Abstract
A hybrid of type-1 and type-2 fuzzy model is proposed, which is applied in controlling the surface roughness of mechanical workpiece in metal cutting manufacturing. There are dozens of factors that affect the quality of surface roughness. The factors can be divided into two groups that are controlled and uncontrolled factors, e.g. feed rate can be setup. Therefore it is controlled factor while tool wear is an example of uncontrolled factor. There are two kinds of factors respectively correspond to type-1 and type-2 solutions because type-1 is suitable for controlled factors and type-2 fuzzy logic can handle uncontrolled or uncertain inputs. The proposed study will use genetic algorithm to identify the significant factors during the cutting process and a mathematical model that can predict the surface roughness under process variations. A fuzzy set based model for metal cutting operations can be used to reliably predict surface roughness under variations so that a continuous control of surface roughness can be affirmed. Two main factors (feed rate and tool wear) which affect the quality of surface roughness are investigated and simulated. The result of simulation shows that hybrid fuzzy logic system has improved precision of output.
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Jiang, F., Li, Z., Zhang, YQ. (2006). Hybrid Type-1-2 Fuzzy Systems for Surface Roughness Control. In: Abraham, A., de Baets, B., Köppen, M., Nickolay, B. (eds) Applied Soft Computing Technologies: The Challenge of Complexity. Advances in Soft Computing, vol 34. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31662-0_25
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DOI: https://doi.org/10.1007/3-540-31662-0_25
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