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Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling

Author

Listed:
  • Aqib Mashood Khan

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Muhammad Jamil

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Konstantinos Salonitis

    (Manufacturing Department, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK)

  • Shoaib Sarfraz

    (Manufacturing Department, School of Aerospace, Transport and Manufacturing, Cranfield University, Cranfield, Bedfordshire, MK43 0AL, UK)

  • Wei Zhao

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Ning He

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

  • Mozammel Mia

    (Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh)

  • GuoLong Zhao

    (College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China)

Abstract

Considering the significance of improving the energy efficiency, surface quality and material removal quantity of machining processes, the present study is conducted in the form of an experimental investigation and a multi-objective optimization. The experiments were conducted by face milling AISI 1045 steel on a Computer Numerical Controlled (CNC) milling machine using a carbide cutting tool. The Cu-nano-fluid, dispersed in distilled water, was impinged in small quantity cooling lubrication (SQCL) spray applied to the cutting zone. The data of surface roughness and active cutting energy were measured while the material removal rate was calculated. A multi-objective optimization was performed by the integration of the Taguchi method, Grey Relational Analysis (GRA), and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The optimum results calculated were a cutting speed of 1200 rev/min, a feed rate of 320 mm/min, a depth of cut of 0.5 mm, and a width of cut of 15 mm. It was also endowed with a 20.7% reduction in energy consumption. Furthermore, the use of SQCL promoted sustainable manufacturing. The novelty of the work is in reducing energy consumption under nano fluid assisted machining while paying adequate attention to material removal quantity and the product’s surface quality.

Suggested Citation

  • Aqib Mashood Khan & Muhammad Jamil & Konstantinos Salonitis & Shoaib Sarfraz & Wei Zhao & Ning He & Mozammel Mia & GuoLong Zhao, 2019. "Multi-Objective Optimization of Energy Consumption and Surface Quality in Nanofluid SQCL Assisted Face Milling," Energies, MDPI, vol. 12(4), pages 1-22, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:710-:d:208048
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    Citations

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    Cited by:

    1. Konstantinos Salonitis, 2020. "Energy Efficiency of Manufacturing Processes and Systems—An Introduction," Energies, MDPI, vol. 13(11), pages 1-5, June.
    2. Lijun Song & Jing Shi & Anda Pan & Jie Yang & Jun Xie, 2020. "A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption," Energies, MDPI, vol. 13(10), pages 1-18, May.
    3. Aqib Mashood Khan & Saqib Anwar & Munish Kumar Gupta & Abdullah Alfaify & Saqib Hasnain & Muhammad Jamil & Mozammel Mia & Danil Yurievich Pimenov, 2020. "Energy-Based Novel Quantifiable Sustainability Value Assessment Method for Machining Processes," Energies, MDPI, vol. 13(22), pages 1-24, November.
    4. 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).
    5. Helena Bulińska-Stangrecka & Anna Bagieńska, 2021. "Culture-Based Green Workplace Practices as a Means of Conserving Energy and Other Natural Resources in the Manufacturing Sector," Energies, MDPI, vol. 14(19), pages 1-21, October.
    6. Pimenov, Danil Yu & Mia, Mozammel & Gupta, Munish K. & Machado, Álisson R. & Pintaude, Giuseppe & Unune, Deepak Rajendra & Khanna, Navneet & Khan, Aqib Mashood & Tomaz, Ítalo & Wojciechowski, Szymon &, 2022. "Resource saving by optimization and machining environments for sustainable manufacturing: A review and future prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    7. Radosław Depczyński & Jim Secka & Katarzyna Cheba & Carlotta D’Alessandro & Katarzyna Szopik-Depczyńska, 2023. "Decision-Making Approach in Sustainability Assessment in Steel Manufacturing Companies—Systematic Literature Review," Sustainability, MDPI, vol. 15(15), pages 1-15, July.

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