Control chart pattern recognition for small samples based on Siamese Neural Network

Abstract

Quality control for small samples is growing in popularity due to its broad application prospects and high research value, and one of the commonly used methods for quality control nowadays is control chart pattern recognition (CCPR). Deep learning is an effective way for CCPR, but its recognition rate of control chart patterns for small samples is low and the quality control capability is limited. Therefore, to solve these problems, this paper first proposes a Perceptron-Convolutional Siamese Neural Network (PCSNN) model to improve the pattern recognition rate of small sample control chart. And then, three pattern recognition models (Convolutional Neural Network, Perceptron-Convolutional Neural Network, and PCSNN) are compared to verify the validity of the proposed model. After that, simulation experiments are conducted, and results show that the proposed PCSNN model has a high recognition rate of control chart patterns for small samples and performs well when the sample quality parameters change within a certain range. Finally, the proposed model is applied to the manufacturing process of automatic boring of enterprise gearbox shell, and its validity is verified.

Keywords:

• control chart pattern recognition
• quality control
• Siamese Neural Network
• small samples

Author contributions

All authors contributed to the study conception and formal analysis. Investigation, methodology and supervision were performed by Kangqu Zhou, Yunhe Chen and Weiqing Xiong. The first draft of the manuscript was written by Yunhe Chen, after that Kangqu Zhou, Weiqing Xiong and Xiaorong Gong reviewed and edited the manuscript. In addition, Kangqu Zhou and Jianming Zhang provided project funding for this study. Finally, all authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Additional information

Funding

This work is supported by the SERES (High-end Model) New Energy Vehicle Innovation and Demonstration Intelligent Factory Program, China (No. 2022Q380).

Notes on contributors

Kangqu Zhou

Prof. Kangqu Zhou is currently a Professor at the College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China. She received the doctorate degree and her Master’s degree in casting in Mechanical Manufacturing and Automation from Chongqing University, Chongqing, China, in 1996 and 2003, respectively. Her current research interests include Intelligent manufacturing, Manufacturing big data development and utilization. Email: [email protected]

Yunhe Chen

Ms. Yunhe Chen is currently a postgraduate at the College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China. She received her Master’s degree and Bachelor’s degree in Mechanical Engineering from Chongqing University of Technology, Chongqing, China, in 2024 and 2021, respectively. Her current research interest is Quality engineering. Email: [email protected]

Weiqing Xiong

Dr. Weiqing Xiong is currently a lecturer at the College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China. She received the Ph.D. and B.S. degree in mechanical engineering from Chongqing University, Chongqing, China, in 2023 and 2017. Her current research interests include Cloud manufacturing, Manufacturing system engineering, Resource optimization allocation, Multi-objective optimization algorithms. Email: [email protected]

Jianming Zhang

Mr. Jianming Zhang is currently a Director of Human Resources and Director of Information Technology at Chongqing Millison Technologies INC, Chongqing, China. He has worked in enterprise digital for over 20 years. He is mainly engaged in the promotion and construction of industrial Internet in manufacturing enterprises. Email: [email protected]

Xiaorong Gong

Dr. Xiaorong Gong is currently a lecturer at the College of Mechanical Engineering, Chongqing University of Technology, Chongqing, China. She received the Ph.D. and Master’s degree in mechanical engineering from Chongqing University, Chongqing, China, in 2018 and 2012. Her current research interests include Manufacturing execution system, Networked collaborative manufacturing, Intelligent manufacturing. Email: [email protected]