LAUSR

Selection of cutting parameters in micro-milling operations is essential for improving machining efficiency and quality, and prolonging the micro-milling tool life. The increase of material removal rate (MRR) always means the increase of cutting parameters, which may lead to poor surface quality and micro-milling tool failure, even cutter breakage. An optimization approach based on genetic algorithm is used to achieve the maximum MRR under the constraints of surface roughness and cutter breakage. A theoretical model for predicting micro-milling cutter breakage is presented and micro-milling experiments were conducted to establish statistical models of cutter breakage and surface roughness. The optimized results were achieved under the constraints of the specified surface roughness and compared under the different surface roughness limitation. We find that the optimized results improve the machining efficiency and quality in micro- milling and is affected by constraint conditions complicatedly.