A Review on Recent Advances in the Energy Efficiency of Machining Processes for Sustainability

Shailendra Pawanr and Kapil Gupta ()
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Shailendra Pawanr: Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Doornfontein Campus, Johannesburg 2028, South Africa
Kapil Gupta: Department of Mechanical and Industrial Engineering Technology, University of Johannesburg, Doornfontein Campus, Johannesburg 2028, South Africa

Energies, 2024, vol. 17, issue 15, 1-21

Abstract: The pursuit of energy efficiency in machining processes is a critical aspect of sustainable manufacturing. A significant portion of global energy consumption is by the industrial sector; thus, improving the energy efficiency of machining processes can lead to substantial environmental and economic benefits. The present study reviews the recent advancement made for improving the energy efficiency of machining processes. First the energy consumption of the machining processes was explored and then the key areas and developments in their energy consumption modeling were identified. Following this, the review explores various strategies for achieving energy savings in machining. These strategies include energy-efficient machine tools, the accurate modeling of the energy consumption of machining processes, the implementation of optimization techniques and the application of artificial intelligence (AI). Additionally, the review highlights the potential of AI in further reducing energy consumption within machining operations and achieving energy efficiency. A review of these energy-saving strategies in machining processes reveals impressive potential for significant reductions in energy consumption: energy-efficient design can achieve up to a 45% reduction, optimizing cutting parameters may minimize consumption by around 40%, optimizing tool paths can reduce consumption by approximately 50%, optimizing non-cutting energy consumption and sequencing can lead to savings of about 30% and employing AI shows promising energy efficiency improvements of around 20%. Overall, the present review offers valuable insights into recent advancements in making machining processes more energy-efficient. It identifies key areas where significant energy savings can be achieved.

Keywords: artificial intelligence; energy efficiency; machine tools; machining processes; optimization; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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