A Comparison of Statistical and AI Approaches to the Selection of Process Parameters in Intelligent Machining

Abstract

This paper presents an approach for the selection of a set of process parameters for use in machining control. The approach is aimed at providing a range of parameters within which machining operations can be optimized. Because of the complexity and somewhat unpredictable nature of the machining process, this approach combines process modelling with rule-based techniques. Modelling correlates process state variables such as surface roughness or chip merit mark to process parameters such as feed rate, cutting speed, and tool rake angle. The modelling techniques considered in this paper include multiple regression analysis, group method of data handling (GMDH), and neural network. A rule-based module determines the final operational range of control parameters based on user information and modelling predictions. The different modelling techniques have been evaluated using data from orthogonal cutting.