Modeling of Piercing Based on DEFORM-3D and the Ensemble OSC-PLS-ELM Method

作者: Dong Xiao , Fenghua Yang , Ba Tuan Le , Shengyong Zhang

DOI: 10.1109/ACCESS.2018.2876435

关键词:

摘要: The seamless tube, being widely used in the automobile, aviation, petroleum, chemical, building, boiler, and military industries, is called “blood vessel of industry.” demands for high-quality tubes are increasing continuously. Piercing first deformation process tube production has great influence on quality product. This paper takes Diescher piercer as object study builds DEFORM finite-element model piercing process. can substitute practical serve a simulation platform be optimization parameters prediction quality, it also greatly reduces debugging time cost. Therefore, theoretical significance value use. Finally, by combining with ensemble OSC-PLS-ELM mathematical model, we build hybrid which further increases precision forecast. method advantages both PLS ELM, i.e., characteristics robustness feature extraction quick nonlinear processing capability ELM.

参考文章(32)
Xiong Luo, Yang Xu, Weiping Wang, Manman Yuan, Xiaojuan Ban, Yueqin Zhu, Wenbing Zhao, Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy Journal of The Franklin Institute-engineering and Applied Mathematics. ,vol. 355, pp. 1945- 1966 ,(2017) , 10.1016/J.JFRANKLIN.2017.08.014
R. Fakih, M.A., Mustapha, S., Tarraf, J., Ayoub, G., Hamade, Detection and assessment of flaws in friction stir welded joints using ultrasonic guided waves: experimental and finite element analysis Mechanical Systems and Signal Processing. ,vol. 101, pp. 516- 534 ,(2018) , 10.1016/J.YMSSP.2017.09.003
Xiong Luo, Changwei Jiang, Weiping Wang, Yang Xu, Jenq-Haur Wang, Wenbing Zhao, User behavior prediction in social networks using weighted extreme learning machine with distribution optimization Future Generation Computer Systems. ,vol. 93, pp. 1023- 1035 ,(2019) , 10.1016/J.FUTURE.2018.04.085
Svante Wold, Nouna Kettaneh-Wold, Bert Skagerberg, Nonlinear PLS modeling Chemometrics and Intelligent Laboratory Systems. ,vol. 7, pp. 53- 65 ,(1989) , 10.1016/0169-7439(89)80111-X
Pak Kin Wong, Hang Cheong Wong, Chi Man Vong, Zhengchao Xie, Shaojia Huang, Model predictive engine air-ratio control using online sequential extreme learning machine Neural Computing and Applications. ,vol. 27, pp. 79- 92 ,(2016) , 10.1007/S00521-014-1555-7
Yimin Yang, Yaonan Wang, Xiaofang Yuan, Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness IEEE Transactions on Neural Networks. ,vol. 23, pp. 1498- 1505 ,(2012) , 10.1109/TNNLS.2012.2202289
Guorui Feng, Zhenxing Qian, Ningjie Dai, Reversible watermarking via extreme learning machine prediction Neurocomputing. ,vol. 82, pp. 62- 68 ,(2012) , 10.1016/J.NEUCOM.2011.10.028
Jiuwen Cao, Tao Chen, Jiayuan Fan, Fast online learning algorithm for landmark recognition based on BoW framework conference on industrial electronics and applications. pp. 1163- 1168 ,(2014) , 10.1109/ICIEA.2014.6931341