Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

Le CHEN , Xianlin WANG , Hua ZHANG , Xugang ZHANG , Binbin DAN

Front. Mech. Eng. ›› 2019, Vol. 14 ›› Issue (4) : 412 -421.

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Front. Mech. Eng. ›› 2019, Vol. 14 ›› Issue (4) : 412 -421. DOI: 10.1007/s11465-019-0551-0
RESEARCH ARTICLE
RESEARCH ARTICLE

Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis

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Abstract

A timing decision-making method for predecisional remanufacturing is presented. The method can effectively solve the uncertainty problem of remanufacturing blanks. From the perspective of reliability, this study analyzes the timing decision-making interval for predecisional remanufacturing of mechanical products during the service period and constructs an optimal timing model based on energy consumption and cost. The mapping relationships between time and energy consumption are predicted by using the characteristic values of performance degradation of products combined with the least squares support vector regression algorithm. Application of game theory reveals that when the energy consumption and cost are comprehensively optimal, this moment is the best time for predecisional remanufacturing. Used engine blades are utilized as an example to demonstrate the validity and effectiveness of the proposed method.

Keywords

predecisional remanufacturing / reliability / least squares support vector regression (LS-SVR) / game theory

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Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN. Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis. Front. Mech. Eng., 2019, 14(4): 412-421 DOI:10.1007/s11465-019-0551-0

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References

[1]

Wang X L, Luo W, Zhang H, . Energy consumption model and its simulation for manufacturing and remanufacturing systems. International Journal of Advanced Manufacturing Technology, 2016, 87(5‒8): 1557–1569

[2]

Wang H, Jiang Z G, Zhang H, . An integrated MCDM approach considering demands-matching for reverse logistics. Journal of Cleaner Production, 2019, 208: 199–210

[3]

Miao Z W, Mao H Q, Fu K, . Remanufacturing with trade-ins under carbon regulations. Computers & Operations Research, 2018, 89: 253–268

[4]

Wang X L, Chen L, Dan B B, . Evaluation of EDM process for green manufacturing. International Journal of Advanced Manufacturing Technology, 2018, 94(1‒4): 633–641

[5]

Macedo P B, Alem D, Santos M, . Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. International Journal of Advanced Manufacturing Technology, 2016, 82(5‒8): 1241–1257

[6]

Matsumoto M, Yang S S, Martinsen K, . Trends and research challenges in remanufacturing. International Journal of Precision Engineering and Manufacturing-Green Technology, 2016, 3(1): 129–142

[7]

Jiang Z G, Jiang Y, Wang Y, . A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. Journal of Intelligent Manufacturing, 2019, 30(1): 19–32

[8]

Li X, Li Y J, Cai X Q. Remanufacturing and pricing decisions with random yield and random demand. Computers & Operations Research, 2015, 54: 195–203

[9]

Jiang Z G, Wang H, Zhang H, . Value recovery options portfolio optimization for remanufacturing end of life product. Journal of Cleaner Production, 2019, 210: 419–431

[10]

Wang L L, Zhang Z M, Chen C. Evaluation model for product green design based on active remanufacturing. Applied Mechanics and Materials, 2012, 215‒216: 583–587

[11]

Chiodo J D, Ijomah W L. Use of active disassembly technology to improve remanufacturing productivity: Automotive application. International Journal of Computer Integrated Manufacturing, 2014, 27(4): 361–371

[12]

Ke Q D, Wang H, Song S X, . Timing decision-making method with life cycle analysis in predecisional remanufacturing. Journal of Mechanical Engineering, 2017, 53(11): 134–143 (in Chinese)

[13]

Song S X, Wang W, Ke Q D. Optimization design of predecisional remanufacturing based on structural function derivative coefficient. Journal of Mechanical Engineering, 2017, 53(11): 175–183 (in Chinese) doi:10.3901/JME.2017.11.175

[14]

Song S X, Liu M, Liu G F, . Theories and design methods for proactive remanufacturing of modern products. Journal of Mechanical Engineering, 2016, 52(7): 133–141 (in Chinese)

[15]

Song S X, Liu M, Ke Q D, . Proactive remanufacturing timing determination method based on residual strength. International Journal of Production Research, 2015, 53(17): 5193–5206

[16]

Ijomah W L, Chiodo J D. Application of active disassembly to extend profitable remanufacturing in small electrical and electronic products. International Journal of Sustainable Engineering, 2010, 3(4): 246–257

[17]

Kharoufeh J P, Cox S M, Oxley M E. Reliability of manufacturing equipment in complex environments. Annals of Operations Research, 2013, 209(1): 231–254

[18]

Saghafi A, Mirhabibi A R, Yari G H. Improved linear regression method for estimating Weibull parameters. Theoretical and Applied Fracture Mechanics, 2009, 52(3): 180–182

[19]

Liu T, Huang H H, Liu Z F, . Product life cycle energy consumption analysis method considering remanufacturing. Applied Mechanics and Materials, 2011, 55(57‒57): 729–736

[20]

Schau M, Traverso M, Lehmann A, . Life cycle costing in sustainability assessment-a cause study of remanufactured alternators. Sustainability, 2011, 3(11): 2268–2288

[21]

Chalkiadakis G, Elkind E, Wooldridge M. Cooperative game theory: Basic concepts and computational challenges. IEEE Intelligent Systems, 2012, 27(3): 86–90

[22]

Zhao Y P, Sun J G. Improved scheme to accelerate sparse least squares support vector regression. Journal of Systems Engineering and Electronics, 2010, 21(2): 312–317

[23]

Wang H, Jiang Z G, Zhang X G, . A fault feature characterization based method for remanufacturing process planning optimization. Journal of Cleaner Production, 2017, 161: 708–719

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