NEURAL NETWORK MODELING AND MULTIOBJECTIVE OPTIMIZATION OF CREEP FEED GRINDING OF SUPERALLOYS

被引：41

作者：

SATHYANARAYANAN, G

LIN, IJ

CHEN, MK

机构：

[1] Department of Industrial Engineering, Lehigh University, Bethlehem, PA

[2] Department of Industrial Engineering and Management, National Taipei Institute of Technology, Taipei

来源：

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH | 1992年 / 30卷 / 10期

基金：

美国国家科学基金会;

关键词：

D O I：

10.1080/00207549208948164

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The grinding process is a very complex system for which analytical and empirical models have been developed to pursue a control strategy. This paper utilizes a new approach to model the creep feed grinding of superalloys, Ti-6Al-4V and Inconel 718, by using a neural network. A back-propagation learning algorithm is adopted to capture the system behaviour. The neural network learns to associate the inputs (feed rate, depth of cut and wheel bond type) with the outputs (surface finish, force and power) and predicts the systems outputs within the working conditions. Mathematical formulation of a multiobjective optimization problem is then carried out by utilizing the network models. The optimization study results are presented in the form of decision tables and value path diagrams to assist the decision-making process.

引用

页码：2421 / 2438

页数：18

共 36 条

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