Publication: AI-based optimization of cutting parameters for tool wear and surface roughness of machined specimens
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Issued Date
2025-01-01
Resource Type
ISSN
19552513
eISSN
19552505
Scopus ID
2-s2.0-105019603732
Journal Title
International Journal on Interactive Design and Manufacturing
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SCOPUS
Bibliographic Citation
International Journal on Interactive Design and Manufacturing (2025)
Suggested Citation
Wiangkham A., Timtong A., Kaewta M., Chanpariyavatevong A. AI-based optimization of cutting parameters for tool wear and surface roughness of machined specimens. International Journal on Interactive Design and Manufacturing (2025). doi:10.1007/s12008-025-02422-3 Retrieved from: https://hdl.handle.net/20.500.14740/50660
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Abstract
The high cost of cutting tools, representing approximately 20% of machining expenses, drives the need for strategies to extend tool life and reduce costs. This study integrates deep cryogenic treatment (DCT) with artificial intelligence to optimize face milling parameters for uncoated carbide inserts machining Al7075 alloy. Inserts were treated at − 196 °C for 36 h and tempered. Experiments covered various cutting conditions for both treated and untreated tools. To handle data imbalance, the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN) was applied before training an Extreme Gradient Boosting (XGBoost) model. The model achieved high accuracy, with R<sup>2</sup> values of 0.902 for tool wear and 0.994 for surface roughness. Multi-Objective Particle Swarm Optimization identified optimum parameters: 400 m/min cutting speed, 0.09 mm/rev feed rate, and DCT. Treated tools showed significantly reduced wear. X-ray diffraction revealed a cobalt phase transformation from α-Co (FCC) to ε-Co (HCP), explaining improved wear resistance. Built-up edge was the primary wear mechanism. SHapley Additive exPlanations (SHAP) analysis highlighted cutting speed as dominant for tool wear and feed rate for surface roughness.