Multi Objective Optimization of Cutting Process Based on Improved Particle Swarm Optimization Algorithm

Abstract:

With the rapid development of machinery industry, the processing parameters which affect on the quality of the products in machining and measuring index also appeared diversified, which makes the study of multi-objective optimization problem is very important. Among the many factors, cutting consumption plays a key role in many indicators, in effect, cutting force and cutting temperature on the quality and performance of products is the most prominent, so this paper takes PCBN tool cutting Cr12MoV steel as the experimental basis, the cutting parameters to optimize the parameters;the cutting force and cutting temperature as the index;with the aid of the BP neural network modeling of cutting force and cutting temperature, at the same time, this paper improved particle swarm optimization algorithm to achieve multiple objectives, provides multi objective optimization parameters more reliable for die steel production process.

• Periodical: Materials Science Forum (Volumes 800-801)
• Pages: 688-692
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.800-801.688
• Online since: July 2014
• Authors: Xue Liu*, Cai Xu Yue, Ming Yang Wu, Yong Heng Yang, Guang Xu Ren, Xian Li Liu
• Keywords: BP Neural Network (BPNN), Die Steel Cr12MoV, Improved Particle Swarm Optimization Algorithm, PCBN Tool

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