A Spatio-temporal Fully Convolutional Recurrent Neural Network Based Surface Topography Prediction

doi:10.3901/JME.2021.20.292

Abstract

Abstract: Surface topography prediction is of great significance to reduce the fluctuation of product quality and processing cost as well as the scrap rate of parts. Based on the high definition metrology (HDM) measured data, a spatio-temporal series prediction model called spatio-temporal fully convolutional recurrent neural network (STFCRNN) is proposed, which achieves the 3D surface topography prediction of machining surface. The time autocorrelation function and global Moran's I are calculated to analyze the spatiotemporal correlation of the surface data, which is used to conduct an accurate input of the model, thereby overcoming the shortcomings of traditional prediction methods that do not make full use of the global and local features of the data and the randomness of model input. Moreover, the result of case study shows that the proposed method has a better comprehensive prediction performance, and its prediction accuracy is 12%-18% better than that of traditional methods and the prediction time is 1/5 of traditional methods.

Key words: surface topography prediction, spatio-temporal correlation analysis, neural networks, high definition metrology, nonstationary spatio-temporal series

CLC Number:

TH161

SHAO Yiping, TAN Jian, LU Jiansha. A Spatio-temporal Fully Convolutional Recurrent Neural Network Based Surface Topography Prediction[J]. Journal of Mechanical Engineering, 2021, 57(20): 292-304.

References

[1] DENKENA B,BÖß V,NESPOR D,et al. Prediction of the 3D surface topography after ball end milling and its influence on aerodynamics[J]. Procedia CIRP,2015,31:221-227.
[2] OMAR O,EL-WARDANY T,NG E,et al. An improved cutting force and surface topography prediction model in end milling[J]. International Journal of Machine Tools & Manufacture,2007,47(7-8):1263-1275.
[3] BENARDOS P G,VOSNIAKOS G C. Predicting surface roughness in machining:A review[J]. International Journal of Machine Tools and Manufacture,2003,43(8):833-844.
[4] 董永亨,李淑娟,李言,等. 球头铣刀余摆线加工表面形貌的建模与仿真研究[J]. 机械工程学报,2018,54(19):212-223. DONG Yongheng,LI Shujuan,LI Yan,et al. Research on modeling and simulation of surface topography obtained by trochoidal milling mode with ball end milling cutter[J]. Journal of Mechanical Engineering,2018,54(19):212-223.
[5] 王艳,李德蔺,刘建国,等. 基于动态轮廓采样法的轴向超声振动辅助磨削工件表面形貌预测与试验验证[J].机械工程学报,2018,54(21):221-230. WANG Yan,LI Delin,LIU Jianguo,et al. Prediction and experimental verification of workpiece surface topology in axial ultrasonic vibration assisted grinding based on dynamic profile sampling method[J]. Journal of Mechanical Engineering,2018,54(21):221-230.
[6] LU Xiaohong,HU Xiaochen,JIA Zhenyuan,et al. Model for the prediction of 3D surface topography and surface roughness in micro-milling Inconel 718[J]. The International Journal of Advanced Manufacturing Technology,2018,94(5):2043-2056.
[7] TORTA M,ALBERTELLI P,MONNO M. Surface morphology prediction model for milling operations[J]. The International Journal of Advanced Manufacturing Technology,2020,106(1):3189-3201.
[8] WANG Wei,LI Qingzhao,JIANG Yunfeng. A novel 3D surface topography prediction algorithm for complex ruled surface milling and partition process optimization[J]. The International Journal of Advanced Manufacturing Technology,2020,107(9):3817-3831.
[9] BUSTILLO A,GRZENDA M,MACUKOW B. Interpreting tree-based prediction models and their data in machining processes[J]. Integrated Computer-Aided Engineering,2016,23(4):349-367.
[10] COSTES J P. A predictive surface profile model for turning based on spectral analysis[J]. Journal of Materials Processing Technology,2013,213(1):94-100.
[11] DUAN Chunzheng,HAO Qinglong. Surface roughness prediction of end milling process based on IPSO-LSSVM[J]. Journal of Advanced Mechanical Design,Systems,and Manufacturing,2014,8(3):1-12.
[12] MANDAL N,DOLOI B,MONDAL B. Surface roughness prediction model using zirconia toughened alumina (ZTA) turning inserts:Taguchi method and regression analysis[J]. Journal of The Institution of Engineers (India):Series C,2016,97(1):77-84.
[13] MAUDES J,BUSTILLO A,GUERRA A J,et al. Random forest ensemble prediction of stent dimensions in microfabrication processes[J]. The International Journal of Advanced Manufacturing Technology,2017,91(1):879-893.
[14] PIMENOV D Y,BUSTILLO A,MIKOLAJCZYK T. Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth[J]. Journal of Intelligent Manufacturing,2018,29(5):1045-1061.
[15] TABERNIK D,ELA S,SKVAR J,et al. Segmentation-based deep-learning approach for surface-defect detection[J]. Journal of Intelligent Manufacturing,2019,31(3):759-776.
[16] 胡茑庆,陈徽鹏,程哲,等. 基于经验模态分解和深度卷积神经网络的行星齿轮箱故障诊断方法[J]. 机械工程学报,2019,55(7):9-18. HU Niaoqing,CHEN Huipeng,CHENG Zhe,et al. Fault diagnosis for planetary gearbox based on EMD and deep convolutional neural networks[J]. Journal of Mechanical Engineering,2019,55(7):9-18.
[17] 雷亚国,贾峰,孔德同,等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报,2018,54(5):94-104. LEI Yaguo,JIA Feng,KONG Detong,et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering,2018,54(5):94-104.
[18] WANG Meng,XI Lifeng,DU Shichang. 3D surface form error evaluation using high definition metrology[J]. Precision Engineering,2014,38(1):230-236.
[19] HUANG Zhenhua,SHIH A J,NI Jun. Laser interferometry hologram registration for three- dimensional precision measurements[J]. Journal of Manufacturing Science and Engineering,2006,128(4):1006-1013.
[20] MARTIN R L,OEPPEN J E. The identification of regional forecasting models using space:Time correlation functions[J]. Transactions of the Institute of British Geographers,1975,66:95-118.
[21] HOCHREITER S,SCHMIDHUBER J. Long short-term memory[J]. Neural Computation,1997,9(8):1735-1780.
[22] SHI Xingjian,CHEN Zhourong,WANG Hao,et al. Convolutional LSTM network:A machine learning approach for precipitation nowcasting[C]//Advances in Neural Information Processing Systems. December 7-12,2015,New York:Curran Associates,2015:802-810.
[23] SHELHAMER E,LONG J,DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640-651.
[24] FAYYAZ M,SAFFAR M H,SABOKROU M,et al. STFCN:Spatio-temporal fully convolutional neural network for semantic segmentation of street scenes[C]//Asian Conference on Computer Vision. November 20-24,2016,Cham:Springer,2016:493-509.