A neural network approach to multiple-objective cutting parameter optimization based on fuzzy preference information

Abstract This paper presents a neural network approach to multiple-objective cutting parameter optimization for planning turning operations. Productivity, operation cost, and cutting quality are considered as criteria for optimizing machining operations. A feedforward neural network and a dynamic training procedure are proposed for modeling manufacturers' preferences using sampled fuzzy preferential data. Optimum cutting parameters are determined based on neural network representations of manufacturers' fuzzy preference structures.

[1]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[2]  R. DeVor,et al.  An application of multiple criteria decision making principles for planning machining operations , 1984 .

[3]  V. E. Zhukovin,et al.  A decision making model with vector fuzzy preference relation , 1987 .

[4]  S.S. Rangwala,et al.  Learning and optimization of machining operations using computing abilities of neural networks , 1989, IEEE Trans. Syst. Man Cybern..

[5]  László Monostori,et al.  Neural networks—Their applications and perspectives in intelligent machining , 1991 .

[6]  Laura I. Burke,et al.  Tool condition monitoring in metal cutting: A neural network approach , 1991, J. Intell. Manuf..

[7]  Lucien Duckstein,et al.  Applying multicriteria decision making techniques for planning machining operations , 1989 .

[8]  Jun Wang,et al.  A feedforward neural network for multiple criteria decision making , 1992, Comput. Oper. Res..

[9]  Laura I. Burke,et al.  Competitive learning based approaches to tool-wear identification , 1992, IEEE Trans. Syst. Man Cybern..

[10]  Behnam Malakooti,et al.  An Interactive Multiple Criteria Approach for Parameter Selection in Metal Cutting , 1989, Oper. Res..

[11]  H. Zimmermann.  Fuzzy sets, decision making, and expert systems , 1987 .

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[13]  Stefan Chanas,et al.  A fuzzy preference relation in the vector maximum problem , 1987 .