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In complex manufacturing systems, such as a semiconductor wafer fabrication facility (wafer fab), it is important to accurately predict cycle times and work-in-progress (WIP) levels. These key performance indicators are commonly predicted using detailed simulation models; however, the detailed simulation models are computationally expensive and have high development and maintenance costs. In this paper, we propose an aggregate modeling approach, where each work area, i.e., a group of functionally similar workstations, in the wafer fab is aggregated into a single-server queueing system. The parameters of the queueing system can be derived directly from arrival and departure data of that work area. To obtain fab-level predictions, our proposed methodology builds a network of aggregate models, where the network represents the entire fab consisting of different work areas. The viability of this method in practice is demonstrated by applying it to a real-world wafer fab. Experiments show that the proposed model can make accurate predictions, but also provide insights into the limitations of aggregate modeling. [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]

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]

The aim of the paper is to present a case study and to point out the possibilities of using computer simulation for the purpose of increasing the efficiency and efficiency of custom production of a company. The use of simulation modeling as a scientific method in research and in practice brings benefits such as financial, time, material and energy savings, as well as streamlining activities in real practice. The development of advanced simulation systems has opened up new possibilities and significantly supported the trend of streamlining production activities, thus reducing costs and improving business performance. Simulation, however, is not a tool for obtaining an optimal solution, but rather a tool that allows you to test different decision outputs on a simulation model. Such a simulation model makes it possible to carry out various experiments to evaluate, analyse and determine solution parameters that can then be used in a real system. Risk factors can be investigated and determined beforehand by 'replacing' the running simulation model while monitoring system performance and behaviour, then, after applying the required changes, the future behaviour of the system is examined for any potential problems and obstacles. Can be removed in advance. The goal is to analyse the material flow in the production process and then create a simulation to determine the length of production and identify bottlenecks in the production process. In order to get a better idea of the production process, a simulation model was developed in the selected software tool as a custom production project under the conditions of a particular company. The incentive to start production is given to the customer, where every order placed is immediately sent to the customer. The highest frequency order in the enterprise's production program is used to create the material flow. [ABSTRACT FROM AUTHOR]

Simulation modeling results for powder mixtures comprised of saponite and stainless steel powders are presented. A literature review focusing on methods for modeling the filling and production of powder materials in technological processes was conducted. Computer modeling employed to examine the behavior of particulate mixtures in two-dimensional and three-dimensional planes, considering their volumetric characteristics, was studied theoretically. The properties of AISI430 steel and saponite powders, such as grain-size composition and sphericity factors, were studied experimentally. The density of workpieces made from steel and saponite powders for modified and unmodified powder particles was calculated as percentage. A method for calculating the distribution of particles and determining the density of workpiece layers using the ImageJ2x software was proposed. Image stacks were generated and image series were analyzed using object mask functions and threshold and image inverter brightness threshold functions. The processed microscopy data enabled the determination of void percentage within the visible areas of the samples. A simulation model was proposed to describe the formation of powder materials with varying geometries and sphericity. The process of filling a cylindrical container with the powder mixture was simulated. A 3D computer model was developed to visualize the formation of a two-component powder mixture using the Blender 3D application software. Maps depicting the distribution of powder particles in selected planes were constructed. [ABSTRACT FROM AUTHOR]

In manufacturing systems, simulation modeling plays an important role in creating some changes instead of working on real systems. Manipulation in a real system is more costly than manipulation in a simulated model. In this research, we tried to use a simulation approach to recognize and minimize bottlenecks of a production line, which will decrease costs and improve productivity. To achieve our objectives, we chose a case and analyzed its production line. By using our case study strategy, we tried to collect our data and adapt a conceptual model of production processes drawing on an operation process chart (OPC). After that, we created a simulation model of the production processes by using the popular Arena simulation software 13.5. By running the Arena model, some bottlenecks were found. Ultimately, we proposed some solutions to obviate bottlenecks and reduce the total costs of production. [ABSTRACT FROM AUTHOR]

The phenomenal growth experienced by Turkey at the turn of the millennium is attributed in part to increased participation in global value chains. While participation in global value chains has been beneficial to the Turkish economy, it also poses unique environmental challenges. Consequently, this study focuses on shedding some light on the environmental externality of Turkey's participation in global value chains. This article examines the environmental effects of Turkey's participation in global value chains for the period 1990–2018, using a dynamic ARDL analysis. The study further compares the environmental effects of Turkey's backward and forward linkages into global value chains, so as to determine which contributes more to carbon emissions. The cointegration test results and dynamic ARDL simulations confirm the existence of a long-run relationship between the environment and global value chain participation. All measures of global value chain participation display a positive long-run impact on carbon emissions. The results also show that the polluting effect of backward and forward linkages into global value chains is not too different. The study finding suggests that Turkey is being assigned segments of the value chain that require dirtier production processes through incentives from global trade integration, thus making Turkey a pollution haven. It is concluded that this is because other countries continually source for inputs requiring dirty production processes from Turkey, as Turkey also exports final goods that are produced using eco-unfriendly techniques. Policymakers in Turkey therefore need to follow more environmentalist policies in the process of global value chain participation. [ABSTRACT FROM AUTHOR]

In the context of modern industry, optimization emerges as one of the most powerful tools, allowing decision-makers to allocate their resources more assertively and deal with complex manufacturing problems. Moreover, manufacturing systems usually involve activities' interdependency and high stochastic levels, which are necessary to associate optimization and simulation techniques to solve problems. Although simulation optimization is a powerful technique, it can converge on a good solution, which often limits its use in day-to-day operations. As an alternative, metamodels may be used to replace simulation models in the optimization process. In recent years, with the development in the machine learning area, algorithms with high learning capacity have emerged, making the metamodel-based simulation optimization (MBSO) a promising study field. Based on the latest theoretical research on the theme, MBSO techniques have been widely used to solve manufacturing problems. However, there is still no consensus about the experimental design, the learning algorithms, and the importance of the hyperparameter optimization step. Then, the article evaluates the performance of six machine learning algorithms trained with and without hyperparameter optimization, two experimental designs, and five different sample sizes to build metamodels in three real manufacturing cases. Based on the results, the random forest algorithm and the random design with 40 × sample size expressed the better performance to metamodels' development. Furthermore, the hyperparameter optimization step reduced the metamodels' error in about 32.83%. [ABSTRACT FROM AUTHOR]

Railway wheel manufacturing is a unique form of the hot forming process and consists of forging, rolling, and dishing operations. With the ever-improving technology, railway coaches and locomotives use wheels of various designs to cater to its growing need for speed and safety. Thus, it becomes imperative for a manufacturer of railway wheels to develop the capability of manufacturing railway wheels of a wide variety of designs to remain relevant in a competitive market. However, for developing any new wheel, the manufacturer needs to design new dies and rolls according to the new wheel design. Using a simulation model to predict possible outputs of railway wheel forming is essential for the design phase. Wheel rolling is one of the most complicated operations in railway wheel forming process. Railway wheel rolling is a particular type of shape rolling where a forged railway wheel is worked upon simultaneously by five rolls to give a specific shape. The present work attempts to develop a simulation model of the wheel rolling process. It proposes a mathematical model for determining roll movement parameters for producing wheels of any given dimension. The simulated results were compared with a rolled wheel from an industrial setup for a railway wheel manufacturing unit and found to be in good agreement based on the prediction of output geometry and load requirement. Thus, the developed simulation model based on FEM allows designers and shop managers to predict rolling outputs quickly with minimum cost implications. This model will help the designer eliminate costly trial productions required for stabilizing die and roll designs during the development of new railway wheels. Further, the simulation model can be beneficial for optimizing production parameters related to wheel rolling and understanding the effects of possible process deviations during production, thereby reducing rejections, energy consumption and improving the shop's overall productivity. The simulation was done using commercial DEFORM 3D finite element code. [ABSTRACT FROM AUTHOR]

Compared with traditional EDM, ultrasonic vibration assisted EDM (UEDM) shows better performance in machining efficiency and surface quality. However, the material removal process of UEDM is complex, and there are many influencing factors. It is difficult to describe the material removal process accurately. In this study, based on the voltage variation during UEDM processing and combined with the heat transfer theory, the material removal model of TC4 titanium alloy under the condition of single pulse vertical UEDM is established. The material removal process of UEDM under different amplitudes is analyzed. The machining efficiency and surface quality of UEDM with different amplitude of ultrasonic vibration under the condition of vertical ultrasonic vibration are verified by UEDM experiments. The best material removal rate (MRR) can be obtained by adjusting the current and ultrasonic amplitude, which can improve the efficiency of UEDM. [ABSTRACT FROM AUTHOR]

Eco-industrial parks (EIPs) are of increasing importance for implementing industrial ecology strategies and are facing increasing challenges in terms of environmental pollution and resource scarcity. As a complex adaptive system, an EIP involves multiple sectors and faces various disturbances that influence its evolutionary trajectories. This study adopts an agent-based model to simulate the material flows and industrial symbiosis process in the EIP, considering the initiative of each company and the ever-changing environment. The proposed EIP model emphasises the heterogeneity of companies and attempts to reflect multiple and dynamic factors that have received less attention in previous studies. This model contains two types of agents, companies and the external environment. A company agent makes decisions and interacts with other agents following its own behaviour rules, while the external environment agent functions to coordinate the material flows and exert influence on the companies. The model has been verified and validated by simulating a 20-year-period development of an empirical EIP in China. The simulation results are assessed by three indicators: eco-connectance, eco-efficiency, and industrial symbiosis indicator. Results showed that during the growing phase, the eco-connectance increased from 0.02 to 0.1 for the non-disturbance situation. The eco-efficiency and industrial symbiosis indicator also realised 78.5% and 74.8% of their total increments. The outcome of this research provides insights for the design of the strategies to improve the industrial symbiosis performance and is of high potential to facilitate EIPs in promoting eco-transformation and sustainable development. [ABSTRACT FROM AUTHOR]

This special issue provides creating synergies of "practice and research" increasing the potential and commercial viability of research and development in the field of Industry 4.0 paradigm, SMART technologies, intelligent manufacturing systems and applications, Lean management and manufacturing, simulation of manufacturing companies, the complexity of supply chain management, management and intelligent infrastructure (road, freight, public transport), RFID in manufacturing, wide range of ICT application challenges, communication ecosystem security, enterprise information system, traffic/transport/logistic issues in Industry 4.0. This research discussed the issue of the use of BIM technology and analyses BIM effect on productivity in construction project management. The fourth article, "Roadmap to an Adaptive Assembly Line for E-Axles", talks about achieving high quality, high variety batch size production can be quite expensive. [Extracted from the article]