基于Self-X认知制造网络实现认知大规模个性化定制——一种工业知识图谱及图嵌入技术使能的途径

Xinyu Li; Pai Zheng; Jinsong Bao; Liang Gao; Xun Xu

doi:10.1016/j.eng.2021.08.018

PDF(1225 KB)

工程（英文） ›› 2023, Vol. 22 ›› Issue (3) : 14-19. DOI: 10.1016/j.eng.2021.08.018

观点述评

基于Self-X认知制造网络实现认知大规模个性化定制——一种工业知识图谱及图嵌入技术使能的途径

Xinyu Li ^a ,
Pai Zheng ^b ,
Jinsong Bao ^a ,
Liang Gao ^c ,
Xun Xu ^d

作者信息 +

Achieving Cognitive Mass Personalization via the Self-X Cognitive Manufacturing Network: An Industrial Knowledge Graph- and Graph Embedding-Enabled Pathway

Xinyu Li ^a ,
Pai Zheng ^b ,
Jinsong Bao ^a ,
Liang Gao ^c ,
Xun Xu ^d

Author information +

History +

引用本文

EndNote

Ris (Procite)

Bibtex

导出引用

Xinyu Li, Pai Zheng, Jinsong Bao. 基于Self-X认知制造网络实现认知大规模个性化定制——一种工业知识图谱及图嵌入技术使能的途径. Engineering. 2023, 22(3): 14-19 https://doi.org/10.1016/j.eng.2021.08.018

参考文献

原文顺序 | 文献年度倒序 | 文中引用次数倒序

[1]	Zheng P, Wang H, Sang Z, Zhong RY, Liu Y, Liu C, et al. Smart manufacturing systems for Industry 4.0: conceptual framework, scenarios, and future perspectives. Front Mech Eng 2018;13:137–50.
[2]	Sisinni E, Saifullah A, Han S, Jennehag U, Gidlund M. Industrial Internet of Things: challenges, opportunities, and directions. IEEE Trans Ind Informatics 2018;14(11):4724–34.
[3]	Xu X. From cloud computing to cloud manufacturing. Robot Comput Integr Manuf 2012;28(1):75–86.
[4]	Zhang H, Liu Q, Chen X, Zhang D, Leng J. A digital twin-based approach for designing and multi-objective optimization of hollow glass production line. IEEE Access 2017;5:26901–11.
[5]	Wang J, Ma Y, Zhang L, Gao RX, Wu D. Deep learning for smart manufacturing: methods and applications. J Manuf Syst 2018;48:144–56.
[6]	Cheng Y, Chen K, Sun H, Zhang Y, Tao F. Data and knowledge mining with big data towards smart production. J Ind Inf Integr 2018;9:1–13.
[7]	Zheng P, Lin TJ, Chen CH, Xu X. A systematic design approach for service innovation of smart product-service systems. J Clean Prod 2018;201:657–67.
[8]	Lee J, Davari H, Singh J, Pandhare V. Industrial artificial intelligence for Industry 4.0-based manufacturing systems. Manuf Lett 2018;18:20–3.
[9]	Iarovyi S, Lastra JLM, Haber R, Del Toro R. From artificial cognitive systems and open architectures to cognitive manufacturing systems. In: Proceedings of 2015 IEEE 13th International Conference on Industrial Informatics; 2015 Jul 22–24; Cambridge, UK. IEEE; 2015. p. 1225–32.
[10]	Tao F, Qi Q, Liu A, Kusiak A. Data-driven smart manufacturing. J Manuf Syst 2018;48:157–69.
[11]	Li X, Wang Z, Chen C-H, Zheng P. A data-driven reversible framework for achieving sustainable smart product-service systems. J Clean Prod 2021;279:123618.
[12]	Wang Y, Li X, Mo D. Knowledge-empowered multi-task learning to address the semantic gap between customer needs and design specifications. IEEE Trans Ind Informatics 2021;17(12):8397–405.
[13]	Li X, Chen CH, Zheng P, Jiang Z, Wang L. A context-aware diversity-oriented knowledge recommendation approach for smart engineering solution design. Knowledge-Based Syst 2021;215:106739.
[14]	Grossberg S. A path toward explainable AI and autonomous adaptive intelligence: deep learning, adaptive resonance, and models of perception, emotion, and action. Front Neurorobot 2020;14:36.
[15]	Li X, Chen CH, Zheng P, Wang Z, Jiang Z, Jiang Z. A knowledge graph-aided concept–knowledge approach for evolutionary smart product–service system development. J Mech Des 2020;142(10):101403.
[16]	Goyal P, Ferrara E. Graph embedding techniques, applications, and performance: a survey. Knowledge-Based Syst 2018;151:78–94.
[17]	Galkin M, Auer S, Vidal ME, Scerri S. Enterprise knowledge graphs: a semantic approach for knowledge management in the next generation of enterprise information systems. In: Proceedings of the 19th International Conference on Enterprise Information System; 2017; Porto, Portugal. p. 88–98.
[18]	Li X, Lyu M, Wang Z, Chen CH, Zheng P. Exploiting knowledge graphs in industrial products and services: a survey of key aspects, challenges, and future perspectives. Comput Ind 2021;129:103449.
[19]	Hu SJ. Evolving paradigms of manufacturing: from mass production to mass customization and personalization. Procedia CIRP 2013;7:3–8.
[20]	Tseng MM, Jiao RJ, Wang C. Design for mass personalization. CIRP Ann 2010;59 (1):175–8.
[21]	Koren Y, Shpitalni M, Gu P, Hu SJ. Product design for mass-individualization. Procedia CIRP 2015;36:64–71.
[22]	Zheng P, Wang Z, Chen CH, Pheng Khoo L. A survey of smart product-service systems: key aspects, challenges and future perspectives. Adv Eng Informatics 2019;42:100973.
[23]	Lee J, Bagheri B, Kao HA. A cyber–physical systems architecture for Industry 4.0-based manufacturing systems. Manuf Lett 2015;3:18–23.
[24]	Wang S, Wan J, Zhang D, Li D, Zhang C. Towards smart factory for Industry 4.0: a self-organized multi-agent system with big data based feedback and coordination. Comput Netw 2016;101:158–68.
[25]	Zhang Y, Qian C, Lv J, Liu Y. Agent and cyber–physical system based selforganizing and self-adaptive intelligent shopfloor. IEEE Trans Ind Informatics 2017;13(2):737–47.
[26]	Lu Y, Xu X, Wang L. Smart manufacturing process and system automation—a critical review of the standards and envisioned scenarios. J Manuf Syst 2020;56:312–25.
[27]	Leng J, Jiang P, Liu C, Wang C. Contextual self-organizing of manufacturing process for mass individualization: a cyber–physical–social system approach. Enterp Inf Syst 2020;14(8):1124–49.
[28]	Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R. Cognitive computing. Commun ACM 2011;54(8):62–71.
[29]	Chen M, Herrera F, Hwang K. Cognitive computing: architecture, technologies and intelligent applications. IEEE Access 2018;6:19774–83.
[30]	Mills K. What is cognitive manufacturing? [Internet]. Wiltshire: Metrology; 2019 Jan 16 [cited 2021 Oct 3]. Available from: https://metrology.news/whatis-cognitive-manufacturing/.
[31]	Kumar A, Jaiswal A. A deep swarm-optimized model for leveraging industrial data analytics in cognitive manufacturing. IEEE Trans Ind Informatics 2021;17 (4):2938–46.
[32]	Zheng P, Liu Y, Tao F, Wang Z, Chen CH. Smart product-service systems solution design via hybrid crowd sensing approach. IEEE Access 2019;7:128463–73.
[33]	Li X, Jiang Z, Song B, Liu L. Long-term knowledge evolution modeling for empirical engineering knowledge. Adv Eng Informatics 2017;34:17–35.
[34]	Li X, Jiang Z, Liu L, Song B. A novel approach for analysing evolutional motivation of empirical engineering knowledge. Int J Prod Res 2018;56 (8):2897–923.
[35]	Cui L, Wei F, Zhou M. Neural open information extraction. In: Proceedings of 56th Annual Meeting of the Association for Computation Linguistics; 2018 Jul 15–20; Melbourne, VIC, Australia. Stroudsburg: Association for Computational Linguistics; 2018. p. 407–13.
[36]	Li J, Sun A, Han J, Li C. A survey on deep learning for named entity recognition. IEEE Trans Knowl Data Eng 2020:1–20.
[37]	Hong Y, Liu Y, Yang S, Zhang K, Hu J. Joint extraction of entities and relations using graph convolution over pruned dependency trees. Neurocomputing 2020;411:302–12.
[38]	Fatemi B, Taslakian P, Vazquez D, Poole D. Knowledge hypergraphs: prediction beyond binary relations. In: Bessiere C, editor. Proceedings of the TwentyNinth International Joint Conference on Artificial Intelligence; 2021 Jan 7–15; Yokohama, Japan. California: International Joint Conferences on Artificial Intelligence; 2021. p. 2191–7.
[39]	Zhang Y, Fang Q, Qian S, Xu C. Multi-modal multi-relational feature aggregation network for medical knowledge representation learning. In: Proceedings of the 28th ACM International Conference on Multimedia; 2020 Oct 12–16; Seattle, WA, USA. New York City: Association for Computing Machinery; 2020. p. 3956–65.
[40]	Pedreschi D, Giannotti F, Guidotti R, Monreale A, Ruggieri S, Turini F. Meaningful explanations of black box AI decision systems. Proc AAAI Conf Artif Intell 2019;33(1):9780–4.
[41]	Li X, Jiang Z, Guan Y, Li G, Wang F. Fostering the transfer of empirical engineering knowledge under technological paradigm shift: an experimental study in conceptual design. Adv Eng Informatics 2019;41:100927.
[42]	Molnar C. Interpretable machine learning: a guide for making black box models explainable [Internet]. 2021 Oct 3 [cited 2021 Oct 3]. Available from: https://christophm.github.io/interpretable-ml-book/.
[43]	Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B. Definitions, methods, and applications in interpretable machine learning. Proc Natl Acad Sci USA 2019;116(44):22071–80.
[44]	Zheng P, Xia L, Li C, Li X, Liu B. Towards Self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent reinforcement learning approach. J Manuf Syst 2021;61:16–26.
[45]	Cui Y, Kara S, Chan KC. Manufacturing big data ecosystem: a systematic literature review. Robot Comput Integr Manuf 2020;62: 101861.
[46]	Kianoush S, Savazzi S, Beschi M, Sigg S, Rampa V. A multisensory edge-cloud platform for opportunistic radio sensing in cobot environments. IEEE Internet Things J 2021;8(2):1154–68.