TITLE:
Optimization Design of Cutting Parameters Based on the Support Vector Machine and Particle Swarm Algorithm
AUTHORS:
Chengwen Yang, Hao Jiang, Baolin Liu
KEYWORDS:
Particle Swarm Algorithm, Support Vector Machine, Cutting Parameters Optimization Design
JOURNAL NAME:
Open Access Library Journal,
Vol.7 No.9,
September
29,
2020
ABSTRACT:
In the field of machining, the processing design of the cutting parameters has the characteristics of greater subjectivity and experience. For obtaining the ideal roughness of surface (Ra), the number of machining experiments is carried out. It makes a lot of waste of materials, labor, energy, and so on. In addition, there is a highly non-linear function relationship between the three elements of cutting: cutting speed (vc), feed (f), cutting depth (ap) and roughness of the surface. It is hard to use mathematical equations to express the relationship clearly. So, in this paper, the support vector machine (SVM) will be used to establish the model of cutting elements and roughness of the surface. Then, taking roughness of surface as optimization goal and cutting elements as optimization parameters, the particle swarm algorithm (PSO) will be carried out to obtain a group of cutting parameters for the ideal roughness surface. It provides an easy, accurate, and feasible optimization design method for machining cutting parameters optimization design.