Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network
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.
[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.
[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.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:78280", author = "Shoujia Fang and Guoqing Ding and Xin Chen", title = "Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network", 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.", volume = "12", number = "12", pages = "656-5", }
