Abstract
The uncertainty recognition, quantification, and optimization of reassembly (remanufacturing assembly) are crucial to improve the precision, quality, and stability of the assembly of remanufactured products. In this paper, we summarize the uncertainties based on the analysis of reassembly features. An uncertainty measure model using information-entropy theory is then established. This model characterizes the uncertainty of remanufactured parts, reused parts, and reassembly quantitatively. An online quality prediction method for reassembly process is then proposed. The method can effectively estimate the quality of remanufactured products using recursive least squares algorithm, which combines real-time data with tremendous amount of history data from the reassembly process. This method can also maximize the use of tremendous amount of history data and improve the accuracy of reassembly by mathematical statistics and examples. Moreover, it can reduce the cost of rework and improve the quality of remanufactured products. Results show that the proposed method achieved good application results with high accuracy and computation efficiency.
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References
Xu B-S (2010) State of the art and future development in remanufacturing engineering. Cailiao Rechuli Xuebao 31:10–14
Mitra S (2016) Models to explore remanufacturing as a competitive strategy under duopoly. Omega-Int J Manag Sci 59:215–227. https://doi.org/10.1016/j.omega.2015.06.009
Gonsch J (2015) A note on a model to evaluate acquisition price and quantity of used products for remanufacturing. Int J Prod Econ 169:277–284. https://doi.org/10.1016/j.ijpe.2015.07.013
Neto JQF, Bloemhof J, Corbett C (2016) Market prices of remanufactured, used and new items: Evidence from eBay. Int J Prod Econ 171:371–380. https://doi.org/10.1016/j.ijpe.2015 02.006
Debo LG, Toktay LB, Van Wassenhove LN (2005) Market segmentation and product technology selection for remanufacturable products. Manag Sci 51:1193–1205. https://doi.org/10.1287/mnsc.1050.0369
Liu MZ et al (2016) Assembly process control method for remanufactured parts with variable quality grades. Int J Adv Manuf Technol 85:1471–1481. https://doi.org/10.1007/s00170-015-8026-x
Xu B, Dong S, Shi P (2013) States and prospects of china characterised quality guarantee technology system for remanufactured parts. J Mech Eng 49:84–90
Du YB et al (2012) An integrated method for evaluating the remanufacturability of used machine tool. J Clean Prod 20:82–91. https://doi.org/10.1016/j.jclepro.2011.08.016
Song SX et al (2015) Proactive remanufacturing timing determination method based on residual strength. Int J Prod Res 53:5193–5206. https://doi.org/10.1080/00207543.2015.1012599
Panagiotidou S et al (2017) Joint optimization of manufacturing/remanufacturing lot sizes under imperfect information on returns quality. Eur J Oper Res 258: 537–551. https://doi.org/10.1016/j.ejor.2016.08.044
Peng ST et al (2016) Comparative life cycle assessment of remanufacturing cleaning technologies. J Clean Prod 137:475–489. https://doi.org/10.1016/j.jclepro.2016.07.120
Li A-D, He Z, Zhang Y (2016) Bi-objective variable selection for key quality characteristics selection based on a modified NSGA-II and the ideal point method. Comput Ind 82:95–103. https://doi.org/10.1016/j.compind.2016.05.008
Ferguson M et al (2009) The value of quality grading in remanufacturing. Prod Oper Manag 18:300–314. https://doi.org/10.1111/j.1937-5956.2009 01033.x
Niu T-X (2011) Optimal model for remanufacturing tolerance design and its application. Jisuanji Jicheng Zhizao Xitong 17:232–238
Zhou J et al (2012) A quality evaluation model of reuse parts and its management system development for end-of-life wheel loaders. J Clean Prod 35:239–249. https://doi.org/10.1016/j.jclepro.2012. 05.037
Tang X, Mao H, Li X (2011) Effect of quality uncertainty of parts on performance of reprocessing system in remanufacturing environment. J Southeast Univ (English Ed) 27:92–95
Ge M, Liu C, Liu M (2014) The online quality control methods for the assembling of remanufactured engines’ cylinder block and cover under uncertainty. Int J Adv Manuf Technol 74:225–233. https://doi.org/10.1007/s00170-014-5971-8
Shen WL et al (2015) The quality control method for remanufacturing assembly based on the Jacobian-torsor model. Int J Adv Manuf Technol 81:253–261. https://doi.org/10.1007/s00170-015-7194-z
Liu MZ et al (2016) The online quality control method for reassembly based on state space model. J Clean Prod 137:644–651. https://doi.org/10.1016/j.jclepro.2016.07.116
Jin XN et al (2013) Assembly strategies for remanufacturing systems with variable quality returns. IEEE Trans Autom Sci Eng 10:76–85. https://doi.org/10.1109/TASE.2012.2217741
Liu M et al (2016) Study on a tolerance grading allocation method under uncertainty and quality oriented for remanufactured parts. Int J Adv Manuf Technol 87:1–8. https://doi.org/10.1007/s00170-013-4826-z
Zhang Y, Yin Y, Yang M (2010) A new selective assembly approach for remanufacturing of mating parts. In: 40th international conference on IEEE computers and industrial engineering (CIE), pp 1–6. https://doi.org/10.1109/ICCIE.2010.5668287
Su C, Xu AJ (2014) Buffer allocation for hybrid manufacturing/remanufacturing system considering quality grading. Int J Prod Res 52:1269–1284. https://doi.org/10.1080/00207543.2013.828165
Cai XQ et al (2014) Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels. Eur J Oper Res 233:374–382. https://doi.org/10.1016/j.ejor.2013.07.017
Ondemir O, Gupta SM (2014) Quality management in product recovery using the Internet of Things: an optimization approach. Comput Ind 65:491–504. https://doi.org/10.1016/j.compind.2013.11.006
Zhang HC (1997) Advanced tolerancing techniques, vol 5. Wiley
Liu M-Z et al (2014) Dynamic assembly process quality control system for mechanical products remanufacturing. Jisuanji Jicheng Zhizao Xitong 20:817–824
Benesty J, Paleologu C, Ciochina S (2011) Regularization of the RLS Algorithm. IEICE Trans Fundam Electron Commun Comput Sci E94A:1628–1629
Eksioglu EM, Tanc AK (2011) RLS algorithm with convex regularization. IEEE Signal Process Lett 18:470–473. https://doi.org/10.1109/LSP.2011.2159373
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This work is supported by the National Science Foundation for Young Scientists of China (Grant No. 51705282).
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Ma, J., Wang, Q. & Zhao, Z. RLS-based quality control method for reassembly under uncertainty. Prod. Eng. Res. Devel. 12, 481–490 (2018). https://doi.org/10.1007/s11740-018-0826-z
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DOI: https://doi.org/10.1007/s11740-018-0826-z