Journal of Production Engineering

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Vol. 29 No. 1 (2026)
Original Research Article

Experimental investigation and multi-objective optimization of energy consumption in high-efficiency milling

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  • Branislav Sredanović,
  • Đorđe Čiča,
  • Jelena Marković,
  • Dejan Vujasin
Branislav Sredanović
Faculty of Mechanical Engineering, University of Banja Luka, Vojvode Stepe Stepanovića 71, 78000 Banja Luka, Bosnia and Herzegovina
Đorđe Čiča
Faculty of Mechanical Engineering, University of Banja Luka, Vojvode Stepe Stepanovića 71, 78000 Banja Luka, Bosnia and Herzegovina
Jelena Marković
Faculty of Mechanical Engineering, University of Banja Luka, Vojvode Stepe Stepanovića 71, 78000 Banja Luka, Bosnia and Herzegovina
Dejan Vujasin
Faculty of Mechanical Engineering, University of Banja Luka, Vojvode Stepe Stepanovića 71, 78000 Banja Luka, Bosnia and Herzegovina
DOI: https://doi.org/10.24867/JPE-2026-01-038

Published 2026-06-15

abstract views: 40 // FULL TEXT ARTICLE: 0


Keywords

  • High-efficiency milling,
  • Energy consumption,
  • Sustainable manufacturing,
  • Optimization,
  • Aluminium machining

How to Cite

Sredanović, B., Čiča, Đorđe, Marković, J., & Vujasin, D. (2026). Experimental investigation and multi-objective optimization of energy consumption in high-efficiency milling. Journal of Production Engineering, 29(1), 38–43. https://doi.org/10.24867/JPE-2026-01-038

Copyright (c) 2026 Journal of Production Engineering

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

The increasing demand for sustainable manufacturing and energy-efficient production systems has emphasized the importance of reducing energy consumption while maintaining high productivity in machining processes. This study investigates the relationship between cutting parameters, energy consumption and material removal rate during high-efficiency milling of aluminium. Experimental investigations were conducted on a three-axis CNC machining centre equipped with an electrical energy monitoring system. The influence of milling width and depth of cut on energy consumption was analysed using a full factorial experimental design, while spindle speed and feed rate were kept constant. The obtained results were statistically evaluated using analysis of variance (ANOVA), and an empirical regression model for predicting energy consumption was developed and validated. Furthermore, a multi-objective optimization procedure was performed to simultaneously maximize productivity and minimize energy consumption. The results revealed that increasing the cutting parameters leads to higher energy demand but also significantly improves the material removal rate. The optimal machining conditions were identified at a depth of cut of 30 mm and a milling width of 1.22 mm, resulting in a material removal rate of 22,027.92 mm³/min and an energy consumption of 0.002185 kWh. The proposed methodology provides practical guidelines for improving the energy efficiency and sustainability of high-efficiency milling processes and may support future implementation within smart manufacturing and Industry 4.0 environments.

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