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Integrated assessment and optimization of dual environment and production drivers in grinding

Author

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  • Wang, Jinling
  • Tian, Yebing
  • Hu, Xintao
  • Han, Jinguo
  • Liu, Bing

Abstract

Grinding is a common machining process for efficiently manufacturing high-end products. However, abundant electrical energy is wasted in grinding due to its inefficient use, thus causing economic loss as well as environmental pollution. Although green grinders are evolving, their high investment cost prevents the majority of discrete manufacturers from using them immediately. Optimization of the grinding process is a straightforward and cost-effective alternative, but proper reference parameters are not known for saving energy and reducing pollution. With an attempt to maintain a balance between production and environment, novel parametric models are established for assessment and multi-objective optimization by considering various issues, such as the efficiency of time, the surface quality of product, consumption of energy, recycling and replacement of coolant, exposure of noise hazard, mass of produced dust, and cost of environmental factors. A popular multi-objective optimizer, namely the non-dominated sorting genetic algorithm-II (NSGA-II), is applied for directly obtaining the Pareto front containing the set of optimal trade-off solutions. The experimental results obtained in the case of ceramic grinding show 90% accuracy of the proposed model. An integrated environmental improvement of 200% is achieved by a minor loss of 7.41% within the required range of surface quality.

Suggested Citation

  • Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:energy:v:272:y:2023:i:c:s0360544223004401
    DOI: 10.1016/j.energy.2023.127046
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    References listed on IDEAS

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    1. Xiao, Qinge & Li, Congbo & Tang, Ying & Li, Lingling & Li, Li, 2019. "A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning," Energy, Elsevier, vol. 166(C), pages 142-156.
    2. Lotfan, S. & Ghiasi, R. Akbarpour & Fallah, M. & Sadeghi, M.H., 2016. "ANN-based modeling and reducing dual-fuel engine’s challenging emissions by multi-objective evolutionary algorithm NSGA-II," Applied Energy, Elsevier, vol. 175(C), pages 91-99.
    3. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
    4. Schudeleit, Timo & Züst, Simon & Weiss, Lukas & Wegener, Konrad, 2016. "The Total Energy Efficiency Index for machine tools," Energy, Elsevier, vol. 102(C), pages 682-693.
    5. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    6. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
    7. Liu, Wei & Li, Li & Cai, Wei & Li, Congbo & Li, Lingling & Chen, Xingzheng & Sutherland, John W., 2020. "Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory," Energy, Elsevier, vol. 212(C).
    8. Liu, Peiji & Liu, Fei & Qiu, Hang, 2017. "A novel approach for acquiring the real-time energy efficiency of machine tools," Energy, Elsevier, vol. 121(C), pages 524-532.
    9. Khan, A.M. & Liang, L. & Mia, M. & Gupta, M.K. & Wei, Z. & Jamil, M. & Ning, H., 2021. "Development of process performance simulator (PPS) and parametric optimization for sustainable machining considering carbon emission, cost and energy aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    Full references (including those not matched with items on IDEAS)

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