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• W2794279078 abstract "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." @default.
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• W2794279078 date "2019-02-01" @default.
• W2794279078 modified "2023-10-13" @default.
• W2794279078 title "Automatic Selection of Optimal Parameters Based on Simple Soft-Computing Methods: A Case Study of Micromilling Processes" @default.
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• W2794279078 doi "https://doi.org/10.1109/tii.2018.2816971" @default.
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