Optimization of SLHMS Based on Dynamic Balance of Mechanical and Electronic Spindle
作者单位：School of Information and Control Engineering Shenyang Jianzhu University
会议名称：《第30届中国控制与决策会议》
会议日期：2018年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化]
基 金：supported by the National Natural Science Foundation committee of China NO(61503259)
关 键 词：Mechanical spindle Dynamic balance Harmony search self-learning particle group(SLPSO)
摘 要：Mechanical spindle is an important component in the machining process, and it often produce an oscillation in the process of high-speed operation, making its spindle deviate from its geometric center, so that it would seriously affect the processing accuracy in the industrial process, thus affecting the stability of the entire system. At present, some dynamic balancing methods are difficult to establish model and have low control precision. Aiming at the above problem, this paper applies the harmony search algorithm to establish the model of the amplitude of the mechanical spindle vibration to search the optimal solution, ultimately search for the vibration deviation of the A and B balance blocks in the mechanical spindle. The harmony search algorithm can significantly improve the search speed because of its simple structure, less adjustable parameters and easy implementation, but the convergence effect is not ideal. Based on the harmony search, the improved self-learning particle swarm(SLPSO) algorithm is used to improve its convergence rate, and to avoid falling into the local optimal. The experimental results verify the feasibility and compared with the actual average, vibration accuracy respectively improves 7.316%, 6.741% and 3.476%.