Optimal maintenance control of machine tools for energy efficient manufacturing

  • Weigang Xu
  • Le CaoEmail author


Performance of machine tool tends to deteriorate in the production process. This deterioration increases the processing energy consumption and leads to more defectives and corresponding energy waste. Maintenance can be taken to restore the performance of machine tool and improve the energy efficiency, which has a significant impact on the total energy consumption and productivity. This paper proposes an approach to improve the energy efficiency of the production process through scheduling the maintenance actions of the machine tool, taking into account productivity, product quality, and energy consumption. The deteriorating machine tool is modeled as a discrete-time, discrete-state Markov process. Partially observable Markov decision process (POMDP) framework is applied to develop the maintenance decision-making model, where the joint observation of processing energy consumption and quality of manufactured workpiece is used to infer the status of the machine tool. An optimal maintenance policy maximizing the total expected reward about energy efficiency over a finite horizon is obtained, which consists of a sequence of decision rules corresponding to the optimal action for each belief vector. The characteristics of the optimal policy are illustrated through a numerical example and the effects of parameters on the policy are analyzed.


Energy efficiency Machine tool Maintenance control Partially observable Markov decision process 


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  1. 1.
    Abdelaziz EA, Saidur R, Mekhilef S (2011) A review on energy saving strategies in industrial sector. Renew Sust Energ Rev 15(1):150–168CrossRefGoogle Scholar
  2. 2.
    Liu F, Wang Q, Liu G (2013) Content architecture and future trends of energy efficiency research on machining systems. J Mech Eng(49)19:87-94 (in Chinese)CrossRefGoogle Scholar
  3. 3.
    Oda Y, Mori M, Ogawa K, Nishida S, Fujishima M, Kawamura T (2012) Study of optimal cutting condition for energy efficiency improvement in ball end milling with tool-workpiece inclination. CIRP Ann Manuf Technol 61(1):119–122CrossRefGoogle Scholar
  4. 4.
    Shao H, Wang H, Zhao X (2004) A cutting power model for tool wear monitoring in milling. Int J Mach Tools Manuf 44(14):1503–1509CrossRefGoogle Scholar
  5. 5.
    Xu M (2007) Smart machining system platform for cnc milling with the integration of a power sensor and cutting model. Dissertation, University of New HampshireGoogle Scholar
  6. 6.
    Xuan Z, Lu C (2009) Analysis on tool wear and cutting force of Si3N4 diamond coated tools in high speed machining. Mach Tool Hydr 37(11):45-48 (in ChineseGoogle Scholar
  7. 7.
    Dietmair A, Verl A (2009) A generic energy consumption model for decision making and energy efficiency optimisation in manufacturing. Int J Sustain Eng 2(2):123–133CrossRefGoogle Scholar
  8. 8.
    Drake R, Yildirim MB, Twomey J, Whitman L, Ahmad J, Lodhia P(2006)Data collection framework on energy consumption in manufacturing. In: IIE Annual Conference and Exposition, Orlando, Florida, USAGoogle Scholar
  9. 9.
    Gutowski T, Dahmus J, Thiriez A (2006) Electrical energy requirements for manufacturing processes. In: 13th CIRP International Conference on Life Cycle Engineering, Leuven, BelgiumGoogle Scholar
  10. 10.
    Spaan MT, Vlassis N (2005) Perseus: randomized point-based value iteration for POMDPs. J Artif Intell Res 24:195–220CrossRefGoogle Scholar
  11. 11.
    Diaz N, Choi S, Helu M, Chen Y, Jayanathan S, Yasui Y, Kong D, Pavanaskar S, Dornfeld D (2010). Machine tool design and operation strategies for green manufacturing. In: Proceedings of 4th CIRP International Conference on High Performance Cutting, Gifu, Japan, 271–276Google Scholar
  12. 12.
    Kroll L, Blau P, Wabner M, Frieß U, Eulitz J, Klärner M (2011) Lightweight components for energy-efficient machine tools. CIRP J Manuf Sci Technol 4(2):148–160CrossRefGoogle Scholar
  13. 13.
    Rajemi MF, Mativenga PT, Aramcharoen A (2010) Sustainable machining: selection of optimum turning conditions based on minimum energy considerations. J Clean Prod 18(10):1059–1065CrossRefGoogle Scholar
  14. 14.
    Mori M, Fujishima M, Inamasu Y, Oda Y (2011) A study on energy efficiency improvement for machine tools. CIRP Ann Manuf Technol 60(1):145–148CrossRefGoogle Scholar
  15. 15.
    Hu S, Liu F, He Y, Hu T (2012) No-load energy parameter characteristics of computerized numerical control machine tool main transmission system. Comput Integr Manuf Syst 18(2):26–31Google Scholar
  16. 16.
    Mouzon G, Yildirim MB, Twomey J (2007) Operational methods for minimization of energy consumption of manufacturing equipment. Int J Prod Res 45(18–19):4247–4271CrossRefGoogle Scholar
  17. 17.
    Shi J, Liu F, Xu D, Chen G (2009) Decision model and practical method of energy-saving in nc machine tool. Chin Mech Eng 20(11):1344-1346( in Chinese)Google Scholar
  18. 18.
    Shrouf F, Ordieres-Meré J, García-Sánchez A, Ortega-Mier M (2014) Optimizing the production scheduling of a single machine to minimize total energy consumption costs. J Clean Prod 67:197–207CrossRefGoogle Scholar
  19. 19.
    Chen G, Zhang L, Arinez J, Biller S (2013) Energy-efficient production systems through schedule-based operations. IEEE Trans Autom Sci Eng 10(1):27–37CrossRefGoogle Scholar
  20. 20.
    Cao H, Tao X, Liu F (2010) An energy saving scheduling model for machine tools and its application. Mech Sci Tech 29(6):744–748 (in Chinese)Google Scholar
  21. 21.
    Xu W, Cao L (2014) Energy efficiency analysis of machine tools with periodic maintenance. Int J Prod Res 52(18):5273–5285CrossRefGoogle Scholar
  22. 22.
    Smallwood RD, Sondik EJ (1973) The optimal control of partially observable markov processes over a finite horizon. Oper Res 21(5):1071–1088CrossRefGoogle Scholar
  23. 23.
    Papakonstantinou KG, Shinozuka M (2014a) Optimum inspection and maintenance policies for corroded structures using partially observable markov decision processes and stochastic, physically based models. Probab Eng Mech 37:93–108CrossRefGoogle Scholar
  24. 24.
    Anily S, Grosfeld-Nir A (2006) An optimal lot-sizing and offline inspection policy in the case of nonrigid demand. Oper Res 54(2):311–323MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ivy JS, Pollock SM (2005) Marginally monotonic maintenance policies for a multi-state deteriorating machine with probabilistic monitoring, and silent failures. IEEE Trans Reliab 54(3):489–497CrossRefGoogle Scholar
  26. 26.
    Meola G (2007) Bayes-optimal control policy for a deteriorating machine based on continuous measurements of the manufactured parts. Dissertation, Polytechnic University of MilanGoogle Scholar
  27. 27.
    AlDurgam MM, Duffuaa SO (2009) Maximizing overall system effectiveness (OSE) for three-state, partially observable system. In: Proceedings of the third international conference on modeling, simulation and applied optimization, Sharjah, UAE, 20–22 JanuaryGoogle Scholar
  28. 28.
    AlDurgam MM, Duffuaa SO (2013) Optimal joint maintenance and operation policies to maximise overall systems effectiveness. Int J Prod Res 51(5):1319–1330CrossRefGoogle Scholar
  29. 29.
    Chiang JH, Yuan J (2001) Optimal maintenance policy for a markovian system under periodic inspection. Reliab Eng Syst Saf 71(2):165–172MathSciNetCrossRefGoogle Scholar
  30. 30.
    Ben-Zvi T, Grosfeld-Nir A (2013) Partially observed markov decision processes with binomial observations. Oper Res Lett 41(2):201–206MathSciNetCrossRefGoogle Scholar
  31. 31.
    Le MD, Tan CM (2013) Optimal maintenance strategy of deteriorating system under imperfect maintenance and inspection using mixed inspection scheduling. Reliab Eng Syst Saf 113:21–29CrossRefGoogle Scholar
  32. 32.
    Bertsekas DP, Tsitsiklis JN (2008) Introduction to probability, 2nd edn. Athena Scientific, BelmontGoogle Scholar
  33. 33.
    Xu W, Cao L (2015) Optimal tool replacement with product quality deterioration and random tool failure. Int J Prod Res 53(6):1736–1745CrossRefGoogle Scholar
  34. 34.
    Papakonstantinou KG, Shinozuka M (2014c) Planning structural inspection and maintenance policies via dynamic programming and markov processes. Part II: POMDP implementation. Reliab Eng Syst Saf 130:214–224CrossRefGoogle Scholar
  35. 35.
    Thiede S (2012) Energy efficiency in manufacturing systems. Springer, DordrechtCrossRefGoogle Scholar
  36. 36.
    Papakonstantinou KG, Shinozuka M (2014b) Planning structural inspection and maintenance policies via dynamic programming and markov processes. Part I: theory. Reliab Eng Syst Saf 130:202–213CrossRefGoogle Scholar

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© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Chengdu SIWI High-Tech Industry Company LimitedChengduChina
  2. 2.State Key Laboratory of Mechanical TransmissionChongqing UniversityChongqingChina

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