REFERENCES
1. US EPA. About green engineering. Available from: https://www.epa.gov/green-engineering/about-green-engineering [Last accessed on 20 Sep 2022].
5. Ren S, Zhang Y, Liu Y, Sakao T, Huisingh D, Almeida CM. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: a framework, challenges and future research directions. J Cle Prod 2019;210:1343-65.
6. Bevilacqua M, Ciarapica FE, Diamantini C, et al. Big data analytics methodologies applied at energy management in industrial sector: a case study. RFT 2017;8:105-22.
7. Li X, Wang B, Peng T, Xu X. Greentelligence: smart manufacturing for a greener future. Chin J Mech Eng 2021:34.
8. Singh S, Ramakrishna S, Gupta MK. Towards zero waste manufacturing: a multidisciplinary review. J Cle Prod 2017;168:1230-43.
9. Be ready for Industry 4.0 with cognitive manufacturing. Available from: https://www.ibm.com/downloads/cas/JD71Q7RK [Last accessed on 20 Sep 2022].
10. 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 Man Syst 2021;61:16-26.
11. Blum L, Blum M. A theory of consciousness from a theoretical computer science perspective: insights from the conscious turing machine. Proc Natl Acad Sci USA 2022;119:e2115934119.
12. Hu L, Miao Y, Wu G, Hassan MM, Humar I. iRobot-Factory: an intelligent robot factory based on cognitive manufacturing and edge computing. Future Generation Computer Systems 2019;90:569-77.
13. IBM. Cognitive manufacturing: an overview and four applications that are transforming manufacturing today. Available from: https://www.ibm.com/downloads/cas/VDNKMWM6 [Last accessed on 20 Sep 2022].
14. Agbozo R, Zheng P, Peng T, Tang R. Towards cognitive intelligence-enabled manufacturing. In: IFIP WG 5.7 APMS; 2022.Accepted
15. Belhadi A, Kamble SS, Zkik K, Cherrafi A, Touriki FE. The integrated effect of big data analytics, lean six sigma and green manufacturing on the environmental performance of manufacturing companies: the case of North Africa. J Cle Prod 2020;252:119903.
16. Understanding and using the Energy Balance - Analysis - IEA. Available from: https://www.iea.org/commentaries/understanding-and-using-the-energy-balance [Last accessed on 20 Sep 2022].
17. Liu W, Peng T, Kishita Y, et al. Critical life cycle inventory for aluminum die casting: a lightweight-vehicle manufacturing enabling technology. Appl Energy 2021;304:117814.
18. Curran MA. Strengths and Limitations of Life Cycle Assessment. In: Klöpffer W, editor. Background and Future Prospects in Life Cycle Assessment. Dordrecht: Springer Netherlands; 2014. pp. 189-206.
19. Finnveden G, Hauschild MZ, Ekvall T, et al. Recent developments in life cycle assessment. J Environ Manage 2009;91:1-21.
20. Material Resources. Center for sustainable systems. Available from: https://css.umich.edu/publications/factsheets/material-resources [Last accessed on 20 Sep 2022].
21. Peng T, Xu X. An interoperable energy consumption analysis system for CNC machining. J Cle Prod 2017;140:1828-41.
22. Peng T, Xu X, Wang L. A novel energy demand modelling approach for CNC machining based on function blocks. J Man Syst 2014;33:196-208.
23. Morris R, Tarassenko L, Kenward M. Cognitive systems: information processing meets brain science. Available from: https://books.google.com.hk/books?hl=zh-CN&lr=&id=mpSdXu8LAtYC&oi=fnd&pg=PP1&dq=Morris+R,+Tarassenko+L,+Kenward+M.+Cognitive+Systems:+Information+Processing+Meets+Brain+Science.+Academic+Press%3B+2006.&ots=ZyhQpUB3rn&sig=2PdvDIVAmgmVF0O5YO3gyk8JDnc&redir_esc=y#v=onepage&q&f=false [Last accessed on 20 Sep 2022].
24. Vernon D. Artificial cognitive systems: a primer. Available from: https://books.google.com.hk/books?hl=zh-CN&lr=&id=zzMqBQAAQBAJ&oi=fnd&pg=PR9&dq=Vernon+D.+Artificial+Cognitive+Systems:+A+primer.+MIT+Press%3B+2014.&ots=U29qpk8a6u&sig=JB7ji0GgN8FoO-azSqFna0SbBng&redir_esc=y#v=onepage&q=Vernon%20D.%20Artificial%20Cognitive%20Systems%3A%20A%20primer.%20MIT%20Press%3B%202014.&f=false [Last accessed on 20 Sep 2022].
25. Levitin D. Foundations of Cognitive Psychology: Core Readings. Available from: https://books.google.com.hk/books?hl=zh-CN&lr=&id=fDpmpub088AC&oi=fnd&pg= [Last accessed on 20 Sep 2022].
26. Stillings N. Cognitive science: an introduction. Available from: https://books.google.com.hk/books?hl=zh-CN&lr=&id=wCRonP7EgDkC&oi=fnd&pg=PR11&dq=Stillings+N.+Cognitive+Science:+An+Introduction.+Cambridge,+Mass.:+MIT+Press%3B+1995.&ots=BLj9MmX7lA&sig=CdBVhhHKNAYJ3Odf53iBhj6p7is&redir_esc=y#v=onepage&q=Stillings%20N.%20Cognitive%20Science%3A%20An%20Introduction.%20Cambridge%2C%20Mass.%3A%20MIT%20Press%3B%201995.&f=false [Last accessed on 20 Sep 2022].
27. Zheng P, Li S, Xia L, Wang L, Nassehi A. A visual reasoning-based approach for mutual-cognitive human-robot collaboration. CIRP Annals 2022;71:377-80.
28. Park H, Tran N. Development of an intelligent agent based manufacturing system. Available from: https://pdfs.semanticscholar.org/4a31/058d3d90ccea0feb4a44ca01d7ae75b9bde5.pdf [Last accessed on 20 Sep 2022].
29. Florida Atlantic University. Machine perceptions. Available from: http://www.ccs.fau.edu/~hahn/mpcr/ [Last accessed on 20 Sep 2022].
30. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of go without human knowledge. Nature 2017;550:354-9.
31. Leng J, Ruan G, Song Y, et al. A loosely-coupled deep reinforcement learning approach for order acceptance decision of mass-individualized printed circuit board manufacturing in industry 4.0. J Cle Prod 2021;280:124405.
32. Kralik JD, Lee JH, Rosenbloom PS, et al. Metacognition for a common model of cognition. Proc Comp Sci 2018;145:730-9.
34. Faruque M, Muthirayan D, Yu S, Khargonekar P. Cognitive digital twin for manufacturing systems. IEEE ;2021:440-5.
