Risk Warning Method for Complex Product Assembly Technology Problems Based on Pre-training Model and Similarity Algorithm

doi:10.3901/JME.2024.24.317

Abstract

Abstract: In the process of complex product development, various assembly technical problems often arise due to imperfect assembly process design. The traditional control scheme is to deal with them through multiple rounds of feedback after they occur, which seriously affects the assembly efficiency. In order to reduce the occurrence of assembly technical problems, this paper proposes a complex product assembly technical problem risk early warning method based on pre-trained models and similarity algorithms, which can realize the risk early warning of potential technical problems in the assembly process design stage. Firstly, the complex product assembly process is analyzed. Secondly, for short texts, the Chinese-roberta-wwm-ext-large pre-trained model is used; for long texts, the paraphrase-multilingual-mpnet-base-v2 pre-trained model is used to extract the semantics of historical technical problem feedback information. On this basis, the improved word mover’s distance algorithm and the cosine similarity algorithm are used respectively to calculate the semantic similarity between the historical problem process text and the new process text, and to retrieve the possible risk points in the new process and give early warning. Finally, a complex product assembly technical problem control system is developed and applied to a space enterprise, which verifies the effectiveness and accuracy of the proposed method.

Key words: complex products, assembly, risk warning, natural language processing, pre-training models, similarity algorithms

CLC Number:

TP301

ZHANG Jin, LIU Jianhua, ZHAO Wenhao, ZHUANG Cunbo, ZHANG Xu. Risk Warning Method for Complex Product Assembly Technology Problems Based on Pre-training Model and Similarity Algorithm[J]. Journal of Mechanical Engineering, 2024, 60(24): 317-329.

References

[1] 李伯虎，柴旭东. 复杂产品虚拟样机工程[J]. 计算机集成制造系统，2002(9)：678-683. LI Bohu，CHAI Xudong，Virtual prototype engineering for complex products[J]. Computer Integrated Manufacturing Systems，2002(9)：678-683.
[2] 尚育如，富大欣，李川生，等．航天工艺基础知识培训教材(下)[M]．北京：中国宇航出版社，2005． SHANG Yuru，FU Daxin，LI Chuansheng，et al. Training materials for basic knowledge of aerospace processes (2)[M]. Beijing：China Aerospace Press，2005.
[3] 庄存波，刘检华，唐承统，等. 复杂产品离散装配过程中的物料动态跟踪与管理技术[J]. 计算机集成制造系统，2015，21(1)：108-122. ZHUANG Cunbo，LIU Jianhua，TANG Chengtong，et al. Material dynamic tracking and management techniques in discrete assembly process of complex products[J]. Computer Integrated Manufacturing Systems，2015，21(1)：108-122.
[4] ZHANG G，LIU J，PENG L，et al. Factor-driven dynamic predictive maintenance policy for manufacturing system[J]. Computer Integrated Manufactering Systems，2013，19：2982-2992.
[5] 林伟锋，陈卫勇，陈晓雷，等. 燃煤电厂除尘设备预测性维护系统的研究[J]. 中国设备工程，2020，16：66-67. LIN Weifeng，CHEN Weiyong，CHEN Xiaolei，et al. Research on predictive maintenance system for coal-fired power plant dedusting equipment[J]. China Plant Engineering，2020，16：66-67.
[6] 王涌泉，高琦. CAPP系统中工艺知识推送方案的设计与研究[J]. 组合机床与自动化加工技，2019(5)：134-137. WANG Yongquan，GAO Qi. Design and research of process knowledge pushing scheme in CAPP system[J]. Combined Machine Tool and Automatic Machining Technology，2019(5)：134-137.
[7] HAJI-KAZEMI S，ARICA E，SEMINI M，et al. Detecting early warning signs of delays in shipbuilding projects[C]// IFIP International Conference on Advances in Production Management Systems. A. G，Switzerland：Springer，Cham，2015：215-222.
[8] MUXFELDT A，STEIL J J. Recovering from assembly errors by exploiting human demonstrations[J]. Procedia CIRP，2018(72)：63-68.
[9] 张峥. 基于信息网络的复杂产品系统研发风险识别[J].现代计算机，2022，28(10)：39-45. ZHANG Zheng. Risk identification of complex product system development based on information network[J]. Modern Computer，2022，28(10)：39-45.
[10] 刘检华，丁向峰，袁丁，等. 复杂产品计算机辅助装配过程控制与管理系统[J]. 计算机集成制造系统，2010，16(8)：1622-1633. LIU Jianhua，DING Xiangfeng，YUAN Ding，et al. Process control and management system for computer-aided assembly of complex products[J]. Computer Integrated Manufacturing Systems，2010，16(8)：1622-1633
[11] 杨飘，董文永. 基于BERT嵌入的中文命名实体识别方法[J]. 计算机工程，2020，46(4)：40-45. YANG Piao，DONG Wenyong. A Chinese named entity recognition method based on BERT embedding[J]. Computer Engineering，2020，46(4)：40-45.
[12] 李春楠，王雷，孙媛媛，等. 基于BERT的盗窃罪法律文书命名实体识别方法[J]. 中文信息学报，2021，35(8)：73-81. LI Chunnan，WANG Lei，SUN Yuanyuan，et al. BERT-based legal theft journal of instrument naming entity identification method[J]. Chinese Journal of Information，2021，35(8)：73-81.
[13] YOKOI S，TAKAHASHI R，AKAMA R，et al. Word rotator’s distance[C]// Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics，2020：2921-2931.
[14] KUSNER M，SUN Y，KOLKIN N，et al. Choosing an NLP library for analyzing software documentation：A systematic literature review and a series of experiments[C]// Proceedings of the 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR). Buenos Aires，Argentina：IEEE/ACM. 2017：417-428.
[15] VASWANI A，SHAZEER N，PARMAR N，et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach，CA，USA：Curran Associates，Inc. 2017：6000-6010.
[16] DEVLIN J，CHANG M W，LEE K，et al. Bert：Pre-training of deep bidirectional transformers for language understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics：Human Language Technologies，Volume 1(Long and Short Papers). Minneapolis，Minnesota：Association for Computational Linguistics. 2019：4171-4186.
[17] HAM A M. Integrated scheduling of m-truck，m-drone，and m-depot constrained by time-window，drop-pickup，and m-visit using constraint programming[J]. Transportation Research Part C：Emerging Technologies，2018，91(6)：1-14.
[18] REIMERS N，GUREVYCH I. Sentence-BERT：Sentence embeddings using siamese BERT-Networks[C]// Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong，China: Association for Computational Linguistics. 2019：3982-3992.
[19] RUBNER Y，TOMASI C，GUIBAS L J. The earth mover's distance as a metric for image retrieval[J]. International Journal of Computer Vision，2000，40(2)：99-121.
[20] KUSNER M，SUN Y，KOLKIN N，et al. From word embeddings to document distances[C]// Proceedings of the 32nd International Conference on Machine Learning. Lille，France：JMLR. 2015：957-966.
[21] SALTON G，BUCKLEY C. Term-weighting approaches in automatic text retrieval[J]. Information Processing & Management，1988，24(5)：513-521.
[22] GROS D，HABERMANN T，KIRSTEIN G，et al. Anaphora resolution：Analysing the impact on mean average precision and detecting limitations of automated approaches[J]. International Journal of Information Retrieval Research，2018，8(3)：33.