Optimization of Cutting Parameters for Minimizing Environmental Impact: Considering Energy Efficiency, Noise Emission and Economic Dimension

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Abstract

Green manufacturing is attracting significant attention from the academic and industrial world under current environmental circumstances. The purpose of the present study is to evaluate trade-offs between the main factors in green manufacturing: energy, noise and cost, through cutting parameter optimization. First, the energy consumption model is developed based on the analysis of the relationship among sub-processes, power and cutting parameters. Then numerical noise emission model, which integrates orthogonal experiment and response surface method, is proposed. Moreover, Analysis of Variance results are employed to analyze the influence of cutting parameters on noise, the results show that the depth of cut is the dominating influencing factor of noise, then the cutting cost model is presented. The multi-objective optimization model for energy saving, noise reducing and cost saving is proposed, which takes cutting speed, feed fate and depth of cut as decision variables. NSGA-II is adopted to obtain the Pareto optimal solutions. The most suitable Pareto-optimal solution is selected by a combination weighting method. A case study involving the cutting process of a CNC lathe is used to validate the proposed methodology, and the results are discussed and analyzed.

Keywords

Green manufacturing Multi-objective optimization RSM NSGA-II Pareto optimization 

Nomenclature

CNC

Computer numerical control

RSM

response surface method

ANOVA

Analysis of Variance

NSGA-II

non-dominated sorting genetic algorithm-II

AHP

analytic hierarchy process

RST

rough set theory

EC

energy consumption

ECF

energy efficiency

MOEA

multi-objective evolutionary algorithm

Gen

evolutionary algebras

MRR

material removal rate

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References

  1. 1.
    National Bureau of Statistics of China, “China Statistical Yearbook 2010,” 2010.Google Scholar
  2. 2.
    Herzog, T., “World Greenhouse Gas Emissions in 2005,” World Resources Institute, 2009.Google Scholar
  3. 3.
    Denkena, B., Helmecke, P., and Hulsemeyer, L., “Energy Efficient Machining of Ti-6Al-4V,” CIRP Annals, Vol. 64, No. 1, pp. 61–64, 2015.CrossRefGoogle Scholar
  4. 4.
    Mori, M., Fujishima, M., Inamasu, Y., and Oda, Y., “A Study on Energy Efficiency Improvement for Machine Tools,” CIRP Annals-Manufacturing Technology, Vol. 60, No. 1, pp. 145–148, 2011.CrossRefGoogle Scholar
  5. 5.
    Li, C., Xiao, Q., Tang, Y., and Li, L., “A Method Integrating Taguchi RSM and MOPSO to CNC Machining Parameters Optimization for Energy Saving,” Journal of Cleaner Production, Vol. 135, pp. 263–275, 2016.CrossRefGoogle Scholar
  6. 6.
    Camposeco-Negrete, C., “Optimization of Cutting Parameters for Minimizing Energy Consumption in Turning of AISI 6061 T6 Using Taguchi Methodology and ANOVA,” Journal of Cleaner Production, Vol. 53, pp. 195–203, 2013.CrossRefGoogle Scholar
  7. 7.
    Bhushan, R. K., “Optimization of Cutting Parameters for Minimizing Power Consumption and Maximizing Tool Life during Machining of Al Alloy SiC Particle Composites,” Journal of Cleaner Production, Vol. 39, pp. 242–254, 2013.CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Li, X., Gao, L., and Zhang, G., “Dynamic Rescheduling in FMS that is Simultaneously Considering Energy Consumption and Schedule Efficiency,” The International Journal of Advanced Manufacturing Technology, Vol. 87, Nos. 5-8, pp. 1387–1399, 2016.CrossRefGoogle Scholar
  9. 9.
    Sharma, A., Zhao, F., and Sutherland, J. W., “Econological Scheduling of a Manufacturing Enterprise Operating under a Time-of-Use Electricity Tariff,” Journal of Cleaner Production, Vol. 108, pp. 256–270, 2015.CrossRefGoogle Scholar
  10. 10.
    Fang, K., Uhan, N., Zhao, F., and Sutherland, J. W., “A new Approach to Scheduling in Manufacturing for Power Consumption and Carbon Footprint Reduction,” Journal of Manufacturing Systems, Vol. 30, No. 4, pp. 234–240, 2011.CrossRefGoogle Scholar
  11. 11.
    Sun, Z. and Li, L., “Potential Capability Estimation for Real Time Electricity Demand Response of Sustainable Manufacturing Systems Using Markov Decision Process,” Journal of Cleaner Production, Vol. 65, pp. 184–193, 2014.CrossRefGoogle Scholar
  12. 12.
    Guo, P., “Experimental Investigation of Sound Signals during Machining Process,” M.Sc. Thesis, Jinan Shandong University, China, 2007.Google Scholar
  13. 13.
    Sampath, K., Kapoor, S. G., and DeVor, R. E., “Modeling and Prediction of Cutting Noise in the Face-Milling Process,” Journal of Manufacturing Science and Engineering, Vol. 129, No. 3, pp. 527–530, 2007.CrossRefGoogle Scholar
  14. 14.
    Bahrami, A., Willamson, H. M., and Lai, J. C., “Control of Shear Cutting Noise: Effect of Blade Profile,” Applied Acoustics, Vol. 54, No. 1, pp. 45–58, 1998.CrossRefGoogle Scholar
  15. 15.
    Bahrami, A. and Williamson, H. M., “Effect of Blade Profile on Sheet Metal Shear Noise,” The Journal of the Acoustical Society of America, Vol. 101, No. 5, Paper No. 3077, 1997.Google Scholar
  16. 16.
    Ji, C. and Liu, Z., “Numerical analysis of Aeroacoustic Noise for High-Speed Face Milling Cutters in Three Dimensional Unsteady Flow Fields,” Journal of Manufacturing Science and Engineering, Vol. 134, No. 4, Paper No. 041002, 2012.Google Scholar
  17. 17.
    Liu, Z.-J., Sun, D.-P., Lin, C.-X., Zhao, X.-Q., and Yang, Y., “Multi-Objective Optimization of the Operating Conditions in a Cutting Process Based on Low Carbon Emission Costs,” Journal of Cleaner Production, Vol. 124, pp. 266–275, 2016.CrossRefGoogle Scholar
  18. 18.
    Yoon, H.-S., Moon, J.-S., Pham, M.-Q., Lee, G.-B., and Ahn, S.-H., “Control of Machining Parameters for Energy and Cost Savings in Micro-Scale Drilling of PCBS,” Journal of Cleaner Production, Vol. 54, pp. 41–48, 2013.CrossRefGoogle Scholar
  19. 19.
    Du, Y. and Li, C., “Implementing Energy-Saving and Environmental-Benign Paradigm: Machine Tool Remanufacturing by OEMs in China,” Journal of Cleaner Production, Vol. 66, pp. 272–279, 2014.CrossRefGoogle Scholar
  20. 20.
    The Environmental Protection Tax Law of China [EB/OL], http:// www.npc.gov.cn/npc/xinwen/2016/12/25/content_2004993.htm (Accessed 8 MAR 2018)Google Scholar
  21. 21.
    Yusup, N., Zain, A. M., and Hashim, S. Z. M., “Evolutionary Techniques in Optimizing Machining Parameters: Review and Recent Applications (2007-2011),” Expert Systems with Applications, Vol. 39, No. 10, pp. 9909–9927, 2012.CrossRefGoogle Scholar
  22. 22.
    Wang, Q., Liu, F., and Wang, X., “Multi-Objective Optimization of Machining Parameters Considering Energy Consumption,” The International Journal of Advanced Manufacturing Technology, Vol. 71, Nos. 5-8, pp. 1133–1142, 2014.CrossRefGoogle Scholar
  23. 23.
    Behnamian, J., Ghomi, S. M. T. F., and Zandieh, M., “A Multi-Phase Covering Pareto-Optimal Front Method to Multi-Objective Scheduling in a Realistic Hybrid Flowshop Using a Hybrid Metaheuristic,” Expert Systems with Applications, Vol. 36, No. 8, pp. 11057–11069, 2009.CrossRefGoogle Scholar
  24. 24.
    Han-bin, W. and Xin, Y., “A New Method of Ascertai-Ning Weight Based on Analytic Hierarchy Process and Rough Set Theory,” Journal of Safety Science and Technology, Vol. 6, No. 6, pp. 155–160, 2010.Google Scholar

Copyright information

© Korean Society for Precision Engineering and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringHefei university of TechnologyHefeiChina

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