Article: Optimisation of machining parameters using Hopfield-type neural networks Journal: International Journal of Industrial and Systems Engineering (IJISE) 2013 Vol.13 No.4 pp.462 - 479 Abstract: The variables affecting the economics of machining operations are numerous and include machine tool capacity, work piece geometry, cutting conditions such as velocity, feed rate, depth of cut, etc. Optimum selection of cutting conditions importantly contributes to the increase of productivity and the reduction of costs. Therefore, in recent years, more attention has been paid to the problem of optimum selection of cutting conditions for multi-pass operations. In this paper, a solution approach based on minimum unit cost criterion is proposed for this problem. The objective of the problem is to minimise unit production cost without violating any technological, economical and organisational constraints. A Hopfield-type dynamical network which employs a penalty function approach is used for solving the problem formulated by mixed integer linear programming. The results of the proposed approach tested on an illustrative example show that the approach provides better or least the same unit costs compared to existing approaches. Since the proposed approach is both effective and efficient, it can be integrated into an intelligent process planning system for solving complex machining parameters optimisation problems. The optimal solution of the proposed approach makes it attractive and suitable for the determination of optimum cutting conditions where there is no enough time for deep analysis. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation of machining parameters using Hopfield-type neural networks

Authors: Sezgi Ozen; G. Mirac Bayhan

Addresses: Department of Industrial Engineering, Dokuz Eylül University, Buca-Izmir 35160, Turkey ' Department of Industrial Engineering, Dokuz Eylül University, Buca-Izmir 35160, Turkey

Abstract: The variables affecting the economics of machining operations are numerous and include machine tool capacity, work piece geometry, cutting conditions such as velocity, feed rate, depth of cut, etc. Optimum selection of cutting conditions importantly contributes to the increase of productivity and the reduction of costs. Therefore, in recent years, more attention has been paid to the problem of optimum selection of cutting conditions for multi-pass operations. In this paper, a solution approach based on minimum unit cost criterion is proposed for this problem. The objective of the problem is to minimise unit production cost without violating any technological, economical and organisational constraints. A Hopfield-type dynamical network which employs a penalty function approach is used for solving the problem formulated by mixed integer linear programming. The results of the proposed approach tested on an illustrative example show that the approach provides better or least the same unit costs compared to existing approaches. Since the proposed approach is both effective and efficient, it can be integrated into an intelligent process planning system for solving complex machining parameters optimisation problems. The optimal solution of the proposed approach makes it attractive and suitable for the determination of optimum cutting conditions where there is no enough time for deep analysis.

Keywords: cutting conditions; multi-pass operations; parameter optimisation; neural networks; machining parameters; mixed integer linear programming; MLIP; intelligent process planning.

DOI: 10.1504/IJISE.2013.052610

International Journal of Industrial and Systems Engineering, 2013 Vol.13 No.4, pp.462 - 479

Published online: 27 Dec 2013 *

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Title: Optimisation of machining parameters using Hopfield-type neural networks

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