Modeling and optimization of multi-scale machining operations

Abstract

Minimization of production time, cost and energy while improving the part quality is the main goal in manufacturing. In order to be competitive in today’s global markets, it is crucial to develop high precision machine tools and maintain high productive operation of the machine tools through intelligent and effective selection of machining parameters. A recent shift in manufacturing industry is towards the production of high value added micro parts which are mainly used in biomedical and electronics industries. However, the knowledge base for micro machining operations is quite limited compared to macro scale machining processes. Metal cutting, which allows production of parts with complex shapes made from engineering materials, constitutes a large portion in all manufacturing activities and expected to remain so in upcoming years. In this thesis, modeling and optimization of macro scale turning and micro scale milling operations have been considered. A well known multi pass turning problem from the literature is used as a benchmark tool to test the performances of Particle Swarm Optimization (PSO) technique and nonlinear optimization algorithms. It is shown that acceptable results can be obtained through PSO in short time. Micro scale milling operation is thoroughly investigated through experimental techniques where the influences of machining parameters on the process outputs (machining forces, surface quality, and tool life) have been investigated and factors affecting the process outputs are identified. A minimum unit cost optimization problem is formulated based on the pocketing operation and machining strategies are proposed for different machining scenarios using PSO technique.