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A comparative analysis of evolutionary techniques for material removal power prediction of milling operations

Fatima Zohra El Abdelaoui et al.

Concurrent Engineering Research and Applications2025https://doi.org/10.1177/1063293x251329705article
AJG 1
Weight
0.37

What the paper says

Optimization of cutting parameters is crucial for increasing energy efficiency in CNC milling operations. This research aims to compare the efficiency of the Genetic Algorithm (GA) optimization technique with two different selection strategies and Hybrid Simulated Annealing Genetic Algorithm (HSAGA). Each of these optimization methods is applied to three different power prediction models to evaluate how well they perform in various scenarios and to assess their robustness. The evaluation is conducted across different materials, including steel, aluminum, and ductile iron. Experimental trials were conducted using L16 and L27 Taguchi’s orthogonal arrays for GA and HSAGA, respectively. The results indicate that HSAGA, when combined with the approach based on Specific Energy Consumption (SEC) and Material Removal Rate (MRR), effectively minimizes cutting power consumption across the studied materials. This study underscores the importance of method selection and parameter optimization for enhancing CNC machining efficiency and provides insights into the most effective strategies for predicting material removal power consumption.

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https://doi.org/https://doi.org/10.1177/1063293x251329705

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@article{fatima2025,
  title        = {{A comparative analysis of evolutionary techniques for material removal power prediction of milling operations}},
  author       = {Fatima Zohra El Abdelaoui et al.},
  journal      = {Concurrent Engineering Research and Applications},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/1063293x251329705},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.