Independent Component Analysis Based on Machining Error Separation

ZHANG Fa-ping;WU Di;ZHANG Ti-guang;ZHANG Ling-yun;YANG Ji-bin

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  • Sponsored by: China Association for Science and Technology (CAST)

    Editor-In-Chief: Xu Yida

    ISSN 1000-1093

    　

  • Hosted By: China Ordnance Society

    Published By: Acta Armamentarii

    CN 11-2176/TJ

Acta Armamentarii, a Chinese monthly journal, was first published in 1979. The journal is supervised by China Association for Science and Technology, sponsored by China Ordnance Society，and edited and published by Editorial Board of Acta Armamentarii. The Journal is included in all important domestic databases，and also collected by Engineering Index (EI, USA), Chemical Abstracts (CA, USA), Science Abstracts (SA, UK）, Abstract Journal (AJ, Russia), and Current Bibliography on Science and Technology(CBST).Acta Armamentarii has been selected into the journal Of Ordnance Science and Technology (Weapons Industry) Field Of High Quality Scientific and Technological Periodical Classification Catalogue (2022 edition), ranking T1, and the journal of Automotive Engineering Field of High Quality Scientific and technological Periodical classification Catalogue, ranking T2.
Acta Armamentarii ›› 2016, Vol. 37 ›› Issue (9) : 1692-1699. DOI: 10.3969/j.issn.1000-1093.2016.09.020
Paper

Independent Component Analysis Based on Machining Error Separation

  • ZHANG Fa-ping, WU Di, ZHANG Ti-guang, ZHANG Ling-yun, YANG Ji-bin
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Abstract

For the unsuccessful separation of the multiple systematic errors with similar scales by current machining error separation method, a new method to separate the machining errors is proposed based on independent component analysis. An error transfer model is built to describe the relationship between the systematic error caused by individual error source and the final systematic error measured from the machining surface. Then according to the theory of blind signal separation, an optimization model is used for the machining error separation where the negative entropy of the estimated error is used as the optimization objective function, and the fixed point algorithm is used as the optimization method. A method to determine the number of machining error sources is also given by means of the principal component analysis. A study case of a certain gyroscope surface is tested to verify the efficiency of the error separation method. The proposed method provides a new way for separation of machining errors and tracing of error sources.

Key words

manufacturing technology and equipment / error source / error propagation model / independent component analysis / principal component analysis / error separation

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ZHANG Fa-ping, WU Di, ZHANG Ti-guang, ZHANG Ling-yun, YANG Ji-bin. Independent Component Analysis Based on Machining Error Separation. Acta Armamentarii. 2016, 37(9): 1692-1699 https://doi.org/10.3969/j.issn.1000-1093.2016.09.020

References

［1］ 田文杰,牛文铁,常文芬,等. 数控机床几何精度溯源方法研究［J］. 机械工程学报,2014,50（7）:128-135.
TIAN Wen-jie,NIU Wen-tie,CHANG Wen-fen,et al. Research on geometric error tracing of NC machine tools［J］.Journal of Mechanical Engineering,2014,50(7):128-135.(in Chinese)
［2］ 蒋庄德,赵卓贤. 形状误差、波度和表面粗糙度划分的谱分析方法［J］. 计量学报,1989,10(3):170-174.
JIANG Zhuang-de, ZHAO Zhuo-xian. A spectrum analysis method to distinguish form error, waviness and surface roughness［J］. Acta Metrologic Sinica,1989,10(3):170-174. (in Chinese)
［3］ RosenboomL, Kreis T, Jüptner W. Surface description and defect detection by wavelet analysis［J］. Measurement Science and Technology, 2011, 22(4):45102-45110.
［4］ 李海燕，刘国栋，刘炳国，等. 双密度小波在表面形貌信号分离中的应用［J］. 光学精密工程,2008,16(6):1094-1097.
LI Hai-yan， LIU Guo-dong， LIU Bing-guo，et al. Application of double density wavelet transform to surface topographic signal separation［J］. Optics and Precision Engineering,2008, 16(6):1094-1097.(in Chinese)
［5］ Liew K M, Wang Q. Application of wavelet theory for crack identification in structures［J］. Journal of Engineering Mechanics, 1998, 124(2):152-157.
［6］ Chi F C, Lee W B. A multi-spectrum analysis of surface roughness formation in ultra-precision machining［J］. Precision Engineering, 2000, 24(1):77-87.
［7］ 李世平,付宇,张进. 一种基于EMD的系统误差分离方法［J］.中国测试,2011,37(3):9-15,36.
LI Shi-ping, FU Yu, ZHANG Jin. Systematic error separation method based on EMD［J］. China Measurement and Test,2011,37(3):9-15, 36. (in Chinese)
［8］ 李成贵，李宝贵，孙丹. 基于HHT变换的超光滑加工表面谱特征分析［J］. 北京航空航天大学学报,2006,32(11): 1341-1344.
LI Cheng-gui, LI Bao-gui, SUN Dan. Application of HHT analysis in surface roughness［J］. Journal of Beijing University of Aeronautics and Astronautics,2006,32(11):1341-1344. (in Chinese)
［9］ 陈岳坪，高健，邓海祥,等．复杂曲面零件在线检测与误差补偿方法［J］. 机械工程学报，2012,48(23):143-151.
CHEN Yue-ping, GAO Jian, DENG Hai-xiang, et al. On-line inspection and machining error compensation for complex surfaces［J］. Journal of Mechanical Engineering,2012,48(23): 143-151. (in Chinese)
［10］ 张磊, 张志胜. 基于半参数回归模型的制造过程加工误差流建模与分析［J］.机械工程学报,2013,49(15):181-185.
ZHANG Lei, ZHANG Zhi-sheng. Stream of variation modeling and analysis for manufacturing processes based on a semi-parametric regression model［J］. Journal of Mechanical Engineering,2013,49(15):181-185. (in Chinese)
［11］ SurianoS, Wang H, Hu S J. Sequential monitoring of surface spatial variation in automotive machining processes based on high definition metrology［J］. Journal of Manufacturing Systems, 2012, 31(1):8-14.
［12］ 韩良，芮小健，钟秉林. 主轴回转误差信号的周期性试验研究［J］.制造技术与机床, 1994(2):26-29.
HAN Liang, RUI Xiao-jian, ZHONG Bing-lin. Experimental study on periodicity of spindle rotation error signal［J］. Manufacturing Technology and Machine Tool, 1994(2):26-29. (in Chinese)
［13］ TongL, Liu R, Soon V C, et al. Indeterminacy and indentifiability of blind indetification［J］. IEEE Transactions on Circuits and System,1991,38(5):499-506.
［14］ HyvrinenA, Oja E. Independent component analysis: algorithms and applications［J］. Neural Networks,2000, 13(4/5):411-442.
［15］ 赵忠华,杨晓梅. 基于FastICA的语音盲源分离方法［J］. 四川大学学报:自然科学版,2015,52(4):830-834.
ZHAO Zhong-hua,YANG Xiao-mei. Blind separation of sound signal by using FastICA［J］. Journal of Sichuan University:Natural Science Edition,2015,52(4):830-834.(in English)

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