Transactions of Nanjing University of Aeronautics & Astronautics
Efficient and Stable Optimization of Multi-pass End Milling Using a Cloud Drop-Enabled Particle Swarm Optimization Algorithm
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Abstract:
Optimization of machining parameters is of great importance for multi-pass end milling because machining parameters adversely or positively affect the time and quality of production. This paper develops a second-order full-discretization method （2ndFDM）-based 3-D stability prediction model for simultaneous optimization of spindle speed， axial cutting depth and radial cutting depth. The optimal machining parameters in each pass are obtained to achieve the minimum production time comprehensive considering constraints of 3-D stability， machine tool performance， tool life and machining requirements. A cloud drop-enabled particle swarm optimization （CDPSO） algorithm is proposed to solve the developed machining parameter optimization， and 13 benchmark problems are used to evaluate CDPSO algorithm. Numerical results show that CDPSO algorithm has a certain advantage in computational cost as well as comparable search quality and robustness. A demonstrative example is provided.
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