KNOWLEDGE-BASED OPTIMIZATION FOR INTELLIGENT MACHINING

In this paper, we develop a knowledge-based machining system that uses experimental machining data based on the adaptive control with optimization (ACO) concept. ACO has been extensively researched to determine optimum machining conditions such as feed rate and spindle speed in a way that increases the metal removal rate without violating machining constraints. In this work, we develop a linear recursive adaptive tool failure identifier model and a cutting condition constraint model using experimentally measured cutting forces. We also discuss the optimization strategy using a knowledge-based system, develop the intelligent controller, and illustrate a practical machining application that provides on-line direct measurement of tool failure.