Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network

Authors: Shoujia Fang, Guoqing Ding, Xin Chen

Abstract:

The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.

Keywords: Keypoint detection, curve feature, convolutional neural network, press-fit assembly.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.2363238

References:

[1] Bo You, Zhifeng Lou, Yi Luo, Yang Xu, and Xiaodong Wang. Prediction of Pressing Quality for Press-Fit Assembly Based on Press-Fit Curve and Maximum Press-Mounting Force, 2015.
[2] Xingyuan Wang, Zhifeng Lou, Xiaodong Wang, and Chonglin Xu. A new analytical method for press-fit curve prediction of interference fitting parts. Journal of Materials Processing Technology, 250:16–24, December 2017.
[3] Cong Tan, Guo-qing Ding, and Xin Chen. Algorithm in searching for the critical point of press-fit curve. In ELECTRICAL ENGINEERING AND AUTOMATION: Proceedings of the International Conference on Electrical Engineering and Automation (EEA2016), pages 673–680. World Scientific, 2017.
[4] Tan Cong. Research of ethercat-based auto parts distributed servo press system. Master’s thesis, Shanghai JiaoTong University, 2016.
[5] HUANG Jiao, BIN Guangyu, and WU Shuicai. Patient-specifc ecg classifcation based on one-dimensional convolution neural network. China Medical Devices, (3):11–14, 2018.
[6] Amarjot Singh, Devendra Patil, G Meghana Reddy, and SN Omkar. Disguised face identification (dfi) with facial keypoints using spatial fusion convolutional network. In Computer Vision Workshop (ICCVW), 2017 IEEE International Conference on, pages 1648–1655. IEEE, 2017.
[7] Rui Zhao, Ruqiang Yan, Jinjiang Wang, and Kezhi Mao. Learning to monitor machine health with convolutional bi-directional lstm networks. Sensors, 17(2):273, 2017.
[8] Daniel E Whitney. The potential for assembly modeling in product development and manufacturing. Technical report, In Proceedings of the 1995 IEEE International Symposium on Assembly and Task Planning, 1996.
[9] Hu Hong-wei. A feasibility study of pressing force-offset curve as the judgment basis for acceptance of bearing press-fit. LOCOMOTIVE & ROLLING STOCK TECHNOLOGY, (5):7–10, 2010.
[10] Yi Shanbo. Research on the Pressing Force and Pressing Force Curve of Rolling Bearings for Railway Freight Cars. PhD thesis, Central South University, 2005.
[11] David H Hubel and Torsten N Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology, 160(1):106–154, 1962.
[12] Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249–256, 2010.
[13] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 315–323, 2011.
[14] L´eon Bottou. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010, pages 177–186. Springer, 2010.
[15] Daniel Strigl, Klaus Kofler, and Stefan Podlipnig. Performance and scalability of gpu-based convolutional neural networks. In Parallel, Distributed and Network-Based Processing (PDP), 2010 18th Euromicro International Conference on, pages 317–324. IEEE, 2010.
[16] R. Anil, K. Manjusha, S. Sachin Kumar, and K. P. Soman. Convolutional neural networks for the recognition of malayalam characters. In Suresh Chandra Satapathy, Bhabendra Narayan Biswal, Siba K. Udgata, and J. K. Mandal, editors, Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014, pages 493–500, Cham, 2015. Springer International Publishing.