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Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics

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Abstract

The industrial cloud robotics (ICRs) integrates distributed industrial robot resources in various places to support complex task processing for multi-resource service requirements, and manufacturing capability assessment plays an important role in determining the optimal service composition to realize the value-added of ICRs resources. However, ignoring correlation relationship within the robot composition, the traditional evaluation method cannot reflect the impact of the robot individuals on the overall manufacturing capability of the ICRs composition. In addition, the problems of excessive resource consumption and environmental pollution in the manufacturing industry are becoming increasingly serious. The paper proposes a dynamic assessment method of sustainable manufacturing capability for ICRs based on the correlation relationship. Firstly, an extensible multi-dimensional indicator system of sustainable manufacturing capability is constructed. Then, multiple composition correlation relationships among ICRs are analyzed to establish the correlation assessment model. Furthermore, a set of dynamic evaluation methods is proposed, in which raw data of the evaluation indicators is processed based on the service correlation model, and the traditional network analytic network process method is improved based on the data correlation model. Finally, a case study is implemented to show the reasonability and effectiveness of the proposed method in the assessment of sustainable manufacturing capability for ICRs.

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The data and materials of this manuscript are available from the corresponding authors on reasonable request.

References

  1. Yan H, Hua Q, Wang Y et al (2017) Cloud robotics in smart manufacturing environments: challenges and countermeasures. Comput Electr Eng 63:56–65

    Article  Google Scholar 

  2. Liu J, Xu W, Zhang J, Zhou Z, Pham DT (2016) Industrial cloud robotics towards sustainable manufacturing. ASME International Manufacturing Science and Engineering Conference (MSEC’16), USA, Blacksburg

  3. Brossog M, Bornschlegl M, Franke J (2015) Reducing the energy consumption of industrial robots in manufacturing systems. Int J Adv Manuf Technol 78(5):1315–1328

    Google Scholar 

  4. Xu X (2012) From cloud computing to cloud manufacturing. Rob Comp Integr Manuf 28(1):75–86

    Article  Google Scholar 

  5. Zhang Y, Cui G, Deng S (2018) Efficient query of quality correlation for service composition. IEEE Trans Serv Comput 99:1–1

    Google Scholar 

  6. Guo H, Tao F, Zhang L et al (2010) Correlation-aware web services composition and QoS computation model in virtual enterprise. Int J Adv Manuf Technol 51(5–8):817–827

    Article  Google Scholar 

  7. Yoshio Y (2019) Dynamically evolving algorithm for minimizing the energy consumption of a manipulator. Artif Life Rob 24(2):1–5

    Google Scholar 

  8. Giret A, Trentesaux D, Prabhu V (2015) Sustainability in manufacturing operations scheduling: a state of the art review. J Manuf Syst 37:126–140

    Article  Google Scholar 

  9. ** L, Xu W, Liu Z, Yan J, Zhou Z, Pham D (2018). Knowledge sharing and evolution of industrial cloud robotics. ASME 2018 13th International Manufacturing Science and Engineering Conference, United states, TX

  10. Du H, Xu W, Yao B, Zhou Z, Hu Y (2019) Collaborative optimization of service scheduling for industrial cloud robotics based on knowledge sharing. Procedia CIRP 83:132–138

    Article  Google Scholar 

  11. Skinner W (1969) Manufacturing–missing link in corporate strategy. Harv Bus Rev 6(9):136–145

    Google Scholar 

  12. Luo Y, Zhang L, Tao F, Ren L, Liu Y, Zhang Z (2013) A modelling and description method of multidimensional information for manufacturing capability in cloud manufacturing system. Int J Adv Manuf Technol 69(5–8):961–975

    Article  Google Scholar 

  13. Zhao Y, Liu Q, Xu W, Wu X, Jiang X, Zhou Z, Pham D (2017) Dynamic and unified modelling of sustainable manufacturing capability for industrial robots in cloud manufacturing. Int J Adv Manuf Technol 93(5):2753–2771

    Article  Google Scholar 

  14. Klimchik A, Ambiehl A, Garnier S (2017) Efficiency evaluation of robots in machining applications using industrial performance measure. Rob Comp Integr Manuf 48:12–29

    Article  Google Scholar 

  15. Lekurwale RR, Akarte MM, Raut DN (2015) Framework to evaluate manufacturing capability using analytical hierarchy process. Int J Adv Manuf Technol 76(1–4):565–576

    Article  Google Scholar 

  16. Wang JJ, Miao ZH, Cui FB, Liu HC (2018) Robot evaluation and selection with entropy-based combination weighting and cloud TODIM approach. Entropy 20(5):349–366

    Article  Google Scholar 

  17. Garetti M, Taisch M (2012) Sustainable manufacturing: trends and research challenges. Prod Plan Contr 23(2–3):83–104

    Article  Google Scholar 

  18. Muhammad A, Takashi I (2018) Scheduling for sustainable manufacturing: a review. J Clean Prod 205:866–883

    Article  Google Scholar 

  19. Fisher O, Watson N, Porcu L et al (2018) Cloud manufacturing as a sustainable process manufacturing route. J Manuf Syst 47:53–68

    Article  Google Scholar 

  20. Wang L, Mohammed A, Wang XV et al (2018) Energy-efficient robot applications towards sustainable manufacturing. Int J Comput Integr Manuf 31(8):692–700

    Article  Google Scholar 

  21. Xu W, Liu Q et al (2017) Energy condition perception and big data analysis for industrial cloud robotics. Procedia CIRP 61:370–375

    Article  Google Scholar 

  22. Luck MM (2012) Efficient correlation-aware service selection. ICWS 2012: IEEE International Conference on Web Services, USA, Hawaii.

  23. ** H, Yao X, Chen Y (2015) Correlation-aware QoS modelling and manufacturing cloud service composition. J Intell Manuf 28(8):1947–1960

    Article  Google Scholar 

  24. Liu J, Chen Y et al (2018) An approach for service composition optimisation considering service correlation via a parallel max-min ant system based on the case library. Int J Comput Integr Manuf 31(12):1174–1188

    Article  Google Scholar 

  25. Li D, Ye D, Gao N, Wang S (2019) Service selection with qos correlations in distributed service-based systems. IEEE Access 7:88718–88732

    Article  Google Scholar 

  26. Kootbally Z (2016) Industrial robot capability models for agile manufacturing. Industr Rob Int J 43(5):481–494

    Article  Google Scholar 

  27. Zhang Z, Xu W, Liu Q, et al. (2017) Dynamic manufacturing capability assessment of industrial robots based on feedback information in cloud manufacturing. ASME 2017 12th International Manufacturing Science and Engineering Conference 2017, USA, Los Angeles

  28. Huang D (2019) A supplier selection method based on AHP. J Phys 1176:042055

    Google Scholar 

  29. Chen L, Yang C, Huang W, et al. (2016) Selecting training method of a rehabilitation robot based on fuzzy comprehensive evaluation. Proceedings of the 5th International Conference on Mechanical Engineering, Materials and Energy, China, Hong Kong

  30. Li Y, Dong S (2015) Study on supplier selection of manufacturing in lean closed-loop supply chain. Proceedings of the Ninth International Conference on Management Science and Engineering Management, Germany, Berlin

  31. Rashid T, Beg I, Husnine SM (2014) Robot selection by using generalized interval-valued fuzzy numbers with TOPSIS. Appl Soft Comput 21:462–468

    Article  Google Scholar 

  32. Cheng H, Xu W, Ai Q et al (2017) Manufacturing capability assessment for human-robot collaborative disassembly based on multi-data fusion. Proc Manuf 10:26–36

    Google Scholar 

  33. Sakthivel G et al (2018) Application of failure mode and effect analysis in manufacturing industry - an integrated approach with FAHP-fuzzy TOPSIS and FAHP-fuzzy VIKOR. Int J Prod Qual Manag 24(3):398–423

    Google Scholar 

  34. Wang S, Zhang X, Xu W, Liu A, Zhou Z, Pham D (2018) Energy-efficient concurrent assessment of industrial robot operation based on association rules in manufacturing. 2018 IEEE 15th International Conference on Networking, Sensing and Control (ICNSC), China, Zhuhai

  35. **e L, Jiang X, Xu W, Wei Q, Li R, Zhou Z (2015) Dynamic assessment of sustainable manufacturing capability for CNC machining systems in cloud manufacturing. Adv Prod Manag Syst 460:396–403

    Google Scholar 

  36. Zhang Y, Zhao T, Zhang Z et al (2017) Modeling and dynamic assessment on sustainable development of drainage enterprise: application of a coupled system dynamics - comprehensive assessment model. J Clean Prod 141:157–167

    Article  Google Scholar 

  37. Tao F, Zhao D, Yefa H, Zhou Z (2010) Correlation-aware resource service composition and optimal-selection in manufacturing grid. Eur J Oper Res 201(1):129–143

    Article  MATH  Google Scholar 

  38. Ma W, Wang Z, Zhao Y et al (2016) Optimizing services composition in cloud manufacturing based on improved ant colony algorithm. Comput Integr Manuf Syst 22(1):113–121

    Google Scholar 

  39. Li J, Meng X, Yang D (2015) An SSUT network security situation awareness method based on supporting extended index system. Microelectron Comp 2:15–19

    Google Scholar 

  40. Wang R, Jiang N (2018) An improved quantitative method of military training effect qualitative evaluation indexes. Comm Contr Simul 274(4):62–66

    Google Scholar 

  41. Reshef DN, Reshef YA, Finucane HK (2011) Detecting novel associations in large data sets. Science 334(6062):1518–1524

    Article  MATH  Google Scholar 

  42. Shao FB, Li KP (2016) A complex network model for analyzing railway accidents based on the maximal information coefficient. Commun Theor Phys 66(4):459–466

    Article  Google Scholar 

  43. Guo YJ, Tang HY, Qu DG (2010) Dynamic comprehensive evaluation method and its application based on minimal variability. Syst Eng Electron 32(6):1225–1228

    Google Scholar 

  44. Pham DT, Castellani M (2009) The bees algorithm: modelling foraging behaviour to solve continuous optimization problems. Proc Inst Mech Eng C J Mech Eng Sci 223(12):2919–2938

    Article  Google Scholar 

  45. Xu W, Tian S, Liu Q et al (2016) An improved discrete bees algorithm for correlation-aware service aggregation optimization in cloud manufacturing. Int J Adv Manuf Technol 84(1–4):17–28

    Article  Google Scholar 

  46. Saaty TL (2004) Fundamentals of the analytic network process—multiple networks with benefits. J Syst Sci Syst Eng 13(3):348–379

    Article  Google Scholar 

Download references

Funding

This research is supported by the National Natural Science Foundation of China (Grant No. 51775399) and the Fundamental Research Funds for the Central Universities (WUT: 2020III047).

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Authors and Affiliations

  1. School of Information Engineering, Wuhan University of Technology, Wuhan, 430070, China

    Sisi Tian, **aotong **e, Wenjun Xu, Jiayi Liu & **aomei Zhang

  2. Hubei Key Laboratory of Broadband Wireless Communication and Sensor Networks, Wuhan University of Technology, Wuhan, 430070, China

    Sisi Tian, **aotong **e, Wenjun Xu, Jiayi Liu & **aomei Zhang

Authors
  1. Sisi Tian
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  2. **aotong **e
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  3. Wenjun Xu
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  4. Jiayi Liu
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  5. **aomei Zhang
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Contributions

Sisi Tian proposed the method and conducted the case study, analyzed the results, and wrote the manuscript. **aotong **e proposed the basic idea of the method and contributed to the experimental materials. Wenjun Xu proposed the method design and experimental idea and also modified this manuscript. Jiayi Liu analyzed the data and conducted the case study. **aomei Zhang modified the structure of the manuscript and contributed to the experiment works.

Corresponding author

Correspondence to Wenjun Xu.

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The research does not involve human participants or animals. This manuscript is an extended version of a paper presented at the 49th International Conference on Computers & Industrial Engineering (CIE 49), Bei**g, China, October 18–21, 2019.

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The research does not involve human participants or animals, and the authors warrant that the manuscript fulfills the ethical standards of the journal.

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All authors give consent to publish this manuscript in the International Journal of Advanced Manufacturing Technology.

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The authors declare no competing interests.

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Tian, S., **e, X., Xu, W. et al. Dynamic assessment of sustainable manufacturing capability based on correlation relationship for industrial cloud robotics. Int J Adv Manuf Technol 124, 3113–3135 (2023). https://doi.org/10.1007/s00170-021-08024-z

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  • DOI: https://doi.org/10.1007/s00170-021-08024-z

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