Probability Distribution Prediction of Milling Error Generated by Tool and Artifact Coupling Deviation Based on Monte-Carlo Method

doi:10.12068/j.issn.1005-3026.2015.07.016

Abstract

Abstract: The milling force calculation model was established based on the theory of bevel cutting， and the milling force was obtained. The bend function of thin plate deformation was built， and the milling error in milling process of tool-workpiece coupling deformation was obtained based on the combination of tool deformation. Neural network fitting method was adopted to obtain the function relationship between the input milling parameters and the output maximum milling error. Considering the influence on metal cutting by the parameters of tool， material， workpiece and working condition， the input parameters were sampled by the Monte-Carlo method. The parameter samples were substituted into the function model which was fitted by neural network， and the milling error samples were obtained. Then a probability distribution prediction method of milling error was put forward by analyzing the probability characteristics of the milling error. It was closer to actual than the deterministic calculation of milling error.

Key words: milling error, coupling deformation, neural network, Monte-Carlo method, probability distribution

CLC Number:

TH161.12

ZHANG Yi-min， CAO Hui， HUANG Xian-zhen. Probability Distribution Prediction of Milling Error Generated by Tool and Artifact Coupling Deviation Based on Monte-Carlo Method[J]. Journal of Northeastern University Natural Science, 2015, 36(7): 985-990.

References

[1]Yang M Y，Park H D.The prediction of cutting forces in ball-end milling［J］.International Journal of Machine Tools and Manufacture，1991，31(1):45-54.
[2]Budak E，AltintasY，Armarego E J A.Prediction of milling force coefficient from orthogonal cutting data［J］.Journal of Manufacturing Science and Engineering，1996，118(2):216-224.
[3]Kops L，Vo D T.Determination of the equivalent diameter of an end mill based on its compliance ［J］.Annals of the CIRP，1990，39(1):93-96.
[4]Law K M Y，Geddam A，Ostaflev V A.A process-design approach to error compensation in the end milling pockets［J］. Journal of Materials Processing Technology ，1999，89/90:238-244.
[5]Seo T L.Integration of tool deflection during the generation of tool path ［D］.Nantes:University of Nantes-Ecole CentraleofNantes，1998.
[6]宋戈.基于切削力精确建模的钛合金薄壁件让刀变形预测研究［D］.济南:山东大学，2012.(Song Ge.Research on surface error prediction in milling process of thin wall part based on accurate cutting force modeling［D］.Jinan:Shandong University，2012.)
[7]胡自化.薄壁件数控侧铣若干基础理论、实验及集成技术问题的研究［D］.湘潭:湘潭大学，2008.(Hu Zi-hua.Study on some problems of basic theory，experiments and integrated technology of thin-walled components in numerical control peripheral milling［D］.Xiangtan:Xiangtan University，2008.)
[8]朱耀.AA7055铝合金在不同温度下及应变率下力学性能的实验研究［D］.哈尔滨:哈尔滨工业大学，2010.(Zhu Yao.Experimental research on mechanical properties of AA7055 aluminum alloys at different temperatures and strain rates［D］.Harbin:Harbin Institute of Technology，2010.)
[9]Depince P，Hascoet J Y.Active integration of tool deflections in end milling.part 1:prediction of milled surfaces ［J］.International Journal Machine Tools & Manufacture，2006，46(9):937-944.
[10]Ratchev S，Liu S，Huang W，et al.Milling error prediction and compensation in machining of low rigidity parts［J］.International Journal of Machine Tools & Manufacture，2004，44(15):1629-1641.
[11]O’Connor P D T，Kleyner A.Practical reliability engineering ［M］.5th ed.Chichester:Weily，2012.
[12]Zhang Y M.Connotation and development of mechanical reliability-based design［J］.Journal of Mechanical Engineering，2010，46(14):167-188.

[1]	LIU Yang， YAN Dong-mei， MENG Fan-wei. Improved Two-Branch Person Re-identification Algorithm Based on Transformer [J]. Journal of Northeastern University(Natural Science), 2023, 44(1): 26-32.
[2]	ZHANG Chun-lei， LI He， DONG Mao-lin， ZHANG Sheng-jie. Adaptive Neural Network Sliding Mode Control for the Fuel Cell Air Supply System [J]. Journal of Northeastern University(Natural Science), 2022, 43(9): 1270-1276.
[3]	MA Yuan-yuan， LIU Yan-ze， LIU Cheng-long， ZHANG Tian-jie. Chinese Investors’ Multi-perspective Sentiment Analysis and Its Role in Stock Market Forecasting [J]. Journal of Northeastern University(Natural Science), 2022, 43(8): 1201-1209.
[4]	ZHANG Yu， HE Kai-wen， LI Qing-shu， GONG Ya-dong. Intelligent Decision-Making of Machining Operation Method for STEP-NC-Compliant Freeform Surface Features [J]. Journal of Northeastern University(Natural Science), 2022, 43(7): 981-987.
[5]	JI Ce， ZHANG Xiao. Channel Estimation Algorithm of OFDM System Based on GSA-BP Neural Network [J]. Journal of Northeastern University(Natural Science), 2022, 43(6): 769-775.
[6]	YANG Bo-wen， HUO Jun-zhou， ZHANG Wei， ZHANG Zhan-ge. Research on Real-Time Overload Prediction Method of in-Service Structures [J]. Journal of Northeastern University(Natural Science), 2022, 43(4): 541-550.
[7]	FAN Chun-long， LI Yan-da， XIA Xiu-feng， QIAO Jian-zhong. A General Adversarial Attack Method Based on Random Gradient Ascent and Spherical Projection [J]. Journal of Northeastern University(Natural Science), 2022, 43(2): 168-175.
[8]	CHEN Bing， HAN Jin-yang， TANG Xiao-lei， XIA Bo-ran. Prediction Model for Elongation of Tension Leveling Based on Machine Learning Algorithm and Numerical Analysis [J]. Journal of Northeastern University(Natural Science), 2022, 43(2): 236-242.
[9]	JING Yuan-wei， XIE Hai-xiu， BAI Yun. Adaptive Finite-Time Funnel Congestion Control of TCP/AWM Network Systems [J]. Journal of Northeastern University(Natural Science), 2022, 43(10): 1369-1375.
[10]	WANG Lu， WANG Shuai， ZHANG Guo-feng， XU Li-sheng. Pedestrian Detection Based on Semantic Segmentation Attention and Visible Region Prediction [J]. Journal of Northeastern University(Natural Science), 2021, 42(9): 1261-1267.
[11]	ZHENG Yan， JIANG Yuan-xiang. Speaker Clustering Algorithm Based on Feature Fusion [J]. Journal of Northeastern University(Natural Science), 2021, 42(7): 952-959.
[12]	YU Hong-liang， WANG Xu， YANG Dan， LI Wei-jun. Backstepping Control Method of Cascaded STATCOM Based on a Current Observer [J]. Journal of Northeastern University(Natural Science), 2021, 42(6): 761-767.
[13]	ZHANG Tao， LIU Tian-wei， DU Wen-li. A Convolutional Neural Network Based Local Dimming Technology [J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 624-632.
[14]	LIAO Zhi-wei， CHEN Lin-tao， HUANG Jie-dong， ZHUANG Jing. Medium and Short-Term Electricity Coal Price Forecast Based on Feature Space Transformation and LSTM [J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 483-493.
[15]	ZHANG Yong-chao， LI Qi， REN Zhao-hui， ZHOU Shi-hua. Cross-Domain Fault Diagnosis of Rolling Bearings Using Domain Adaptation with Classifier Discrepancy [J]. Journal of Northeastern University(Natural Science), 2021, 42(3): 367-372.