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Descripción
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| 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. | |
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Internacional
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Si |
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JCR del ISI
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Si |
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Título de la revista
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Ieee Transactions on Industrial Informatics |
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ISSN
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1551-3203 |
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Factor de impacto JCR
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9,112 |
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Información de impacto
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Volumen
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15 |
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DOI
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10.1109/TII.2018.2816971 |
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Número de revista
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Desde la página
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800 |
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Hasta la página
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811 |
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Mes
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MARZO |
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Ranking
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