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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]

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]