Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

doi:10.1007/s40436-024-00481-5

Advances in Manufacturing ›› 2024, Vol. 12 ›› Issue (4): 679-697.doi: 10.1007/s40436-024-00481-5

• • 上一篇

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Hao-Liang Shi, Ping-Yu Jiang

State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

收稿日期:2023-05-23 修回日期:2023-08-30 发布日期:2024-12-06
通讯作者: Ping-Yu Jiang,E-mail:pjiang@mail.xjtu.edu.cn E-mail:pjiang@mail.xjtu.edu.cn
作者简介:Hao-Liang Shi received the B.S. degree and M.S. degree from Nanchang University, Nanchang, China. He is currently pursuing a PhD degree with the State Key Laboratory for Manufacturing Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, China. His research interests include production process quality control, knowledge graph, social manufacturing, and autonomous human-machine collaborations in shop floor. Ping-Yu Jiang is a professor of the state key laboratory for manufacturing systems engineering at Xi’an Jiaotong University (XJTU), China. He received his Ph.D degree in mechanical engineering from XJTU in 1991 and held Humboldt and JSPS international research fellowships from 1995 to 1999 in Germany and Japan, respectively. Prof. Jiang has been a faculty member at XJTU since 1991 and was promoted to full professor in 1999. His current research interests include social manufacturing, cyber-physical systems, product service systems, data-driven intelligent product design, etc.
基金资助:
This research was funded by the National Natural Science Foundation of China (Grant No. 51975464).

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Hao-Liang Shi, Ping-Yu Jiang

State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, People's Republic of China

Received:2023-05-23 Revised:2023-08-30 Published:2024-12-06
Contact: Ping-Yu Jiang,E-mail:pjiang@mail.xjtu.edu.cn E-mail:pjiang@mail.xjtu.edu.cn
Supported by:
This research was funded by the National Natural Science Foundation of China (Grant No. 51975464).

摘要/Abstract

摘要： In multistage machining processes (MMPs), a clear understanding of the error accumulation, propagation, and evolution mechanisms between different processes is crucial for improving the quality of machining products and achieving effective product quality control. This paper proposes the construction of a machining error propagation event-knowledge graph (MEPEKG) for quality control in MMPs, inspired by the application of knowledge graphs to data, information, and knowledge organization and utilization. Initially, a cyber-physical system (CPS)-based production process data acquisition sensor network is constructed, and process flow-oriented process monitoring is achieved through the radio frequency identification (RFID) production event model. Secondly, the process-related quality feature and working condition data are preprocessed; features are extracted from the distributed CPS nodes; and the production event model is used to achieve the dynamic mapping and updating of feature data under the guidance of the MEPEKG schema layer. Moreover, the mathematical model of machining error propagation based on the second-order Taylor expansion is used to quantitatively analyze the quality control in MMPs based on the support of MEPEKG data. Finally, the efficacy and reliability of the MEPEKG for error propagation analysis and quality control of MMPs were verified using a case study of a specially shaped rotary component.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00481-5

关键词: Multistage machining processes, Quality control, Machining error propagation, Knowledge graph, Cyber-physical system (CPS), Radio frequency identification (RFID) production event model

Abstract: In multistage machining processes (MMPs), a clear understanding of the error accumulation, propagation, and evolution mechanisms between different processes is crucial for improving the quality of machining products and achieving effective product quality control. This paper proposes the construction of a machining error propagation event-knowledge graph (MEPEKG) for quality control in MMPs, inspired by the application of knowledge graphs to data, information, and knowledge organization and utilization. Initially, a cyber-physical system (CPS)-based production process data acquisition sensor network is constructed, and process flow-oriented process monitoring is achieved through the radio frequency identification (RFID) production event model. Secondly, the process-related quality feature and working condition data are preprocessed; features are extracted from the distributed CPS nodes; and the production event model is used to achieve the dynamic mapping and updating of feature data under the guidance of the MEPEKG schema layer. Moreover, the mathematical model of machining error propagation based on the second-order Taylor expansion is used to quantitatively analyze the quality control in MMPs based on the support of MEPEKG data. Finally, the efficacy and reliability of the MEPEKG for error propagation analysis and quality control of MMPs were verified using a case study of a specially shaped rotary component.

The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-024-00481-5

Key words: Multistage machining processes, Quality control, Machining error propagation, Knowledge graph, Cyber-physical system (CPS), Radio frequency identification (RFID) production event model

Hao-Liang Shi, Ping-Yu Jiang. Quality control in multistage machining processes based on a machining error propagation event-knowledge graph[J]. Advances in Manufacturing, 2024, 12(4): 679-697.

参考文献

[1] Shang C, You F (2019) Data analytics and machine learning for smart process manufacturing: recent advances and perspectives in the big data era. Engineering 5:1010-1016
[2] Ji S, Pan S, Cambria E et al (2022) A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neural Netw Learn Syst 33:494-514
[3] He L, Jiang P (2019) Manufacturing knowledge graph: a connectivism to answer production problems query with knowledge reuse. IEEE Access 7:101231-101244
[4] Tsai JM, Sun IC, Chen KS (2021) Realization and performance evaluation of a machine tool vibration monitoring module by multiple MEMS accelerometer integrations. Int J Adv Manuf Technol 114:465-479
[5] Ghosh AK, Ullah AS, Teti R et al (2021) Developing sensor signal-based digital twins for intelligent machine tools. J Ind Inf Integr 24:100242. https://doi.org/10.1016/J.JII.2021.100242
[6] Lee WJ, Mendis GP, Triebe MJ et al (2020) Monitoring of a machining process using kernel principal component analysis and kernel density estimation. J Intell Manuf 31:1175-1189
[7] Serin G, Sener B, Ozbayoglu AM et al (2020) Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol 109:953-974
[8] Liu J, Zhou H, Liu X et al (2019) Dynamic evaluation method of machining process planning based on digital twin. IEEE Access 7:19312-19323
[9] Teti R, Mourtzis D, D’Addona DM et al (2022) Process monitoring of machining. CIRP Ann 71:529-552
[10] Bi Z, Liu Y, Krider J et al (2018) Real-time force monitoring of smart grippers for internet of things (IoT) applications. J Ind Inf Integr 11:19-28
[11] Dafflon B, Moalla N, Ouzrout Y (2021) The challenges, approaches, and used techniques of CPS for manufacturing in Industry 4.0: a literature review. Int J Adv Manuf Technol 113:2395-2412
[12] Ding K, Jiang P (2018) RFID-based production data analysis in an IoT-enabled smart job-shop. IEEE/CAA J Autom Sin 5:128-138
[13] Zhou G, Zhang C, Li Z et al (2019) Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing. Intl J Prod Res 58:1034-1051
[14] Leng J, Zhang H, Yan D et al (2019) Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop. J Ambient Intell Humaniz Comput 10:1155-1166
[15] Yang H, Kumara S, Bukkapatnam STS et al (2019) The Internet of Things for smart manufacturing: a review. IISE Trans 51:1190-1216
[16] Zhou L, Jiang Z, Geng N et al (2021) Production and operations management for intelligent manufacturing: a systematic literature review. Intl J Prod Res 60:808-846
[17] Wang C, Jiang P, Lu T (2018) Production events graphical deduction model enabled real-time production control system for smart job shop. Proc Inst Mech Eng C J Mech Eng Sci 232:2803-2820
[18] Wang C, Jiang P (2018) Manifold learning based rescheduling decision mechanism for recessive disturbances in RFID-driven job shops. J Intell Manuf 29:1485-1500
[19] Shojaeinasab A, Charter T, Jalayer M et al (2022) Intelligent manufacturing execution systems: a systematic review. J Manuf Syst 62:503-522
[20] Rezaei-Malek M, Mohammadi M, Dantan JY et al (2018) A review on optimisation of part quality inspection planning in a multi-stage manufacturing system. Intl J Prod Res 57:4880-4897
[21] Shi J, Zhou S (2009) Quality control and improvement for multistage systems: a survey. IIE Trans 41:744-753
[22] Hu SJ (1997) Stream-of-variation theory for automotive body assembly. CIRP Ann 46:1-6
[23] da Mata AS (2020) Complex networks: a mini-review. Braz J Phys 50:658-672
[24] Wang Y, Jiang P, Leng J (2016) An extended machining error propagation network model for small-batch machining process control of aircraft landing gear parts. J Aero Eng 231:1347-1365
[25] Du S, Xu R, Li L (2018) Modeling and analysis of multiproduct multistage manufacturing system for quality improvement. IEEE Trans Syst Man Cybern Syst 48:801-820
[26] Wang K, Yin Y, Du S et al (2021) Variation management of key control characteristics in multistage machining processes considering quality-cost equilibrium. J Manuf Syst 59:441-452
[27] Wang K, Li G, Du S et al (2021) State space modelling of variation propagation in multistage machining processes for variable stiffness structure workpieces. Int J Prod Res 59:4033-4052
[28] Li P, Jiang P, Guo W (2021) Modeling of machining errors’ accumulation driven by RFID graphical deduction computing in multistage machining processes. IEEE Trans Ind Inform 17:3971-3981
[29] Peres RS, Barata J, Leitao P et al (2019) Multistage quality control using machine learning in the automotive industry. IEEE Access 7:79908-79916
[30] Proteau A, Tahan A, Zemouri R et al (2023) Predicting the quality of a machined workpiece with a variational autoencoder approach. J Intell Manuf 34:719-737
[31] Dai HN, Wang H, Xu G et al (2019) Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies. Ent Info Syst 14:1279-1303
[32] Bader SR, Grangel-Gonzalez I, Nanjappa P et al (2020) A knowledge graph for Industry 4.0. In: Harth A, Kirrane S, Ngomo ACN et al (eds) Proceedings of the 17th international conference, ESWC 2020, Heraklion, Crete, Greece, May 31-June 4, Lecture Notes in Computer Science, vol 12123. Springer, Cham. https://doi.org/10.1007/978-3-030-49461-2_27
[33] Abu-Salih B (2021) Domain-specific knowledge graphs: a survey. J Netw Comput Appl 185:103076. https://doi.org/10.1016/J.JNCA.2021.103076
[34] Hedberg TD, Bajaj M, Camelio JA (2020) Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads. J Comput Inf Sci Eng 20(1):011011. https://doi.org/10.1115/1.4044921
[35] Armand H, Frédéric S, Romain P et al (2021) Context-aware cognitive design assistant: implementation and study of design rules recommendations. Adv Eng Inform 50:101419. https://doi.org/10.1016/J.AEI.2021.101419
[36] Zhou B, Bao J, Li J et al (2021) A novel knowledge graph-based optimization approach for resource allocation in discrete manufacturing workshops. Robot Comput Integr Manuf 71:102160. https://doi.org/10.1016/J.RCIM.2021.102160
[37] Zhou B, Hua B, Gu X et al (2021) An end-to-end tabular information-oriented causality event evolutionary knowledge graph for manufacturing documents. Adv Eng Inform 50:101441. https://doi.org/10.1016/J.AEI.2021.101441
[38] Liu H, Ma R, Li D et al (2021) Machinery fault diagnosis based on deep learning for time series analysis and knowledge graphs. J Signal Process Syst 93:1433-1455
[39] Liu M, Li X, Li J et al (2022) A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing. Adv Eng Inform 51:101515. https://doi.org/10.1016/J.AEI.2021.101515
[40] Lyu M, Li X, Chen CH (2022) Achieving knowledge-as-a-service in IIoT-driven smart manufacturing: a crowdsourcing-based continuous enrichment method for industrial knowledge graph. Adv Eng Inform 51:101494. https://doi.org/10.1016/J.AEI.2021.101494
[41] Guan S, Cheng X, Bai L et al (2022) What is event knowledge graph: a survey. IEEE Trans Knowl Data Eng 35(7):7569-7589. https://doi.org/10.1109/TKDE.2022.3180362
[42] Glavaš G, Šnajder J (2014) Event graphs for information retrieval and multi-document summarization. Expert Syst Appl 41:6904-6916
[43] Rospocher M, Van Erp M, Vossen P et al (2016) Building event-centric knowledge graphs from news. J Web Semant 37(38):132-151
[44] Li Z, Zhao S, Ding X et al (2017) EEG: knowledge base for event evolutionary principles and patterns. Commun Comput Inform Sci 774:40-52
[45] Gottschalk S, Demidova E (2018) EventKG: a multilingual event-centric temporal knowledge graph. Lect Notes Comput Sci 10843:272-287
[46] Ding X, Li Z, Liu T et al (2019) ELG: an event logic graph. arXiv preprint arXiv:1907.08015. https://doi.org/10.48550/arXiv.1907.08015
[47] Mao Q, Li X, Peng H et al (2021) Event prediction based on evolutionary event ontology knowledge. Futur Gener Comput Syst 115:76-89
[48] Gottschalk S, Demidova E (2019) EventKG—the hub of event knowledge on the web—and biographical timeline generation. Semant Web 10:1039-1070
[49] Souza Costa T, Gottschalk S, Demidova E (2020) Event-QA: a dataset for event-centric question answering over knowledge graphs. In: Proceedings of international conference on information and knowledge management, pp 3157-3164. https://doi.org/10.1145/3340531.3412760
[50] Wu J, Zhu X, Zhang C et al (2020) Event-centric tourism knowledge graph—a case study of Hainan. Lect Notes Comput Sci 12274:3-15
[51] Cheng D, Yang F, Wang X et al. (2020) Knowledge graph-based event embedding framework for financial quantitative investments. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, Xi’an, pp 2221-2230. https://doi.org/10.1145/3397271.3401427
[52] Kuntoğlu M, Salur E, Gupta MK et al (2021) A state-of-the-art review on sensors and signal processing systems in mechanical machining processes. Int J Adv Manuf Technol 116:2711-2735
[53] Ren H, Guo W, Jiang P et al (2021) An integrated approach of Active Incremental fine-tuning, SegNet, and CRF for cutting tool wearing areas segmentation with small samples. Knowl Based Syst 218:106838. https://doi.org/10.1016/J.KNOSYS.2021.106838

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

Quality control in multistage machining processes based on a machining error propagation event-knowledge graph

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