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A knowledge-based method for eco-efficiency upgrading of remanufacturing process planning

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Abstract

Eco-efficiency has been proved as one of the powerful indicators in link with environment management and economic output. As a core procedure of remanufacturing activities, upgrading the eco-efficiency of remanufacturing process planning is of great significance for maximizing the circular economic advantages of remanufacturing. With the increasing of individual differences in used parts, the remanufacturing process planning is becoming more complicated. In this paper, a knowledge-based method for remanufacturing process planning is proposed as part of the efforts in upgrading eco-efficiency which also aims to improve the efficiency of process planning and realize the inheritance and evolvability of the process planning knowledge. Based on the identification of basic attribute characteristics and damage characteristics of used parts, a similarity calculation method is presented to match the most relevant historical remanufacturing process planning knowledge from the existing knowledge base, so as to design the feasible initial process plan set. For eco-efficiency upgrading, a remanufacturing process planning model is proposed to obtain the process plan with the optimal economic and environmental performance, and then the knowledge base can be updated and expanded. Finally, a case study of remanufacturing a used worm gear is demonstrated to validate the proposed method.

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References

  1. Bin-shi X (2018) Innovation and development of remanufacturing with Chinese characteristics for a new era. China Surf Eng 31(1):1–6

    Google Scholar 

  2. Liu WW, Zhang B, Li YZ, He YM, Zhang HC (2014) An environmentally friendly approach for contaminants removal using supercritical co2 for remanufacturing industry. Appl Surf Sci 292:142–148

    Article  Google Scholar 

  3. Li C, Tang Y, Li C (2011) A GERT-based analytical method for remanufacturing process routing. IEEE Conference on Automation Science and Engineering, CASE 2011, Trieste, Italy, Aug. 24-27, 2011. IEEE

  4. Song C, Guan X, Zhao Q, Jia QS (2011) Remanufacturing planning based on constrained ordinal optimization. Front Electr Electron Eng 6(3):443–452

    Article  Google Scholar 

  5. Jiang Z, Zhang H, Sutherland JW (2011) Development of multi-criteria decision making model for remanufacturing technology portfolio selection. J Clean Prod 19(17–18):1939–1945

    Article  Google Scholar 

  6. Congbo LI (2013) Fuzzy learning system for uncertain remanufacturing process time of used components. J Mech Eng 49(15):137

    Article  Google Scholar 

  7. Jiang Z, Fan Z, Sutherland JW, Zhang H, Zhang X (2014) Development of an optimal method for remanufacturing process plan selection. Int J Adv Manuf Technol 72(9–12):1551–1558

    Article  Google Scholar 

  8. Kin STM, Ong SK, Nee AYC (2014) Remanufacturing process planning. Procedia CIRP 15:189–194

    Article  Google Scholar 

  9. Gao J, Chen X, Yilmaz O, Gindy N (2008) An integrated adaptive repair solution for complex aerospace components through geometry reconstruction. Int J Adv Manuf Technol 36(11–12):1170–1179

    Article  Google Scholar 

  10. Kuo TC (2010) Combination of case-based reasoning and analytical hierarchy process for providing intelligent decision support for product recycling strategies. Expert Syst Appl 37(8):5558–5563

    Article  Google Scholar 

  11. Veerakamolmal P, Gupta SM (2002) A case-based reasoning approach for automating disassembly process planning. J Intell Manuf 13(1):47–60

    Article  Google Scholar 

  12. Song C, Guan X, Zhao Q, Ho YC (2005) Machine learning approach for determining feasible plans of a remanufacturing system. IEEE Trans Autom Sci Eng 2(3):262–275

    Article  Google Scholar 

  13. Ghazalli Z, Atsuo M. (2009) The development of the computer aided remanufacturing system (CARES) part I: software development (phase I) and a simulation study. Proceedings of the 5th International Workshop Computational Intelligence & Applications. IEEE SMC, 181

  14. Ghazalli Z, Murata A (2011) Development of an AHP–CBR evaluation system for remanufacturing: end-of-life selection strategy. Int J Sustain Eng 4(1):2–15

    Article  Google Scholar 

  15. Zhu S, Liang Y (2006) Path planning for MIG surfacing of robot-based remanufacturing system. China Weld (Engl Ed) 15(4):59–62

    Google Scholar 

  16. Jiang Z, Jiang Y, Wang Y, Zhang H, Cao H, Tian G (2019) A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf 30(1):19–32

    Article  Google Scholar 

  17. Burkhard HD (2001) Similarity and distance in case based reasoning. Fundam Inf 47(3–4):201–215

    MATH  MathSciNet  Google Scholar 

  18. Wang H, Jiang Z, Zhang X, Wang Y, Wang Y (2017) A fault feature characterization based method for remanufacturing process planning optimization. J Clean Prod 161:708–719

    Article  Google Scholar 

  19. Ahmad I, Siddiqi MH, Fatima I, Lee S, Lee YK (2011) Weed classification based on Haar wavelet transform via k-nearest neighbor (k-NN) for real-time automatic sprayer control system. Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, ICUIMC 2011, Seoul, Republic of Korea, February 21–23, 2011. ACM

  20. Jiang Z, Ding Z, Zhang H, Cai W, Liu Y (2019) Data-driven ecological performance evaluation for remanufacturing process. Energy Convers Manag 198:111844

    Article  Google Scholar 

  21. Wang H, Jiang Z, Wang Y, Zhang H, Wang Y (2018) A two-stage optimization method for energy-saving flexible job-shop scheduling based on energy dynamic characterization. J Clean Prod 188:575–588

    Article  Google Scholar 

  22. William Diebold J (1992) Changing course: a global business perspective on development and the environment. Thromb Haemost 111(4):575–582

    Google Scholar 

  23. Quariguasi-Frota-Neto J, Bloemhof J (2012) An analysis of the eco-efficiency of remanufactured personal computers and mobile phones. Prod Oper Manag 21(1):101–114

    Article  Google Scholar 

  24. Letnes PA, Nerb IS, Aas LMS, Ellingsen PG, Kildemo M (2010) Fast and optimal broad-band stokes/mueller polarimeter design by the use of a genetic algorithm. Opt Express 18(22):23095–23103

    Article  Google Scholar 

  25. Hong Y, Zhuang YP, Sun B (2012) Combination evaluation of industry independent innovation ability based on adaptive genetic algorithm. Adv Mater Res 452-453:633–636

    Article  Google Scholar 

  26. Wang Y, Jiang Z, Hu X, Li C (2020) Optimization of reconditioning scheme for remanufacturing of used parts based on failure characteristics. Robot Comput Integr Manuf 61:101833

    Article  Google Scholar 

  27. Ding Z, Jiang Z, Zhang H, Cai W, Liu Y (2018) An integrated decision-making method for selecting machine tool guideways considering remanufacturability. Int J Comput Integr Manuf:1–12

  28. Gogate NG, Kalbar PP, Raval PM (2017) Assessment of stormwater management options in urban contexts using multiple attribute decision-making. J Clean Prod 142:2046–2059

    Article  Google Scholar 

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Funding

The work described in this paper was supported by the National Natural Science Foundation of China (Grant No. 51675388, Grant No. 51605347), China Postdoctoral Science Foundation (Grant No.2018M642935), and the Plateau Disciplines in Shanghai. These financial contributions are gratefully acknowledged.

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Authors and Affiliations

  1. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science & Technology, Wuhan, 430081, China

    Daojia Chen & Zhigang Jiang

  2. Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science & Technology, Wuhan, 430081, China

    Daojia Chen, Zhigang Jiang, Shuo Zhu & Hua Zhang

  3. Engineering Research Center for Metallurgical Automation and Detecting Technology, Wuhan University of Science & Technology, Ministry of Education, Wuhan, 430081, China

    Shuo Zhu

Authors
  1. Daojia Chen
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  2. Zhigang Jiang
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  3. Shuo Zhu
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  4. Hua Zhang
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Corresponding author

Correspondence to Shuo Zhu.

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Chen, D., Jiang, Z., Zhu, S. et al. A knowledge-based method for eco-efficiency upgrading of remanufacturing process planning. Int J Adv Manuf Technol 108, 1153–1162 (2020). https://doi.org/10.1007/s00170-020-05025-2

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  • DOI: https://doi.org/10.1007/s00170-020-05025-2

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