面向能耗的数控铣削过程建模与参数优化

摘要/Abstract

摘要： 为了选择合理的切削参数以达到降低能耗的目的,对稳定的数控铣削过程面向能耗进行建模并优化。首先,在分析输入功率去向构成的基础上,建立数控铣床系统输入功率模型。然后,建立数控铣床系统能耗测试平台。通过对实验数据的多元回归建立数控铣床输入功率与切削参数的函数,对比分析证实函数的精确性。随后,由该函数得出数控铣床稳定切削阶段的单位体积能耗函数,以此为优化目标,以铣床性能和表面质量为约束,通过引力搜索算法(GSA)进行切削参数的能效优化。最后,与经验的切削参数进行对比,结果表明优化后切削参数显著提高了铣床能量效率,大幅节省了电能。

关键词: 数控铣床, 节能, 功率模型, 切削参数优化, 引力搜索算法

Abstract: In order to select the appropriate cutting parameters to reduce energy consumption, a stable CNC milling process was modeled and optimized based on energy consumption function. First of all, the input power model of CNC milling system was built by characterizing its components. Then an energy consumption test platform was set up. The function between the input power of CNC milling system and cutting parameters was established through multiple regression, and its accuracy was confirmed by a comparative analysis. After that the specific energy consumption function during stable CNC milling process was figured out from the input power function. GSA was used to optimize the cutting parameters by a model, which regarded the ESEC as goal and the performance of milling machine and surface quality as constraints. Compared with empirical data, it is shown that the optimized cutting parameters will improve the energy efficiency of milling machine significantly and drastically.

Key words: CNC milling machine, energy saving, power model, optimization of cutting parameters, gravitational search algorithm(GSA)

中图分类号:

TH18

黄拯滔, 杨杰, 张超勇, 周志恒, 谢阳, 林文文. 面向能耗的数控铣削过程建模与参数优化[J]. 中国机械工程.

Huang Zhengtao, Yang Jie, Zhang Chaoyong, Zhou Zhiheng, Xie Yang, Lin Wenwen. Energy-oriented CNC Milling Process Modelling and Parameter Optimization[J]. China Mechanical Engineering.

参考文献

[1]Abele E, Anderl R, Birkhofer H. Environmentally-friendly Product Development: Methods and Tools[M]. London:Springerverlag London Limited, 2005.

[2]Steiner R, Frischknecht R. Metals Processing and Compressed Air Supply. No23[R]. Switzerland: Ecoinvent Report, 2007.

[3]Jiang Z, Zhang H, Yan W, et al. An Evaluation Model of Machining Process for Green Manufacturing[J]. Advanced Science Letters, 2011, 4(4/5):1724-1728.

[4]Aggarwal A, Singh H, Kumar P, et al.Optimizing Power Consumption for CNC Turned Parts Using Response Surface Methodology and Taguchis Technique—a Comparative Analysis[J]. Journal of Materials Processing Technology, 2008, 200(1):373-384.

[5]Gutowski T, Dahmus J, Thiriez A. Electrical Energy Requirements for Manufacturing Processes[C]//13th CIRP International Conference on Life Cycle Engineering. Leuven: CIRP,2006:560-564.

[6]Li L, Yan J, Xing Z. Energy Requirements Evaluation of Milling Machines Based on Thermal Equilibrium and Empirical Modelling[J]. Journal of Cleaner Production, 2013, 52:113-121.

[7]刘飞, 刘霜. 机床服役过程机电主传动系统的多时段能量模型[J]. 机械工程学报, 2012, 48(21):132-140.

Liu Fei, Liu Shuang.Multi-period Energy Model of Electro-mechanical Main Driving System during the Service Process of Machine Tools[J]. Chinese Journal of Machine Engineering, 2012, 48(21):132-140.

[8]Sonmez A H, BaykasoLu A, Dereli T, et al. Dynamic Optimization of Multipass Milling Operations via Geometric Programming[J]. International Journal of Machine Tools and Manufacture, 1999,39(2):297-320.

[9]Wang Z G, Rahman M, Wong Y S, et al. Optimization of Multi-pass Milling Using Parallel Genetic Algorithm and Parallel Genetic Simulated Annealing[J]. International Journal of Machine Tools and Manufacture, 2005,45(15):1726-1734.

[10]Tandon V, El-Mounayri H, Kishawy H. NC End Milling Optimization Using Evolutionary Computation[J]. International Journal of Machine Tools and Manufacture, 2002,42(5):595-605.

[11]刘海江, 黄炜. 基于粒子群算法的数控加工切削参数优化[J]. 同济大学学报（自然科学版）, 2008,36(6):803-806.

Liu Haijiang, Huang Wei.Computer Numerical Control Machining Parameter Optimization Based on Particle Swarm Optimization[J]. Journal of Tongji University (Natural Science), 2008,36(6):803-806.

[12]Jafarian F, Taghipour M, Amirabadi H. Application of Artificial Neural Network and Optimization Algorithms for Optimizing Surface Roughness, Tool Life and Cutting Forces in Turning Operation[J]. Journal of Mechanical Science and Technology, 2013,27(5):1469-1477.

[13]He Y, Liu B, Zhang X, et al. A Modeling Method of Task-oriented Energy Consumption for Machining Manufacturing System[J]. Journal of Cleaner Production, 2012,23(1):167-174.

[14]许弟建, 施金良. 机床功率测试系统设计与节能方法[J]. 组合机床与自动化加工技术, 2010(3):47-51.

Xu Dijian, Shi Jinliang. The Design of Active Power Test System and Energy Saving Method of NC Machine Tool[J]. Modular Machine Tool and Automatic Manufacturing Technique, 2010(3):47-51.

[15]施金良, 刘飞, 许弟建, 等. 数控机床空载运行时节能决策模型及实用方法[J]. 中国机械工程, 2009,20(11):1344-1346.

Shi Jinliang, Liu Fei,Xu Dijian, et al. Decision Model and Practical Method of Energy-saving in NC Machine Tool[J]. China Mechanical Engineering, 2009,20(11):1344-1346.

[16]施金良, 刘飞, 许弟建, 等. 变频调速数控机床主传动系统的功率平衡方程[J]. 机械工程学报, 2010,46(3):118-124.

Shi Jinliang, Liu Fei, Xu Dijian, et al. Power Balance Equation about the Numerical Control Machine Tool's Main Driver System Driven by Variable Voltage Variable Frequency[J]. Journal of Machine Engineering, 2010,46(3):118-124.

[17]Rashedi E, Nezamabadi P H, Saryazdi S. GSA: A Gravitational Search Algorithm[J]. Information Sciences, 2009, 179(13):2232-2248.

[18]Shi Y, Eberhart R. A Modified Particle Swarm Optimizer[C]// IEEE World Congress on Computational Intelligence. Evolutionary Computation Proceedings. New Jersey: IEEE Press, 1998:69-73.

[19]Nobahari H, Nikusokhon M, Siarry P. Non-dominatedSorting Gravitational Search Algorithm[C]//Proc. of the 2011 International Conference on Swarm Intelligence. Cergy: ICSI， 2011:1-10.