@article{(Open Science Index):https://publications.waset.org/pdf/10009883,
	  title     = {Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network},
	  author    = {Shoujia Fang and  Guoqing Ding and  Xin Chen},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Materials and Metallurgical Engineering},
	  volume    = {12},
	  number    = {12},
	  year      = {2018},
	  pages     = {650 - 654},
	  ee        = {https://publications.waset.org/pdf/10009883},
	  url   	= {https://publications.waset.org/vol/144},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 144, 2018},
	}