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

  • Lei Zhang
  • Beikun Zhang
  • Hong Bao
  • Haihong Huang
Regular Paper
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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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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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