Causal modelling and quality control of complex product assembly processes driven by data and knowledge fusion
International Journal of Production Research · Kai Guo Guijiang Duan Hengyu Zhang Haoming Rong Zhongchuan Ouyang a School of Mechanical Engineering and Automation, Beihang University, Beijing, People’s Republic of Chinab Jingdezhen Research Institute, Beihang University, Beijing, People’s Republic of Chinac Ningbo Institute of Beihang University, Beijing, People’s Republic of Chinad Jiangxi Changhe Aviation Industry Co., Ltd., Jingdezhen, People’s Republic of ChinaKai Guo is a Ph.D. student at the School of Mechanical Engineering and Automation, Beihang University. His research interests include quality management and control, causal learning, and reinforcement learning.Guijiang Duan is a full professor at the School of Mechanical Engineering and Automation, Beihang University. He received his bachelor’s and master’s degrees from Dalian University of Technology in 1992 and 1995, respectively. In 1999, he obtained his Ph.D. degree from Beihang University and completed his postdoctoral research there in 2001. Professor Duan has long been engaged in research on modern manufacturing technology and systems, modern quality engineering, quality management informatisation, and digital inspection technologies.Hengyu Zhang is a master’s student at Beihang University. His research interests include digital inspection, intelligent manufacturing, and industrial software algorithms.Haoming Rong received his bachelor’s degree in Aircraft Manufacturing Engineering from Nanjing University of Aeronautics and Astronautics in 2009. His research interests include digital assembly technologies.Zhongchuan Ouyang received his bachelor’s degree in Aircraft Power Engineering from Nanchang Hangkong University in 2012. His research interests include helicopter assembly technologies.
摘要
本研究针对复杂产品装配过程的质量控制问题,提出了一种基于数据与知识融合的因果建模方法。该方法整合了过程数据和领域知识,以构建更准确的因果模型来识别质量问题的根本原因。研究结果表明,该融合方法能有效提升装配过程的质量控制能力,为复杂制造系统的质量改进提供了管理启示。