基于热误差敏感度图的温度关键点选择方法

摘要/Abstract

摘要：

摘要：采用有限元法仿真计算了数控车削中心主轴箱的瞬态温度场分布及相应热变形位移，根据热误差敏感度导出了主轴箱的热误差敏感度图；运用小波图像压缩技术提取了512个有限单元结点作为侯选温度关键点；根据遗传优化算法原理，提出了流程图式的目标函数，并在Matlab软件中以M文件的形式编写目标函数，同步实现了温度关键点的选择以及与之匹配的热误差模型的建立.通过在数控车削中心的验证实验结果表明，根据优化关键点所建立的数控车削中心主轴箱热误差模型的计算精度较高.

关键词: 热误差敏感度, 数控车削中心, , 图像压缩, 温度关键点, 遗传优化

Abstract:

Abstract： The transient temperature fields and corresponding thermal deformations of a computer numerical control (CNC) turning center headstock were simulated using the finite element method. Based on the concept of thermal error sensitivity, the sensitivity images of thermal errors of the turning center headstock were derived from the simulation results. 512 candidate temperature key points were extracted from finite element nodes by using the wavelet image compression technique. According to the principle of the genetic optimization algorithm, the optimization object function was expressed as a flowchart which could be programmed as an Mfile in Matlab. The selection of temperature key points and building matching thermal error models were simultaneously realized. The validation experimental results on the turning center show that the fitting precision of thermal error models built on optimized temperature key points is better than that on arbitrarily selected temperature measuring points.

Key words: thermal error sensitivity, computer numerical control (CNC) turning center, image compression, temperature key points, genetic optimization

中图分类号:

U 463.1

赵海涛1，冯伟1，周海1，杨建国2. 基于热误差敏感度图的温度关键点选择方法[J]. 上海交通大学学报（自然版）, 2015, 49(05): 725-729.

ZHAO Haitao1,FENG Wei1,ZHOU Hai1,YANG Jianguo2. Method for Selection of Temperature Key Points Based on Thermal Error Sensitivity Images and Genetic Optimization[J]. Journal of Shanghai Jiaotong University, 2015, 49(05): 725-729.

参考文献

［1］Mian N S, Fletcher S, Longstaff A P, et al. Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations［J］. Precision Engineering, 2013, 37(2): 372379.

［2］Creighton E, Honegger A, Tulsian A, et al. Analysis of thermal errors in a highspeed micromilling spindle［J］. International Journal of Machine Tools and Manufacture, 2010, 50(4): 386393.

［3］Mori M, Mizuguchi H, Fujishima M, et al. Design optimization and development of CNC lathe headstock to minimize thermal deformation［J］. CIRP AnnalsManufacturing Technology, 2009, 58(1): 331334.

［4］Yang S H, Kim K H, Park Y K. Measurement of spindle thermal errors in machine tool using hemispherical ball bar test［J］. International Journal of Machine Tools and Manufacture, 2004, 44(23): 333340.

［5］Ramesh R, Mannan M A, Poo A N, et al. Thermal error measurement and modelling in machine tools. Part II. Hybrid bayesian network—Support vector machine model［J］. International Journal of Machine Tools and Manufacture, 2003, 43(4): 405419.

［6］林伟青, 傅建中, 许亚洲, 等. 基于最小二乘支持向量机的数控机床热误差预测［J］. 浙江大学学报: 工学版, 2008, 42 (6): 905909.

LIN Weiqing, FU Jianzhong, XU Yanzhou, et al. Thermal error prediction of numerical control machine tools based on least squares support vector machines［J］. Journal of Zhejiang University: Engineering Science, 2008, 42(6): 905909.

［7］Zhao H T, Yang J G, Shen J H. Simulation of thermal behavior of a CNC machine tool spindle［J］. International Journal of Machine Tools and Manufacture, 2007, 47(6): 10031010.

[1]	王聚团, 戚晓宁, 黄志明. 水下生产管汇测试技术及其改进研究[J]. 海洋工程装备与技术, 2022, 9(2): 43-49.
[2]	袁振钦, 邹科, 孙亚峰, 刘刚, 屈衍, 李居跃. 基于时域分析法的动态电缆疲劳分析[J]. 海洋工程装备与技术, 2022, 9(2): 50-55.
[3]	王娟, 杨明旺, 郑茂尧, 刘凌云, 赵立君. 高强钢在大型半潜式平台组块建造中的应用[J]. 海洋工程装备与技术, 2022, 9(1): 27-31.
[4]	陈欣, 赵晓磊, 王立坤, 肖德明, 张腾月. 深水大型吸力锚建造技术研究[J]. 海洋工程装备与技术, 2022, 9(1): 32-36.
[5]	尹彦坤, 易涤非. 半潜式生产平台船体结构关键节点工程临界评估[J]. 海洋工程装备与技术, 2022, 9(1): 52-57.
[6]	MA Qunsheng (马群圣), CEN Xingxing (岑星星), YUAN Junyi (袁骏毅), HOU Xumin (侯旭敏). Word Embedding Bootstrapped Deep Active Learning Method to Information Extraction on Chinese Electronic Medical Record[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 494-502.
[7]	ZHANG Shengfa (张胜发), TANG Na (唐纳), SHEN Guofeng (沈国峰), WANG Han (王悍), QIAO Shan (乔杉). Universal Software Architecture of Magnetic Resonance-Guided Focused Ultrasound Surgery System and Experimental Study[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(4): 471-481.
[8]	安庆升, 孙立东, 武秋生. 碳纤维增强复合材料发射筒设计研究[J]. 空天防御, 2021, 4(2): 13-.
[9]	KONG Xiangqiang (孔祥强), MENG Xiangxi (孟祥熙), LI Jianbo (李见波), SHANG Yanping (尚燕平), CUI Fulin (崔福林) . Comparative Study on Two-Stage Absorption Refrigeration Systems with Different Working Pairs[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 155-162.
[10]	ZHUANG Weimin (庄蔚敏), WANG Pengyue (王鹏跃), AO Wenhong (熬文宏), CHEN Gang (陈刚) . Experiment and Simulation of Impact Response of Woven CFRP Laminates with Different Stacking Angles[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 218-230.
[11]	ZHOU Xuhui (周旭辉), ZHANG Wenguang (张文光), XIE Jie (谢颉). Effects of Micro-Milling and Laser Engraving on Processing Quality and Implantation Mechanics of PEG-Dexamethasone Coated Neural Probe[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 1-9.
[12]	HUANG Ningning (黄宁宁), MA Yixin (马艺馨), ZHANG Mingzhu (张明珠), GE Hao (葛浩), WU Huawei (吴华伟). Finite Element Modeling of Human Thorax Based on MRI Images for EIT Image Reconstruction[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(1): 33-39.
[13]	WANG Xianjin, GAO Xu, YU Kuigang . Fixture Locating Modelling and Optimization Research of Aluminum Alloy Sidewall in a High-Speed Train Body[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 706-713.
[14]	QIAO Xing, MA Dan, YAO Xuliang, FENG Baolin. Stability and Numerical Analysis of a Standby System[J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 769-778.
[15]	WU Jin, MIN Yu, YANG Xiaodie, MA Simin . Micro-Expression Recognition Algorithm Based on Information Entropy Feature[J]. Journal of Shanghai Jiao Tong University(Science), 2020, 25(5): 589-599.