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The paper describes the state-of-the-art in modeling and simulation of surface texture and topography generation at micro and nano dimensional scales. Three main classes of manufacturing processes used for the generation of engineering surfaces are considered: material removal processes, material conservative processes, and material additive processes. Types of modeling techniques for the simulation of surface generation are reviewed and discussed including analytical models, numerical multi-physics models, and data-driven methods. After presenting the application of those modeling techniques for the prediction of characteristics and geometry of surfaces generated by different manufacturing processes, their performance, implementation, and accuracy are discussed. Finally, a roadmap for the realization of a complete surface generation digital twin in manufacturing is outlined. [ABSTRACT FROM AUTHOR]

The paper is devoted to optimization of technological processes of a single and small-scale production at the stage of technological preparation of production. Simulation model of a technological process and solution of a multi-criteria optimization problem are proposed to obtain optimal working process of a production site in terms of time and cost based on initial data, conditions and restrictions. Software implementation of the developed approach was tested in simulation of technological preparation of production of a "Pusher" part. [ABSTRACT FROM AUTHOR]

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]

This paper presents the first proof of concept validation of the STAR thruster prototype. The device contains an innovative multifunctional monolithic heat exchanger, enabled by metal additive manufacturing processes. A 316L stainless steel printed thruster is characterized through a combination of dry heating and wet firing tests. This includes verification testing with argon in both cold and hot firing mode, at a range of electrical power inputs. Thrust measurements range from 9.7 mN ± 0.16 mN–29.8 mN ± 0.16 mN, with a maximum measured specific impulse of 80.11 ± 1.49 s. Thrust performance is measured using a high-precision balance, and liquid-metal power transfer terminals to eliminate thermal drift. Highly coupled multiphysics computational models provide validation of the electro-thermal and thermo-fluidic characteristics of the prototype, including a prediction of the maximum propellant stagnation temperature and structural temperature, which were 649 °C and 854 °C. • Proof of concept validation of the Super-high Temperature Additive Resistojet. • SLM enabled a thin-wall concentric monolithic heat exchanger and resistive heater in 316L. • The prototype produced a specific impulse of 80.11 ± 1.49 s with Ar propellant. • Simulations suggest that the maximum structural temperature was of 854 °C. [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]

Abstract: This paper reviews the history of centerless grinding and its contribution to industry. It summarizes the evolution of centerless grinding theory including advanced modeling and simulation. Then, it discusses the design of main elements of a centerless grinding machine such as spindles, bed, guideways and positioning system, and provides design guidelines for future machines. The paper presents the state-of-the-art centerless grinding technologies: advanced machines, advanced process monitoring and the latest developments in grinding wheels. Finally, in conclusion, future trends and research work in centerless grinding technology are discussed. [Copyright &y& Elsevier]

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]

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 presents the application of the Discrete Event Simulation (DES) in an industrial plant to analyze the behavior of the consumption and generation of electrical energy in conjunction with other variables of the process. In other words, the process modeling was conducted considering the energy consumption in equipment as well as the local photovoltaic (PV) energy generation. A medium-sized plant was the reference base used herein, and the main variables considered were the time of supply of tanks and processing of raw material, active power of equipment and energy tariffs. A simulation model with resolution in seconds was designed, enabling the simulation of consumption curves of each equipment of a given part of the production process. The equipment of variable active demand was modeled by a mathematical regression method, and the others were assumed with constant power. The same model was altered to unit in minutes for simulating the monthly electricity consumption, so as to obtain financial results. The contractual rules for electricity supply were included, and the use of a diesel-powered generator was taken into account. The application of a freeware was employed as a low cost alternative for small and medium-sized companies, even though the lack of graphic resources has increased the complexity of the system modeling process. The model with resolution in seconds proved to be efficient to simulate the consumption profile of a specific equipment that alters the power in a short-time period, such as the pumps. The model, on the other hand, in minutes allowed a more effective analysis in terms of finance, enabling the verification of alternative scenarios throughout a month. The monthly cost reduction perceived by considering self-generation in this model was about 36%. Detailed diagram of the main elements of modeling of a manufacturing process of an industry concentrating on the consumption and generation of electric energy. Image 1 • The electricity consumption in an industrial process can be approached in a simulation model. • PV energy can be simulated together with variables of industrial processes. • The discrete event simulation can be used in industrial processes to optimize the electricity management. [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]

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]

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]

Abstract Models of distributed manufacturing systems cannot be consistent without a formal ontology. In this paper, the ontology formulation and maintenance are addressed in the scope of a collaborative modelling environment – in which concurrency, consistency, and model life cycle management should be supported. Thus, an extensible foundational ontology for manufacturing – system modelling is proposed in which the formal definitions of the modelling environment itself enable the definition of the manufacturing system's elements. The presented approach ensures the consistency of ever-changing models. The ontology is integrated into a modelling framework through the concept of description layers that assist in the management of the model description's complexity. The feasibility of the approaches is illustrated in an industrial case study that models of a manufacturing system for material processing. [ABSTRACT FROM AUTHOR]

Data reconciliation is a crucial technique to improve the accuracy of the measured data in industrial processes. However, most traditional data reconciliation researches mainly focused on global modeling for single mode processes, but little attention was paid to multimode processes. In this paper, a layered online data reconciliation strategy based on Gaussian mixture model is proposed for complex industrial processes with multiple modes. In the proposed data reconciliation framework, Gaussian mixture model is first used to identify and partition different operating modes from process data. Then, layered data reconciliation models are established for each operating mode. In the online data reconciliation step for new data, it is reconciled with the trained reconciliation models from different modes and its posteriors corresponding to different modes are calculated for new data. Finally, the reconciled result is obtained by the weighted sum of individual reconciled data in each operating mode. The effectiveness and feasibility of the proposed data reconciliation strategy are validated through a real industrial application on the sodium aluminate solution evaporation process. [ABSTRACT FROM AUTHOR]

Business process simulation (BPS) enables detailed analysis of resource allocation schemes prior to actually deploying and executing the processes. Although BPS has been widely researched in recent years, less attention has been devoted to intelligent optimization of resource allocation in business processes by exploiting simulation outputs. This paper endeavors to combine the power of a genetic algorithm (GA) in finding optimum resource allocation scheme and the benefits of the process simulation. Although GA has been successfully used for finding optimal resource allocation schemes in manufacturing processes, in this previous work the design of these algorithms is ad hoc, meaning that the chromosomes, crossover and selection operators, and fitness functions need to be manually tailored for each problem. In this research, we pioneer to design and implement a Petri Nets based Generic Genetic Algorithm (GGA) framework that can be used to optimize any given business processes which are modeled in Color Petri Nets (CPN). Specifically, the proposed GGA framework is capable of producing an optimized resource allocation scheme for any CPN process model, its task execution times, and the constraints on available resources. The effectiveness of the proposed framework was evaluated on archive management workflow at Macau Historical Archives and an insurance claim workflow from an Australian insurance company. In both case studies, the framework identified significantly improved resource allocation scheme relative to the one that existed when the data for the case studies were collected. [ABSTRACT FROM AUTHOR]

Abstract: The dynamic behaviour of production systems is explained to a great extent by the time varying flows of parts entering, accumulating, and leaving workstation buffers. In this paper a continuous flow formulation to model such dynamics is analysed, using the law of conservation of mass as the main governing equation. The paper shows how integral and differential mass balances relate to important production planning and control variables. The dynamic flow based model is then used in a simulation to investigate how different control strategies affect some key performance measures in the context of time varying total production capacity with local disturbances. [Copyright &y& Elsevier]

Abstract: This paper presents a novel mobile parallel robot, which is able to carry welding and machining processes from inside the international thermonuclear experimental reactor (ITER) vacuum vessel (VV). The kinematics design of the robot has been optimized for ITER access. To improve the accuracy of the parallel robot, the errors caused by the stiffness and manufacture process have to be compensated or limited to a minimum value. In this paper kinematics errors and stiffness modeling are given. The simulation results are presented. [Copyright &y& Elsevier]

Abstract: This paper presents a new approach for the determination of cutting-force coefficients. The proposed methodology models a practical mechanism for collecting and analysing experimental data. The surface error is the measured parameter used for defining the cutting-force coefficients. The cutting tool deviation resulting from the cutting forces is considered as the main element of the surface error. The proposed mechanism consists of cutting two strips with the same cutting conditions but the axial depth of cutting differs with the height of the elemental axial disk. Hence, the resultant surface error represents the deviation of the cutter while cutting the strip with axial depth equal to the cutter elemental axial disk. The size effect has been taken into account when generating the functions for the cutting-force coefficients. Milling experiments have been conducted to verify the methodology and are reported in the paper. The proposed method overcomes the need to analyse and recalculate the instantaneous chip thickness, which is associated with the experimentally measured cutting forces traditionally used in the process of defining the cutting-force coefficients. The new approach provides the simulation process with instantaneous cutting-force coefficients, which are independent of the cutting operation geometry. The results when applying the new cutting-force coefficients show a good agreement between the predicted and measured surface error. [Copyright &y& Elsevier]

Abstract: This paper presents a systematic scheme for developing feasible tool-paths for machining a sculptured surface on a multi-axis NC machine. In our previous paper (in CAD journal), a simplified problem of CT-dependent partitioning of a sculptured surface is solved with application of the focal surfaces. The derived solution to the problem is convenient for particular cases when the angle of the surfaces and local relative orientation is identical to zero (). In general case of sculptured surface machining on multi-axis NC machine, the identity often doesn’t observe. Here this problem is enhanced up to cases when the actual value of the angle could be of arbitrary value. The reported approach is based on application of the characteristic surfaces of novel kind. The characteristic surfaces of the newly discovered kind are referred to as the characteristic -surfaces. The reported solution to the problem is based on implementation of the DG/K-method of surface generation. The methodology presented here is a general one. It combines the activities of exploration of topology of a given sculptured surface, of the generating surfaces of the cutting tool, of the cutting tool configuration and of the cutting tool motion that it performs in the machining operation relative to the work, thus making the production process flexible, automatic and controllable. The approach is the CAD/CAM orientated one. The effectiveness of the proposed approach is verified from numerical simulation. Perfect correlation of the reported results with those previously published by the author, as well as by other researchers, is observed. [Copyright &y& Elsevier]

Abstract: This paper deals with multi-state systems (MSS), whose performance can settle on different levels, e.g. 100%, 80%, 50% of the nominal capacity, depending on the operative conditions of the constitutive multi-state elements. Examples are manufacturing, production, power generation and gas and oil transportation systems. Often in practice, MSS are such that operational dependencies exist between the system state and the state of its components. For example, in a production line of nodal series structure, with no buffers between the nodes, if one of the nodes throughput changes (e.g. switches from 100% to 50% due to a deterministic or stochastic transition of one of its components), the other nodes must be reconfigured (i.e. their components must deterministically change their states) so as to provide the same throughput. In this paper, we present a Monte Carlo simulation technique which allows modelling the complex dynamics of multi-state components subject to operational dependencies with the system overall state. A correlation method is tailored to model the automatic change of state of the relevant components following a change in one of the system nodes. The proposed technique is verified on a simple case study of literature. [Copyright &y& Elsevier]

Abstract: In comparison with machining of worm on a lathe or on a milling machine, whirling process uses a rotating tool ring carrying tools and encompassing the work blank. Due to nature of contact between tool and work blank, simulation of whirling process is necessary to understand the geometry of worm surface generated. In this paper, an attempt is made to simulate the whirling process and analyze the effect of tilting of tool head. In some applications worm geometry is specified and tool geometry has to be arrived at. An approach for profiling of tool is explained in this paper and validation of tool profile is carried out by simulation. Two worms, namely straight-sided in axial section and involute helicoid are taken as case studies and results are discussed. [Copyright &y& Elsevier]

This paper presents a swept envelope approach to determining tool position for five-axis ruled surface machining. The initial tool position is traditionally located to contact with two directrices of a ruled surface. The swept profile of the tool is then determined based on the tool motion. By comparing the swept profile with the ruled surface, the tool position is corrected to avoid machining errors. The cutter''s swept envelope is further constructed by integrating the intermediate swept profiles, and applied to NC simulation and verification. This paper presents the explicit solution for the swept profile of a taper-end cutter in five-axis ruled surface machining. The relation of the ruled surface geometry, the tool motion and the machining errors is developed. Therefore, the error sources can be detected early and prevented during tool path planning. The explicit swept envelope indicates that the machined surface is not a ruled surface in five-axis ruled surface machining. Manufacturing industries should take extra care in high precision ruled surface machining. Computer illustrations and example demonstrations are shown in this paper. The results reveal that the developed method can accurately position tool location and reduce machining errors for five-axis ruled surface machining. [Copyright &y& Elsevier]

In this paper, a graphics hardware-assisted approach to 5-axis surface machining is presented that builds upon a tool positioning strategy named the Rolling Ball Method presented in an earlier paper by the present authors [Comput. Aided Des. 35 (2003) 347]. The depth buffer of the computer''s graphics card is used to compute the data needed for the Rolling Ball Method, which generates gouge-free 5-axis curvature-matched tool positions. With this approach, the tool path for a workpiece can be computed with triangulated data instead of parametric surface equations. It also permits the generation of tool paths for multiple surface patch workpieces that have only positional continuity. The method is easy to implement and it is robust since every tool position is computed with the same algorithm regardless of the type of surface. For illustration, tool paths were generated for a workpiece with two bi-cubic surface patches, connected with only position continuity. Simulations for gouge-checking and machining tests were performed. Workpiece cusp heights were measured using a coordinate measuring machine. The maximum undercutting measured in the machining examples was 0.07 and 0.05 mm, which is within the expected NC machine accuracy and measuring capabilities for surfaces. [Copyright &y& Elsevier]

The major purpose of this paper is to assure quality in mass production by using improved robust design. The essential activity in quality assurance is to achieve high quality (Cpk≥1.33). The reduction of dispersion and the reduction of bias are indispensable for the achievement of this high quality. This paper discusses the way to improve the robust design based on regression analysis for high quality, low cost and high productivity. Simulative experiments and simulative manufacturing using paper helicopters are used as concrete examples, and an education program is introduced in order to explain this approach in detail. [Copyright &y& Elsevier]

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]

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]

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]

When designing production lines, a series of complex, interdependent phases need to be considered. These phases include the facility layout design, the allocation of tasks to available resources, the workload balancing as well as the validation of the proposed design against demand. This paper addresses all these phases in an integrated way, with the aim of reducing the overall time required to finalise the layout and production line design, while improving the overall performance of the manufacturing system under process and demand uncertainty, utilising a sophisticated, simulation-based software framework. The proposed approach is demonstrated using a realistic case scenario. [ABSTRACT FROM AUTHOR]

Fibre-reinforced-thermoplastics (FRT) have excellent weight-specific properties compared to conventional engineering materials. However, a wider dissemination of this technology into existing plant technologies is restrained by the low degree of automation. Complex FRT component geometries pose special challenges to gripper design and handling strategies in automated preform processes regarding limp material behaviour and fast cooling time. The preform quality is influenced by the component geometry, reinforcing fabric, and preform process. This paper presents the development of an automated handling and draping strategy, which is validated by finite-element-analysis and experimental testing to meet the requirements of large-scale preforming processes for complex geometries. [ABSTRACT FROM AUTHOR]

Digitalization allows for production control based on the current state of the manufacturing system. Thereof, this paper proposes and applies a data-driven adaptive planning and control approach that uses simulation-based optimization to determine most suitable dispatching rules in real-time under varying conditions. The data integration between the real manufacturing system and the simulation model is implemented through a data-exchange framework. The approach is evaluated in a scenario of a Brazilian manufacturer of mechanical components for the automotive industry, achieving better operational performance compared to the procedure previously applied by the company as well as in comparison to static dispatching rules. [ABSTRACT FROM AUTHOR]

Abstract: The paper deals with numerical modeling and experimental testing of the clinched joint combined with an adhesive bonding. Two specimen manufacturing procedures were used to prepare overlap hybrid joint: adhesive curing before clinching, and clinching before adhesive curing. The strength of the joints was determined experimentally in the tensile test by pull and peel method. The experimental results were compared with numerical simulation results using ABAQUS code. The major conclusion of the paper is the following – clinching before adhesive curing is more effective technology for the joint strength. [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]

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]

Multi-objective constrained optimization problems which arise in many engineering fields often involve computationally expensive black-box model simulators of industrial processes which have to be solved with limited computational time budget, and hence limited number of simulator calls. This paper proposes two heuristic approaches aiming to build proxy problem models, solvable by computationally efficient optimization methods, in order to quickly provide a sufficiently accurate approximation of the Pareto front. The first approach builds a multi-objective mixed-integer linear programming (MO-MILP) surrogate model of the optimization problem relying on piece-wise linear approximations of objectives and constraints obtained through brute-force sensitivity computation. The second approach builds a multi-objective nonlinear programming (MO-NLP) surrogate model using curve fitting of objectives and constraints. In both approaches the desired number of approximated solutions of the Pareto front are generated by applying the ɛ -constraint method to the multi-objective surrogate problems. The proposed approaches are tested for the cost vs. life cycle assessment (LCA)-based environmental optimization of drinking water production plants. The results obtained with both approaches show that a good quality approximation of Pareto front can be obtained with a significantly smaller computational time than with a state-of-the-art metaheuristic algorithm. [ABSTRACT FROM AUTHOR]

Environmental sustainability is an everlasting concern of our modern society and has been gaining importance in practice and academia over the last decades. For being a major contributor to global environmental pollution, stakeholder pressure on manufacturing companies is increasing to reduce their environmental impacts, while simultaneously delivering high value goods and services at competitive prices, understood as increasing their eco-efficiency. However, complex interrelations between the economic and environmental performance dimensions in production systems create a non-transparency to decision makers, hindering them to translate this strategic target into improvement measures on a shop-floor level. Discrete-event simulation has been established as a promising method to solve this problem, although current approaches are insufficient for focusing only on energy aspects and being too complex in their application. This paper proposes a decision support consisting of two elements: 1) A novel discrete-event simulation approach that seamlessly integrates LCA with common economic evaluations by embedding the Ecoinvent LCI database, while considering all relevant eco-efficiency drivers within a production system; and 2) a system analysis and modelling procedure supporting its application and the identification of appropriate improvement measures. [ABSTRACT FROM AUTHOR]

The simulation of a manufacturing process chain with the finite element method requires the selection of an appropriate finite element solver, element type and mesh density for each process of the chain. When the simulation results of one step are needed in a subsequent one, they have to be interpolated and transferred to another model. This paper presents an in-core grid index that can be created on a mesh represented by a list of nodes/elements. Finite element data can thus be transferred across different models in a process chain by mapping nodes or elements in indexed meshes. For each nodal or integration point of the target mesh, the index on the source mesh is searched for a specific node or element satisfying certain conditions, based on the mapping method. The underlying space of an indexed mesh is decomposed into a grid of variable-sized cells. The index allows local searches to be performed in a small subset of the cells, instead of linear searches in the entire mesh which are computationally expensive. This work focuses on the implementation and computational efficiency of indexing, searching and mapping. An experimental evaluation on medium-sized meshes suggests that the combination of index creation and mapping using the index is much faster than mapping through sequential searches. [ABSTRACT FROM AUTHOR]

This paper considers the problem of optimizing on-line the production scheduling of a multiple-line production plant composed of parallel equivalent machines which can operate at different speeds corresponding to different energy demands. The transportation lines may differ in length and the energy required to move the part to be processed along them is suitably considered in the computation of the overall energy consumption. The optimal control actions are recursively computed with Model Predictive Control aiming to limit the total energy consumption and maximize the overall production. Simulation results are reported to witness the potentialities of the approach in different scenarios. [ABSTRACT FROM AUTHOR]

The paper presents the application of a Technical Mapping and tacit knowledge elicitation in industry in order to promote the modeling of tacit knowledge to explicit and represent it in the form of production rules for use in manufacturing processes. The technique was applied with the involved people in the lithographic process in a Metallurgical Company located in southern Brazil. Knowledge of two production coordinators were modeled. For the process of knowledge acquisition and mapping of attributes and values to feed the knowledge base of an expert system, were used quality tools such as Brainstorming, Pareto Chart and Ishikawa Diagram associated with knowledge elicitation techniques such as unstructured interview, rating chips, observation technique, limitation of information and protocol analysis. Quality tools and techniques of knowledge elicitation were systematized to promote process mapping and the elicitation of tacit knowledge, with the aim of representing knowledge by means of production rules. We constructed two knowledge bases with the same methods of production, one in a non-probabilistic expert system (knowledge-based system) and the other in a probabilistic expert system (Bayesian networks) in order to perform comparisons and simulations of the results found. Expert systems perform systematic analysis from the answers given by those involved in lithographic labels process while the defect is identified in order to support the user in diagnosing the root cause of the failure process. From simulations of changes in process variables was possible to prove the hypothesis of the use of probabilistic expert system as industrial support tool in preventing the occurrence of defects in the process and result in a productivity gain. [ABSTRACT FROM AUTHOR]

Efficiency of manufacturing processes is very important in today's competitive world with hard economic rules. In chemical engineering area the efficiency depends on the production heart, which is often a chemical reactor. In this paper authors describe process of optimal semi-batch exothermic reactor dimensions finding. The task is to find reactor dimensions which lead to process efficiency improving, i.e. to processing the greater chemicals amount in the same or shorter time. The optimizing process uses an objective function which includes also the reactor mathematical model. The objective function is modified here to find better results and necessary limitations and penalizations are used to improve the dimensions searching. An evolutionary approach represented by the Self-Organizing Migrating Algorithm (SOMA) was used to minimize the defined function. A suitability of the reactor with newly found dimensions was then verified by the process control simulations. [ABSTRACT FROM AUTHOR]

Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems. [ABSTRACT FROM AUTHOR]

Abstract: This study addressed the issue of automated material handling systems (AMHS) in the photolithography zone of a 300mm (12-in.) wafer fab facility. The lithography process accounts for 40–50% of the time required to produce wafers. Therefore, managing the AMHS in the photolithography zone is a challenging task. This paper examines the dispatching rule and the number of vehicles in variable wafer input cases. With a stochastic and complex manufacturing process, a photobay simulation may lead to excessive iterations and wasted computation time. The most frequently used approach for process management in the literature is performance analysis with a model that simulates each alternative for N times. However, this approach becomes time consuming as the number of variables and iterations increases. To address this issue, we use Optimal Computing Budget Allocation (OCBA) and extend OCBA by adding particle swarm optimization (PSO). With this combined approached, the number of iterations of each alternative is determined by OCBA, and the optimal solution in the domain of feasible solutions is identified through PSO. This research provides a useful reference to optimally allocate lithographical resources and the number of iterations with random parameters for both scholars and practitioners. Results demonstrate the superiority of PSOOCBA in terms of searching quality and robustness. [Copyright &y& Elsevier]

Electrodialysis metathesis is an attractive technology to synthesize electrolyte by electrodialysis technology. It could realize the metathesis reaction and product concentration simultaneously in one process. A semi-empirical mathematical model based on Nernst–Planck equations was built to describe the continuous EDM process converting Na 2 SO 4 to K 2 SO 4. The parameters of mathematical model were determined by the experimental data. The simulated results were verified by the laboratory scale test and a pilot-scale experiment. The economic feasibility of an industrial scale process was evaluated. It was indicated that the current density had a significant effect on the capital cost of EDM, while the effect of feed concentration was small. The specific cost of EDM was lowest when the current density was 150 A·m−2 in this paper. [Display omitted] • A semi-empirical model for continuous EDM process was developed and applied to the simulation of industrial process. • The equation of water flux from different dilute solution was determined by the ion hydration numbers. • The capital cost of membrane occupied about 50% of the total ones. • The capital cost was reduced as the current density was increased, and variated little as the concentration was increased. [ABSTRACT FROM AUTHOR]

Abstract: Sustainable manufacturing is a new paradigm in which manufacturing industries produce products in a sustainable manner while maintaining global competitiveness and coping with recent challenges and problems. Sustainable manufacturing research is very broad in scope and has not yet been firmly established at the academic or practical levels. This paper introduces MAS2, which is an integrated modeling and simulation-based life cycle evaluation approach for sustainable manufacturing. Four requirements that are essential for evaluating the sustainability performance of manufacturing industries are suggested. The proposed MAS2 approach consists of the following main components: 1) a theoretical foundation comprising 20 principles and definitions of sustainable manufacturing and manufacturing sustainability, 2) indicators, namely the MAS2 indicator and Manufacturing Sustainability Index (MSI), 3) e-MAS2 as an evaluation method, and 4) i-MAS2 as an information management method. The MAS2 system is developed based on the proposed approach, and consists of two main modules: MS-Modeler and MS-Simulator. The MAS2 approach and system are validated by application to the product life cycle of an automotive stamping shop. [Copyright &y& Elsevier]

Abstract: There is increasing evidence that more and more companies throughout the world are seeking measures to reduce their production carbon emissions and product carbon footprint. Simulation tools can be used to characterize the carbon emission dynamics of manufacturing system to help companies clearly developing the potential reduction strategies of production carbon emissions. On the basis of the carbon emissions dynamics described by general state equations of manufacturing system as a hybrid system, a simulation approach based on hybrid Petri-nets (HPN) is proposed to display the carbon emission dynamics. In order to estimate the total carbon emissions of the whole process chain, the carbon emissions of production system are represented as segments of specific carbon emissions for each operation state of the individual production equipments in the approach. Furthermore, two function indicators deriving from general state equations, carbon emission rate and carbon efficiency, are defined to describe the dynamic characteristics. Production disturbance as one of the critical factors influencing the carbon emission performance of production system is also considered in this paper for demonstrating their impacts on the carbon emission dynamic characteristics. Finally, an experimental study is presented to illustrate validation of the proposed approach and shows its positional significance for carbon emission reduction. [Copyright &y& Elsevier]