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Research on collaborative manufacturing process modelling and simulation is of great significance in improving production efficiency and product quality, reducing costs, promoting collaborative work, and supporting decision-making. However, current studies have not offered the optimal costs of node enterprises participating in collaborative manufacturing, and ignored the impact of demand uncertainty of suppliers, manufacturers and customers on costs and economic benefits. Therefore, this paper aimed to study the collaborative manufacturing process modelling and simulation of production cost and economic benefit accounting analysis. First, this paper summarized three cost types, conducted cost accounting for the collaborative manufacturing process, and analysed its economic benefits. Second, based on the basic timed automata (TA) model, this paper used a timed input/output automata with port labels (Lp-TIOA) for modelling test cases of the simulation system. Finally, the experimental results verified the effectiveness of the constructed simulation model. [ABSTRACT FROM AUTHOR]

The critical role that numerical simulation plays in additive manufacturing has stimulated research on the effectiveness and potential applications of mesh-free, particle-based discretisation techniques. These methods excel at handling fluid flows and are viable alternatives to the mesh-based techniques typically used in commercial simulation software. In this paper, we review recent advances in developing computational models for metal additive manufacturing (MAM) processes using particle methods, in the theoretical understanding of the fundamental mechanisms that control such processes at the powder (or melt pool) scale, and in the predictability of physics-based modelling approaches. The paper explores the applicability and performance of particle-based methods in simulating powder bed fusion, directed energy deposition, and binder jetting processes. Since the progress of MAM relies on systematic material-process-structure realisations which are often impossible to sense or observe experimentally, developing efficient particle-based and multiscale simulation tools can be essential to achieving this objective through in-situ process control and optimisation. [ABSTRACT FROM AUTHOR]

With the introduction of new technologies, the scope of miniaturization has broadened. The conditions under which complicated products are designed, manufactured, and assembled ultimately influence how well they perform. The intricacy and crucial functionality of products are frequently only fulfilled through the use of high-precision components such as micro gears. In power transmission systems, gears are used in a variety of industries. Micro gears or gears with micro features, with tolerances of less than 5 μm, are pushing manufacturing processes to their technological limits. Monte-Carlo simulation methods enable an accurate forecast of inaccuracies in compliance. The complexity of the micro gear's design, on the other hand, increases the simulation computation and runtime. An alternative method for simulation is to create a surrogate model to predict the behavior. This paper proposes a statistical surrogate model to predict the conformity of a pair of micro gears. Afterward, the advantage of the surrogate model enables the optimal tolerance assignment while taking gear functionality and production cost into account. [ABSTRACT FROM AUTHOR]

The potential security problems of blockchain technology are constantly restricting the development process of related industrial applications. The cost of deploying a blockchain system in a real environment to conduct research on security issues is relatively high, and the related security analysis and verification are also destructive and irreproducible. Therefore, based on the idea of layered design, this paper proposes a blockchain system simulation platform. The blockchain system is divided into four layers in the simulation platform: the consensus layer, network layer, contract layer, and storage layer. In the consensus layer, the problem of computing resource waste is solved. In the network layer, a peer-to-peer network topology simulation is implemented. In the storage layer, the problem of redundant storage is solved. In the contract layer, the contract replay speed is accelerated. Finally, a prototype of an efficient blockchain simulation system is implemented based on the above methods. [ABSTRACT FROM AUTHOR]

Discrete Event Simulation (DES) is one of the most important simulation techniques in decision-making in several areas. Some authors state that small and large companies can benefit significantly through the utilisation of DES. Literature shows that most DES studies carried out are at large companies. Studies carried out at small and medium-sized enterprises (SMEs) have been mostly explored on a one-off, caseby-case basis, given the characteristics and limitations of smaller companies. In order to expand DES studies in a wide range of companies, this article seeks to consider the SME characteristics and limitations prevent them from broadly adopting traditional (“hard”) DES. Then an alternative application of DES in “soft” mode was proposed. A review of facilitated DES frameworks was carried out. The frameworks were critically analysed in this review in relation to their adequacy to specific SME requirements and similar contexts. Finally, some issues and characteristics of these frameworks are presented that make directly applying in these contexts difficult. Furthermore, some suggestions are proposed for developing future facilitated DES frameworks. (Received in February 2022, accepted in May 2022. This paper was with the authors 3 weeks for 1 revision.) [ABSTRACT FROM AUTHOR]

The rapid development of additive manufacturing technology (AM) is revolutionizing the traditional continuous fiber-reinforced polymer (CFRP) manufacturing process. The combination of FDM technology and CFRP technology gave birth to continuous fiber reinforced thermoplastic composites (CFRTPC) 3D printing technology. Parts with complex structure and excellent performance can be fabricated by this technology. However, the current research on CFRTPC printing mainly focuses on printing equipment, materials, and the improvement of mechanical properties. In this paper, the CFRTPC 3D printing track errors are investigated during the printing process, and it is found that the polytetrafluoroetylene (PTFE) tube in the nozzle of the printer head is often blocked. Through detailed analysis, a line-following mathematical model reflecting the deviations of the CFRTPC printing track is established. According to the characteristics of the fiber and its track during actual laying, a modified line-following model, without the minimum curvature point, is further proposed. Based on this model, the actual printing track for the theoretical path is simulated, the process tests are carried out on the printing track at different corner angles, and the relevant rules between the parameters of the model and different corner angles are obtained. The mathematical model is verified by experiments, and the clogging problem of the printer head caused by the fiber track error is solved, which provides theoretical support for the rational design of the fiber track in CFRTPC printing. [ABSTRACT FROM AUTHOR]

Manufacturing large and/or complex structural components is today non-trivial and far-too expensive, due to limitations in the state-of-the-art production processes. Swarm robotics could then bring a different perspective and it may promise flexible, autonomous, and highly robust solutions for a large variety of applications; hence, its adoption in industry and construction may change manufacturing rules. The present contribution introduces a continuous model to capture the behavior of a swarm of manufacturing agents (e.g., drones, 3D printers, etc..) as well as a very simple, but effective, numerical implementation to simulate the evolution of such a swarm. The paper also presents one- and two-dimensional examples, showing the potentiality of the proposed approach in predicting swarm behaviors. [ABSTRACT FROM AUTHOR]

Increasing competition and globalization that encompasses all dealings in the world of business compel SMEs to seek more successful organizational and management modes and approaches. This paper presents a simulation model for the application of Lean methods and tools in SMEs and their impact on the improvement of production processes and product sustainability. The Lean concept can be used to achieve more efficient production whilst manufacturing lower-cost products including shorter manufacturing cycle, high quality, and added value. Research results show the usefulness of the application of dynamic simulations in the detection of critical places in processes and the usefulness of the application of relevant Lean methods and Lean tools for process improvement. Simulation results underline better performance of product manufacturing through the use of Lean tools with the aim to eliminate any waste. Reengineering the assembly line focuses on the following aspects, namely reducing waste, internal logistics, redesigning workplaces, and changing the workplace layout. [ABSTRACT FROM AUTHOR]

A new finite element dynamic model of a moving yarn segment has been proposed in this paper based on the absolute nodal coordinate formulation (ANCF). Apart from taking into account the elastic properties of the yarn in three dimensions, the model also considers the viscosity in the longitudinal direction and takes into account the effect of gravity and air resistance. In this paper, the simulation described the movement of the yarn segment that is pulled by the fixer on the guideway. Then, a corresponding experiment was proposed to evaluate the theoretical model. The theoretical and experimental comparisons of the motion tracing exhibited good agreement, demonstrating that the new model could predict the actual moving trace of the yarn segment. Moreover, another simulation of the spatial motion of the yarn segment was presented, to elucidate the role of the model in predicting the movement of the yarn segment. After considering the parameters of the actual process and its constraints, the authors established that the proposed model could be used to predict the trajectory of a yarn segment in the actual production process, which is vital when fabricating textile products. [ABSTRACT FROM AUTHOR]

Investment decision dilemmas were since late 60's of previous century interesting for research from the perspective of financial dilemmas, while later, with arising uncertainty and dynamic of business environments, stochastic decisions problems needed more in-depth and complex considerations. High technology flexibility and multiple variation production process with significant losses related to resetting machine on one hand and great investment costs for new technology on the other hand, makes technology investment decisions in contemporary business environment multifarious. In this paper, we are presenting the case of optimizing investment decision dilemma in multiple variation production process. Using a discrete event simulation model we test two alternatives regarding the impact on predefined production indicators: 1) optimal grouping of the different products on an existing machine, 2) additional identical machine and optimal sorting of products between the existing and additional machine. In the paper, we determine indicators that should be monitored in investment decision dilemmas and design simulation model that can be used to calculate aforementioned indicators. [ABSTRACT FROM AUTHOR]

Production systems present a different complex consisting of interconnected sub-units. For their proper functioning, it is necessary to use approaches in the design process that will fully accept the interaction of individual units. A dynamic simulation is now a powerful tool for the need of designing production systems. However, in its application in practice, we often encounter various. This paper presents a framework for using dynamic simulation in production systems design. It describes suggested steps to take when we start the process of production systems design, mainly the process of data collection and simulation model building (current and future virtual systems). The article details framework usage and describes the most important challenges of each step. The proposed framework is then successfully applied to a real-life project of innovation of a production system in a pressing shop, where every step of the framework is documented with a working example and with described difficulties or outcomes that each step gave to the team. The result of the dynamic simulation application at the presented real example by the software Witness, the saving was 40.000 EUR. [ABSTRACT FROM AUTHOR]

The present paper aims to describe the development of a FEM model for the simulation of a die-sinking micro-EDM process. A commercial FEM code specific for the simulation of thermo-mechanical forming processes was used. The developed model was based on the capacity of the code to simulate the removal process or the separation of the material through the mesh element deletion. The electrical discharges were simulated through a proper heat flux and the material melting temperature was set as a trigger for the element deletion. The heat flux was related to the EDM process parameters by means of mathematical models selected from the literature. In the first part of the present work, a model based on a single discharge analysis was developed; then, the model was extended to a sink EDM process characterised by repeated discharges. The model was validated in terms of machining time, top diameter and MRR by the execution of experimental tests. [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]

This paper aims to provide an introduction to the analytical modelling and the discrete-events simulation for a manufacturing system. A general method of constructing a Markov chain and the simulation with discrete events using Delmia Quest software, are presented. In the present paper, those interested can find a real case study: modelling and simulation of the headrest support manufacturing line. The scientific contribution of this paper consists in the development of a theoretical model implemented in a real case study. The theoretical model estimates the production rate of the line and then, using this model we can establish how many parts in the buffers are required in order to achieve a production rate of X parts per minute. In the first phase, the manufacturing line is analysed, then the Markov chains for subsystems with two machines and a buffer are constructed. As the Markov chains need more time to provide solutions in queues theory, they can be used only for small production lines. To find the production rate for all the system, it is necessary to apply the Markov chains and the decomposition method. Those methods are implemented in the C/C++ programming. To validate the analytical model, the discrete-event simulation with Delmia Quest software is used. The article presents some comparisons between modelling with Markov chains and simulation with Delmia Quest software and provides a method of optimization of the buffers for the case study. [ABSTRACT FROM AUTHOR]

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]

2-D directed self-assembly (DSA) is an emerging lithography for the 5-nm process node and beyond that can substantially increase design flexibility in critical routing layers and reduce the number of cuts for better yield. The state-of-the-art 2-D DSA process manipulates the orientation of double posts inside guiding templates to guide block copolymers (BCPs) to form 2-D patterns. However, a key challenge arises on how to correctly assign double post orientations and place cut patterns to make desired net connections for a given routing instance. In this paper, we propose a novel 2-D DSA-compliant routing (2-D-DCR) framework to systematically derive feasible orientation assignments for double posts to maximize routability. Specifically, 2-D-DCR features a complete set of new routing rules which transform the underlying physical BCP growth principles for large-scale routing, adopts a network-flow-based double-post assignment routing algorithm, and leverages a 2-D DSA line-end creation property to maximally reduce line-end cuts. Experimental results show that our 2-D-DCR can effectively generate a 2-D-DCR solution with zero double post conflicts, maximized routability, and minimized the number of cuts. [ABSTRACT FROM AUTHOR]

5-axis hybrid additive-subtractive manufacturing is a new and promising technology for many industrial applications. Because of its complexity, the machining simulation is very important to verify the effectiveness of tool path. However, most simulation methods are proposed for subtractive manufacturing and additive manufacturing separately. This paper presents a novel geometric simulation technique for the 5-axis hybrid additive-subtractive manufacturing, which is based on Laser Engineered Net Shaping (LENS) and milling. In the proposed method, 5-axis additive swept volume elements are represented with Tri-dexel models and then converted into triangular meshes for visualization. Workpiece after additive manufacturing can be then used as a workblank for subtractive manufacturing simulation. Finally, a geometric simulation module is developed based on SIEMENS NX software to demonstrate the feasibility of the proposed simulation method. [ABSTRACT FROM AUTHOR]

As corner smoothing algorithm is crucial to high-speed CNC machining, a new error constraint optimization method for corner smoothing algorithm is proposed in this paper. Traditional corner smoothing algorithms are all based on a toolpath to generate motion trajectory, and the same allowable error is used for all smoothed corners. However, in using this method, the contour error of workpiece will be invalidated because of the cutting tool radius in real cutting processing, thereby resulting in over-cutting or inefficiency. In this study, the tool offset and corner smoothing algorithm are combined in order to optimize the error constraint. Besides, a cutting process geometry model was established to analyze the error relationship. On the basis of this model, the smoothing error constraints of each corner can be solved according to the allowable contour error of the workpiece. Finally, motion trajectory is planned through a multi-constraint planning strategy, which can balance the contour error in workpiece at each corner and can guarantee the cutting accuracy, compared with the traditional corner smoothing algorithm. Moreover, a test workpiece is designed to verify the correctness of the proposed algorithm through simulation and actual experiments. The experimental outcome proves that the proposed method improves the workpiece cutting accuracy in high-speed CNC machine tool. [ABSTRACT FROM AUTHOR]

In the computer numerical control (CNC) machining for free form surfaces, the time-optimized and smooth feedrate profile plays an essential role in the high-speed and high-precision machining. For the traditional feedrate scheduling method (e.g., the "bang-bang" control method), there may be many fluctuations in the planned feedrate profile, especially in region of large curvature, which results in poor quality of machining surface. To address this issue, a novel concept called sliding arc tube (SAT) is proposed in this paper, from which the trajectory is partitioned as feedrate restricted intervals (FRIs) determining the target feedrate and the positions of tool acceleration and deceleration on the tool path. The SAT is built based on the local geometric information of tool paths. As the SAT is constructed in the tube-shaping process and then sliding on the tool path and finally blocked by it, the breaking point for generating the FRI is found on the trajectory curve. For the proposed FRI identification method, it is computational efficient and it can be implemented in the real-time manner in the CNC controller. Results from both simulation and real machining experiments validate that, with the proposed SAT, the FRIs can be precisely and efficiently identified for the given trajectory curve, the frequent acceleration and deceleration of machine tool is alleviated, and the machining quality can be improved in this way. [ABSTRACT FROM AUTHOR]

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]

Due to the compliance and geometrical defects of composite parts, gaps exist between assembly components after the preassembly. Assembly requirements impose to fill these gaps to eliminate any unexpected internal stresses. Although it is identified as a problematic and expensive nonadded value stage, a gap measurement is still needed. This paper develops a numerical process for gap prediction before the assembly step. After the assembly components are scanned in a specified configuration, finite element meshes are created using the scanned data and the shape variations of each component caused by constraints and forces in different configurations are evaluated by finite element analysis. Assembly gaps are finally assessed by assembling the simulated preassembly shapes of all components. The feasibility of the proposed method is proved by an experiment. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to analyze the quality of ductile iron castings through simulations and experiments. Standard tensile test specimens are considered as simple cast products for which a multi-cavity mold is designed, simulated, and optimized to minimize porosity using MAGMASoft. X-ray imaging, hardness measurement, and tensile testing are done for selected specimens produced using optimized mold design. Next, finite element simulation of tensile testing until fracture is done in ABAQUS using elastic-plastic material model and porous metal plasticity model. Simulation results for sound specimen are found to be in good agreement with the experimental results. Since mold design optimization is solely based on porosity minimization, no porosity is observed in the final mold design. However, if multi-criteria optimization of mold is done, the specimens may show some porosity which can be integrated in the developed finite element model of tensile testing. It is concluded that simulation-based mold design optimization can produce nearly defect-free castings and at the same time exhibit the similar mechanical properties as their sound counterparts produced with other manufacturing processes. [ABSTRACT FROM AUTHOR]

With the rapid development of high-energy laser system, thick silicon mirror, as an important optical component to reflect laser, is required to have a surface with micron-level flatness, which poses significant challenges to silicon mirror manufacture process. Fine rotation grinding (FRG) is a typical efficient method for silicon mirror manufacture. A geometric model is developed in this paper to research silicon mirror surface figure control mechanism in FRG process. The model demonstrates the relationship between the grinding spindle obliquity and the mirror surface figure. From an application perspective, the relationship between the spindle obliquity and the spindle dial gauge indication is further presented. Based on this model, a MATLAB program is designed to give a series of simulation results for researching spindle adjustment regularity and getting the most ideal spindle obliquity. Besides, several grinding experiments are carried out. The simulation and experiment results are in good agreement, and a silicon mirror surface with the flatness of less than 2 μm is achieved. The proposed grinding geometric model can provide an actual guide for grinding spindle adjustment to control the surface figure of silicon mirror. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for determining the optimal supply chain design with economic, environmental and risk management considerations. A multi-objective model based on mixed integer programming is proposed seeking three objectives: First, to minimize the total cost of transportation and the costs associated to the use of intermediate nodes. Second, to minimize the risks of product losses in transportation. Third, to minimize the environmental impact of CO2 emissions produced by transportation and storage operations. The proposed model is solved with two approaches: First, a commercial solver to compute the Pareto-optimal set of solutions. Second, a simulation-based optimization approach that allows to obtain statistically different but efficient solutions such that the decision-maker will be able to trade-off objectives while considering only Pareto optimal solutions. Experiments on random instances demonstrate the capability of the models and methods. [ABSTRACT FROM AUTHOR]

There are many emerging manufacturing processes whereby surface structures are processed by spatially laser patterning of an entire feature at a time, as opposed to rastering a small beam. It is important to ascertain and ideally control the induced thermal fields underneath the pattern. This paper develops a computational framework to rapidly evaluate the induced thermal fields due to application of a laser on the surface. The aggregate thermal fields are efficiently computed by superposing individual “beamlet” heat-kernel solutions, based on Green’s functions, to form complex surface patterns. The utility of the approach is that laser-process designers can efficiently compute the results of selecting various system parameters, such as spatially-variable laser intensity within a pattern. This allows one to rapidly compute system parameter studies needed in the manufacturing of new products. Included are:A computational framework to compute the time-transient thermal response from a spatio-temporally non-uniform laser beam in an arbitrary spatial pattern andAn analysis of how the results can be used to track the evolution of the thermal gradients and their correlation to thermal stresses. Three-dimensional examples are provided to illustrate the technique. The utility of the approach is that an analyst can efficiently ascertain a large number of laser-input scenarios without resorting to computationally-intensive numerical procedures, such the Finite Element Method. [ABSTRACT FROM AUTHOR]

Hot metal gas forming is a modern metal forming process which is generally utilized to manufacture automotive parts with complex shape and light-weight materials such as aluminum-magnesium alloys. One of the critical parameters in this approach is controlling the internal pressure of the tube during the hot forming process. The improvement of formability in tube hydroforming by utilizing pulsating pressure paths has been confirmed in the last few years. In this paper, the effect of the pulsating pressure on the hot tube gas bulging process has been investigated by experimental and numerical methods. In addition, an oscillating heating mechanism was used to provide a uniform temperature distribution along the tube. A novel, simple pneumatic system was designed and used to provide pressure paths. Moreover, the finite element simulation of hot tube gas bulging was carried out to investigate the effect of different parameters of pulsating pressure on tube formability and thickness distribution. The simulation and experimental results showed that the proposed pulsating pressure path improved formability and thickness distribution along the tube in the hot metal tube gas bulging process. It was also concluded that the axial feeding intensifies the effect of pulsating pressure on formability and it should be applied right after the start of the plastic deformation of the tube. [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]

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]

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]

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]

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 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 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]