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.
Cite this paper
Yang, C. , Jiang, H. and Liu, B. (2020). Optimization Design of Cutting Parameters Based on the Support Vector Machine and Particle Swarm Algorithm. Open Access Library Journal, 7, e6788. doi: http://dx.doi.org/10.4236/oalib.1106788.
Luo, Y.S., Qin, M.Y. and Li, Y.Z. (2014) Experimental Study on Residual Stress in Stainless Steel Cutting. Modern Manufacturing Engineering, No. 3, 79-81 104.
Zhang, G.X., Ji, W.X. and Wang, Y. (2016) Research on Optimization of CNC Machining Parameters for Green and Efficient Manufacturing. Machine Design and Research, No. 32, 127.
Kennedy, J. and Eberhart, R. (1995) Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks, Perth, 27 November-1 December 1995, 1942-1948. https://doi.org/10.1109/ICNN.1995.488968
Eberhart, R. and Kennedy, J. (1995) A New Optimizer Usingparticle Swarm Theory. Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, 4-6 October 1995, 39-43.
https://doi.org/10.1109/MHS.1995.494215
Zhang, Y. and Liu, Y.C. (2009) Traffic Forecasting Using Least Squares Support Vector Machines. Transportmetrica, 5, 193-213.
https://doi.org/10.1080/18128600902823216
Wang, X.B., Luo, F.J., Sang, C.Y., et al. (2017) Personalized Movie Recommendation System Based on Supportvector Machine and Improved Particle Swarm Optimization. IEICE Transactions on Information and Systems, E100-D, 285-293.
https://doi.org/10.1587/transinf.2016EDP7054
Li, N. (2006) Theoretical Analysis and Application Research of Particle Swarm Optimization Algorithm. Huazhong University of Science and Technology, Wuhan.
