Multi-Objective Optimization of Grinding Processes with Two Approaches: Optimal Pareto Set with Genetic Algorithm and Multi-attribute Utility Theory

Optimization of grinding process is a multiobjective optimization problem. However, it traditionally has been solved as a single objective problem. In this paper, general, useful and practical multidisciplinary optimization methods are discussed for optimal design of surface grinding processes. The methods allow designer to explicitly consider and control multiple design objectives as an integrated part of the optimization process, and easily choose and set up preferences for the objectives in order to increase productivity and quality of the workpiece surface. The methods discussed in this paper are Pareto efficient set and multi-attribute utility theory based optimization approaches. The algorithm for finding Pareto set is proposed and an example is presented. A new formulation for multi-objective optimization of grinding process is developed. The results of the formulation represent the tradeoffs the designers are willing to make between work piece surface roughness, tool life, grinding ratio, and material removal rate. Four examples are used to illustrate the application of the formulation for multiobjective optimization of the surface grinding processes. An example for defining the preferences in the multi-objective optimization process is also presented.