyse, even if they are not developed enough yet. In the last decade, several cost models for AM have been proposed, but each of them focuses on a specific aspect of the process, lacking the ability to consider the effective costs associated with AM, i.e. regarding AM as part of a more general production context. The aim of this study is to develop a cost model that evaluates process costs of AM for relevant technologies such as stereolithography, selective laser sintering and electron beam melting when integrated in a general production process. The integration of AM on the shop floor is proved by the introduction of an index such as the Overall Equipment Effectiveness (OEE) index, which allows this evaluation to be more connected to real production system issues. At the end of the paper, an experiment to compare the results of the proposed model with those of previous studies is reported and it is put in evidence how this model overcomes the previous problem of estimation. [ABSTRACT FROM AUTHOR]

Density's increase in Static Random Access Memory (SRAM) has become an important concern for testing, since new types of defects, that may occur during the manufacturing process, are introduced. On the one hand, new manufacturing defects may lead to dynamic faults, which are considered one of the most important causes of test escape in deep-submicron technologies. On the other hand, the SRAM's robustness is considered crucial, since it may affect the entire SoC. One of the most important phenomena to degrade SRAM reliability is Negative-Bias Temperature Instability (NBTI) causing the memory cells' aging. In this context, the paper proposes to analyse the impact of NBTI on SRAM cells with resistive defects that eventually escape manufacturing test and, with aging, may generate faults over time. Finally, SPICE simulations adopting a commercial 65 nm CMOS technology library have been performed in order to estimate NBTI's precise impact over time. [ABSTRACT FROM AUTHOR]

Abstract: Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper. [Copyright &y& Elsevier]

With the development of casting process and its numerical simulation technique, many demands of realism and clearness in post-processing have arisen. This paper introduces the implicit surface to post-processing for the results of casting simulation so as to meet these demands. In this paper, a robust and fully automatic algorithm is proposed for extracting the implicit surface in the signed distance function calculated from the import stereolithography files. Then several applications of implicit surface in the post-processing of casting simulation are listed in two categories. The applications in the first category include fast rendering of massive data, section view in arbitrary direction, visualization of residual liquid, and casting deformation, giving clear and pretty visualization of the results in simulation, and making it possible for engineers and process designers to look through the opaque mold, gain insights to casting process, and locate possible defects. The applications in the second category involve thickness distribution and modulus calculation, which offer important characteristics of castings and facilitate the optimization of casting processes. [ABSTRACT FROM AUTHOR]

An ever increasing trend in today's dynamic and competitive world markets, manufacturing organizations should provide the right amount of flexibility at the right time in the right direction. Among many manufacturing strategies available, reconfigurable manufacturing system is emerging as an attractive alternative to meet this goal. Organizations in pursuit of reconfigurations are often presented with a number of options and hence face a problem of evaluating the feasible alternative configurations before choosing the best one. The study reported in this paper is related to an organization interested in reconfiguring the present manufacturing setup. A number of alternative configurations were identified. Simulation models were developed for these configurations, and the results of the simulation experiments were analyzed to assess the suitability of each configuration in the point of view of five performance measures. The simulation experiments, method used to analyze the simulated results, and the outcome of the study are reported in this paper. [ABSTRACT FROM AUTHOR]

This paper explores energy efficient building design for manufacturing plants. Many efforts have been directed into the field of building design optimization concerning building energy performance, but most of the studies focus on residential buildings or public buildings. Very limited research results studying plants buildings have been reported. However, plants buildings have certain unique features that make the design problem more challenging. Furthermore, the approaches presented in the current publications could not guarantee the performance of their designs if the computation capacity is limited. This paper attempts to address these two issues. First, an EnergyPlus-integrated overall energy consumption estimation framework is developed for a class of manufacturing plants, where the environmental conditions would not affect the energy consumption of the production processes. Based on that, the building design problem for this type of manufacturing plants is formulated as a stochastic programming problem concerning uncertainties arising from the future weather conditions and energy prices, where seasonal production scheduling optimizing is incorporated when estimating the performance of building designs. Second, Ordinal Optimization (OO) method refid="ref1"/ is introduced to solve the problem so as to quantitatively guarantee a high probability of finding satisfactory designs while reducing the computation burden. A numerical example is provided, showing our solution method performs effectively in finding a satisfactory design. [ABSTRACT FROM AUTHOR]

Abstract: Up-to-date market dynamics has been forcing manufacturing systems to adapt quickly and continuously to the ever-changing environment. Self-evolution of manufacturing systems means a continuous process of adapting to the environment on the basis of autonomous goal-formation and goal-oriented dynamic organization. This paper proposes a goal-regulation mechanism that applies a reinforcement learning approach, which is a principal working mechanism for autonomous goal-formation. Individual goals are regulated by a neural network-based fuzzy inference system, namely, a goal-regulation network (GRN) updated by a reinforcement signal from another neural network called goal-evaluation network (GEN). The GEN approximates the compatibility of goals with current environmental situation. In this paper, a production planning problem is also examined by a simulation study in order to validate the proposed goal regulation mechanism. [Copyright &y& Elsevier]

Extreme-scale computer systems take advantage of large arrays of general-purpose multicore processors coupled with specialized manycore accelerators. In order to support complex applications and correctly feed such processing elements, increasingly larger memory cores are integrated at different levels of the hierarchy. However, the adoption of increasingly aggressive manufacturing processes makes the memory sub-system particularly sensitive to faults. Error correcting codes (ECCs) allow the memory to recover from faults at run-time without interfering with the application execution. However, due to the loss of performance introduced every time an error must be corrected, the persistence of faults requires a more radical repair approach in which faulty cells are physically replaced by spare ones. Memory redundancy analysis (MRA) algorithms are used to drive the allocation process of spare resources. Many one-dimensional and two-dimensional MRAs have been proposed, but tools for evaluating their recovering capability are still not well established. This paper presents SIERRA, a simulation environment for precisely evaluating the repair efficiency of an MRA considering different fault signatures and faulty memory configurations. Our simulation engine provides a precise estimation of the MRA quality by analyzing the behavior of the MRA on several faulty memory configurations. To this end, different parameters such as the area of the memory blocks and the defect density are taken into account. The evaluation of the quality of an MRA takes into account its repairing capability, the power consumption derived from its execution, and the area overhead. Thanks to the use of a database for storing information, our tool is able to speed-up the simulation process by distributing it among several nodes. All these features make SIERRA essential in supporting the design of next-generation high-performance computers. [ABSTRACT FROM AUTHOR]

In this paper, a new control scheme is proposed for Phase-II monitoring of simple linear profiles in multistage processes. In this scheme, an approach based on the U transformation is first applied to remove the effect of the cascade property involved in multistage processes. Then, a single max-EWMA-3 control statistic is derived based on the adjusted parameter estimates for simultaneous monitoring of all the parameters of a simple linear profile in each stage. Not only is the proposed scheme able to detect both increasing and decreasing shifts but it also has the feature of identifying the out-of-control parameter responsible for the source of process shift. Using extensive simulation experiments, the performance of the proposed method is evaluated and is compared with the ones of some other available methods for both weak and strong autocorrelations. Moreover, the diagnostic performance of the proposed method is evaluated. The results show that the proposed scheme performs well and works better than the competing methods. The application of the proposed method is illustrated at the end using a numerical example. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated. [ABSTRACT FROM AUTHOR]

Abstract: The global market has become increasingly dynamic, unpredictable and customer-driven. This has led to rising rates of new product introduction and turbulent demand patterns across product mixes. As a result, manufacturing enterprises were facing mounting challenges to be agile and responsive to cope with market changes, so as to achieve the competitiveness of producing and delivering products to the market timely and cost-effectively. This paper introduces a currency-based iterative agent bidding mechanism to effectively and cost-efficiently integrate the activities associated with production planning and control, so as to achieve an optimised process plan and schedule. The aim is to enhance the agility of manufacturing systems to accommodate dynamic changes in the market and production. The iterative bidding mechanism is executed based on currency-like metrics; each operation to be performed is assigned with a virtual currency value and agents bid for the operation if they make a virtual profit based on this value. These currency values are optimised iteratively and so does the bidding process based on new sets of values. This is aimed at obtaining better and better production plans, leading to near-optimality. A genetic algorithm is proposed to optimise the currency values at each iteration. In this paper, the implementation of the mechanism and the test case simulation results are also discussed. [Copyright &y& Elsevier]

Abstract: Manufacturing of advanced components like aeroengine parts is performed in a global network. Different manufacturers deliver individual components to the engine and even different manufacturing steps for a given component may be performed at different companies. Furthermore, quality is of utmost importance in this context. Simulations are increasingly used to assure the latter. The current paper describes the simulation of a chain of manufacturing processes for an aeroengine component. Different partners have performed the simulations of the different steps using a variety of finite element codes. The results are discussed in the paper and particularly the lessons learned regarding the modelling process. [Copyright &y& Elsevier]

Abstract: A flexible flow shop is a generalized flow shop with multiple machines in some stages. This system is fairly common in flexible manufacturing and in process industry. In most practical environments, scheduling is an ongoing reactive process where the presence of real time information continually forces reconsideration of pre-established schedules. This paper studies a flexible flow shop system considering non-deterministic and dynamic arrival of jobs and also sequence dependent setup times. The problem objective is to determine a schedule that minimizes average tardiness of jobs. Since the problem class is NP-hard, a novel dispatching rule and hybrid genetic algorithm have been developed to solve the problem approximately. Moreover, a discrete event simulation model of the problem is developed for the purpose of experimentation. The most commonly used dispatching rules from the literature and two new methods presented in this paper are incorporated in the simulation model. Simulation experiments have been conducted under various experimental conditions characterized by factors such as shop utilization, setup time level and number of stages. The results indicate that methods proposed in this study are much better than the traditional dispatching rules. [Copyright &y& Elsevier]

This paper proposes a study of a milling process planning to determine the tolerancing for manufacturing using statistical tool. The deviation between the machined surface and the surface corresponding to the nominal one due to combined errors is expressed in terms of the small displacement torsor parameters. These parameters are considered as random variables, displacements are expresses as variances. Experimentation is realised on a set of manufactured parts which consists of measuring various defects on machine tool. The experimental results and the simulation results are compared in this paper. [ABSTRACT FROM AUTHOR]

In complex systems like Large-ScaleDistributed Systems (LSDSs) the optimization of resource control is an open issue. The large number of resources and multicriteria optimization requirements make the optimization problem a complex one. The importance of resource control increases with the need of use for industrial process and manufacturing, being a key solution for QoS assuring. This paper presents different solutions for multiobjective decentralized control models for tasks assignment in LSDS. The transaction in real-time complex system is modeled in simulation by tasks which will be scheduled and executed in a distributed system, so a set of specifications and requirements are known. The paper presents a critical analysis of existing solutions and focuses on a genetic-based algorithm for optimization. The contribution of the algorithm is the fitness function that includes multiobjective criteria for optimization in different way. Several experimental scenarios, modeled using simulation, were considered to offer a support for analysis of near-optimal solution for resource selection. [ABSTRACT FROM AUTHOR]

In spite of the development of automated tolerance inspection systems for manufactured parts over the years, there are still processes that inevitably require manual intervention making full automation impossible in most cases; in particular when dealing with deformable parts. In most current industrial inspection systems, a deformable part under inspection must first be mechanically constrained on a rigid support or jig so as to be able to compare it with its nominal shape. This paper presents a new system to perform real-time surface inspection of deformable parts that does not require fixturing. Instead, the proposed system applies virtual forces to the part's CAD model as if the part was installed in the fixturing device. Normally, a precise finite element method (FEM) simulation should be used to approximate the deformation that appends when the part is installed in the device. Even with a fast parallel computer, FEM is far from being real-time and cannot be used for on-line inspection. In the proposed system, a radial basis function approximation of the FEM simulation is trained off-line and used to speed-up the simulation by an order of magnitude. Experimental evaluation of the proposed system is presented for three plastic parts. Using the proposed scheme, an approximation of 0.25 mm compared with the real deformation was obtained. In this paper, statistical results are presented such as the average deviation, standard deviation, and processing time between the approximations obtained with the proposed method and with the finite element method applied to the full CAD model. [ABSTRACT FROM AUTHOR]

This paper is focused on a combined deep drawing and extrusion process dedicated to the new process class of sheet bulk metal forming (SBMF). Exemplified by the forming of gearings, combined sheet and bulk forming operations are applied to sheet metal in order to form local functional features through an intended and controlled change of the sheet thickness. For investigations on the form filling and the identification of significant influencing factors on the material flow, a FE simulation model has been built. The FE model is validated by the results of manufacturing experiments using DC04 with a thickness of 2.0 mm as blank material. Due to the fact that the workpiece is in extensive contact to the tool surface and that the pressure reaches locally up to 2500 MPa, the tribological conditions are a determining factor of the process. Thus, their influence is discussed in detail in this paper. In the first instance, different fictional zones having a distinct effect on the resulting material flow are identified and their effect on improved form filling is demonstrated. Subsequently, a more comprehensive methodology is developed to define tribological zones of forming tools. For this, a system analysis of the digital mock-up of the forming process is performed. Besides friction, other relevant aspects of forming tool tri-bology like contact pressure, sliding velocity, and surface magnification are considered. The gathered information is employed to partition the tools into tribological zones. This is done by systematically intersecting and re-merging zones identified for each of the criterion. The so-called load-scanning test allows the investigation of the friction coefficient in dependence of the contact pressure and possible loading limits of tribological pairings. It provides an appropriate tribological model test to evaluate tribological measures like coatings, surface textures and lubricants with respect to their targeted application in particular zones. The obtained results can be employed in the layout of further forming processes to reach the desired process behavior. This can be, for example, an improved form filling, less abrasive wear and adhesive damage or lower forming forces, respectively tool load for an improved durability of the die. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of classical cellular manufacturing systems compared to other two systems proposed in literature: fractal cells and remainder cells. This paper proposes three strategies to control a cellular manufacturing system with remainder cell and how to configure and control a fractal manufacturing system. The performance measures of the manufacturing systems are analyzed when several unforeseen events occur as: machine breakdowns, production mix changes, demand fluctuations, and processing time variability. A simulation environment developed by Arena® package was used to investigate the three manufacturing system configurations. The performance measures investigated are: throughput, throughput times of the parts, work in process, manufacturing utilization, and due date performance (tardiness). The simulation results show how the fractal and remainder cells can be a valid alternative to cellular manufacturing systems in a very dynamic environment. [ABSTRACT FROM AUTHOR]

Abstract: A virtual enterprise (VE) is an organization intended to cope with the rapidly changing manufacturing environment. Organization building is important in virtual domains because it has largely been affecting the success of VEs. However, the process of forming a VE is based on self-determination by the participants. This paper adopts a bargaining model under a scenario of incomplete information to formalize the formation process, considers the characteristics of the VE formation process, presents the pricing strategies for the corresponding bargaining, and verifies the correctness and validity of the pricing strategies using computer simulation. This paper breaks through the relative research that compares the formation process with partner selection from the core enterprise’s perspective and also provides the basis for the intelligent information platform of VE, whose key part is pricing software. [Copyright &y& Elsevier]

Since testing wide range of management decision in real-world production is absolutely impossible, discrete-event simulation has often been adopted to evaluate the manufacturing system performance. Performance of various layout alternatives can be studied using simulation. In this paper there are presented reasons to use simulation in order to evaluate manufacturing system performance. Paper presents a simulation approach to assessing the impact of management decision on a production process and the associated impact on costs and on potential gain. [ABSTRACT FROM AUTHOR]

Process variations increasingly challenge the manufacturability of advanced devices and the yield of integrated circuits. Technology computer-aided design (TCAD) has the potential to make key contributions to minimize this problem, by assessing the impact of certain variations on the device, circuit, and system. In this way, TCAD can provide the information necessary to decide on investments in the processing level or the adoption of a more variation tolerant process flow, device architecture, or design on circuit or chip level. In this first of two consecutive papers, sources of process variations and the state of the art of related simulation tools are reviewed. An approach for hierarchical simulation of process variations including their correlations is presented. The second paper, also published in this issue, presents examples of simulation results obtained with this methodology. [ABSTRACT FROM AUTHOR]

Abstract: Since the semiconductor manufacturing system is a large-scale complex system, it is difficult to solve complex problems in semiconductor manufacturing by the mathematical modeling method. This paper presents a multi-agent-based distributed simulation platform to support the extremely complex semiconductor manufacturing analysis. A multi-agent-based distributed simulation platform framework and a multi-agent collaborative control model are proposed to provide a flexible infrastructure and a multi-agent coordination mechanism in distributed environment for semiconductor manufacturing simulation. A multi-agent time synchronization model for distributed simulation is designed to keep events in the correct logical time order in simulation and steps of time synchronization are given. An interaction model and message formats are presented to describe how agents communicate with each other in simulation. The platform development and the design of graphical user interface are also exploited in this paper. Finally, evaluation of this multi-agent-based platform was illustrated with a case study. It indicates that the platform is effective in modeling and simulating the complex semiconductor manufacturing and provides the insights about how to improve the semiconductor manufacturing process with well targeted measures. [ABSTRACT FROM AUTHOR]

Java Agent DEvelopment framework (JADE™) is a leading platform for the development of agent-based systems that are complaint with Foundation for Intelligent Physical Agents specifications. Due to the complexity, concurrency, and dynamic nature of manufacturing, it has been an important application of agent-based systems. Application of multi-agent concept in simulation leads to the agent-based simulation. Modeling the elements of manufacturing system (such as part, machine, and AGV) in reactive agent architecture is a better way of modeling for achieving discrete-event agent-based simulation. This paper focuses on modeling of different agents in manufacturing domain on JADE reactive architecture. Modeling of different agents on a shop floor in JADE reactive architecture led to the development of a simulator known as an agent-based shop floor simulator (ABSFSim). In the modeling process, different agents in the manufacturing domain have been identified by physical and functional decomposition. Internal architecture of individual agents is finalized based on their behavioral requirements. Modeling of the agents is an important development step of ABSFSim. A randomly generated sample manufacturing system has been used for testing and demonstration of ABSFSim. The modeling details provided in this paper are useful for development of agent-based systems in manufacturing domain as well as other discrete systems. [ABSTRACT FROM AUTHOR]

Abstract: This paper investigates the technical and economic potential of energy-intensive industries to provide demand-side management (DSM) in electricity and balancing markets through 2030. Increasing shares of renewables will lead to a rising demand for ancillary services at the same time that less conventional plants will be available to provide these services. This paper makes projections on the extent to which DSM from industrial processes can provide economic benefits in electricity markets with renewables by providing tertiary reserve capacity. Different industrial processes and their specific technical and economic properties are investigated and compared with other storage devices and electricity generation technologies. Based on an extension of an existing European electricity market model, simulations are used here to make long-term forecasts for market prices, dispatch and investments in the electricity markets through linear optimization. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a multi-agent architecture based on the actors computational model, for the distributed simulation of discrete event systems whose entities have a complex dynamic behaviour. Complexity is dealt with by exploiting statechart-based actors which constitute the basic building blocks of a model. Actors are lightweight reactive autonomous agents that communicate to one another by asynchronous message passing. The thread-less character of actors saves memory space and fosters efficient execution. The behaviour of actors is specified through “distilled statecharts” that enable hierarchical and modular specifications. Distributed simulation is achieved by partitioning a system model among a set of logical processes (theatres). Timing management and inter-theatre communications rest in a case on the High Level Architecture services. The paper illustrates the practical application of the proposed modelling and simulation methodology by specifying and analysing a complex manufacturing system. [Copyright &y& Elsevier]

Purpose - The purpose of this paper is to propose and assess the possibility of using ceramic coating layer as a heat-resistant material in micro solid propellant thruster. Design/methodology/approach - A ceramic layer is coated on the inner surface of the combustor of a micro thruster by applying micro-arc oxidation (MAO) technology. The thermal property of the coating is analyzed with laser pulse method. To evaluate the heat-resistant performance of the coating, the temperature history of the micro thruster inner surface is experimentally tested and recorded in a water thermostat bath. A numerical simulation of the thruster working condition is also carried out. Findings - Both experimental and simulation results reveal that the heat-resistant ability of the coating processed by MAO is proven to be effective. Practical implications - This paper attempts to help designers choose material processing technology to improve micro solid propellant thruster heat-resistant ability. Originality/value - The paper shows that with a ceramic coating layer processed with MAO, aluminium can sustain higher temperature and it can be used as the structural material for short-time-work micro thruster. [ABSTRACT FROM AUTHOR]

Lean manufacturing appears to hold considerable promise for addressing a range of simultaneous, competitive demands including high levels of process and product quality, low cost and reductions in lead times. This research addresses the application of lean manufacturing concepts to the continuous production sector with a focus on the motor manufacturing industry. The goal of this research is to investigate how lean manufacturing tools can be adapted from the discrete to the continuous manufacturing environment. This paper presents lean manufacturing as a leading manufacturing paradigm applied in many sectors. The fundamental focus on lean production is the systematic elimination of non-value added activity and waste from the production process. The implementation of lean principles and methods results in improved system and surrounding performance. Value stream mapping is used to first map the current state used to identify sources of waste and to identify lean tools to eliminate this waste. The future state map is then developed for a system with lean tools applied to it. To quantify the benefits gained from using lean tools and techniques in the value stream mapping, a detailed simulation model is developed and a designed experiment is used to analyze the outputs of the simulation model for different lean configurations. This paper demonstrates the implementation of lean philosophy through layout modification. [ABSTRACT FROM AUTHOR]

The characteristic of layered manufacturing is the incremental build-up which inevitably leads to the staircase effect. One method to reduce this effect is to optimise part orientation and to apply adaptive slicing procedures. Although layer thickness will be decreased in some regions, process efficiency can be kept to a reasonable level. This paper presents different approaches of adaptive slicing for laser-based applications in consideration of limited range of thickness variation to ensure the mechanical part properties. In contradiction to previous work, the number of applied layer thicknesses is limited due to the associated process parameters. In order to optimise the slicing process and to obtain the best results, this paper presents a practicable approach based on existing work to simulate adaptive slicing. To underline the potential of non-uniform slicing, illustrative examples will show possible improvements in the case of replacing the uniform slicing procedure. [ABSTRACT FROM AUTHOR]

The role of flexibility in manufacturing is becoming most important as the pressures due to variety of products are continuously increasing coupled with declining volumes and faster response to customers. This paper deals with implementation of manufacturing flexibility at the shop floor level of the industry under consideration. The main objective of this paper is to compare the performance of existing manufacturing system with that of flexibility based manufacturing system. To achieve the objectives simple demo simulation model of the existing and proposed manufacturing system is built and performance compared. Both these system are compared on the following performance measures i.e., total production and total production time. The introduction of flexibility causes a decrease in makespan time, with maximum reduction in the makespan time occurring when routing flexibility is increased from 0 to 1. Also total production of parts increases with the increase in the level of flexibility. [ABSTRACT FROM AUTHOR]