Nowadays, the application of novel soft-computing methods to new industrial processes is often limited by the actual capacity of the industry to assimilate state-of-the-art computational methods. The selection of optimal parameters for efficient operation is very challenging in microscale manufacturing processes, because of intrinsic nonlinear behavior and reduced dimensions. In this paper, a decision-making system for selecting optimal parameters in micromilling operations is designed and implemented using simple and efficient soft-computing techniques. The procedure primarily consists of four steps: an experimental characterization; the modeling of cutting force and surface roughness by means of a multilayer perceptron; multiobjective optimization using the cross-entropy method, taking into account productivity and surface quality; and a decision-making procedure for selecting the most appropriate parameters using a fuzzy inference system. Finally, two different alloys for micromilling processes are considered, in order to evaluate the proposed system: a titanium-based alloy and a tungsten-copper alloy. The experimental study demonstrated the effectiveness of the proposed solution for automated decision-making, based on simple soft-computing methods, and its successful application to a real-life industrial challenge.

中文翻译：

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基于简单软计算方法的最佳参数自动选择：微铣削工艺案例研究


如今，新颖的软计算方法在新的工业流程中的应用往往受到行业吸收最先进计算方法的实际能力的限制。由于固有的非线性行为和尺寸的减小，在微型制造工艺中选择最佳参数以实现高效运行非常具有挑战性。在本文中，使用简单高效的软计算技术设计和实现了用于选择微铣削操作中最佳参数的决策系统。该过程主要包括四个步骤：实验表征；通过多层感知器对切削力和表面粗糙度进行建模；使用交叉熵方法进行多目标优化，同时考虑生产率和表面质量；以及使用模糊推理系统选择最合适参数的决策程序。最后，考虑用于微铣削工艺的两种不同合金，以评估所提出的系统：钛基合金和钨铜合金。实验研究证明了所提出的基于简单软计算方法的自动化决策解决方案的有效性，及其成功应用于现实生活中的工业挑战。