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July 12, 2026Engineering Computations

Prediction of cutting forces and surface roughness in hard turning process of 42CrMo4 steel using machine learning

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Authors

MPMirza PašićAZAleksandar ZivkovicKMKenan Muhamedagić

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Overview

Randomized trial demonstrates reliable predictions of cutting forces and surface roughness in hard turning, indicating effective ML model use.

Key Points

  • This research aims to develop machine learning models to predict cutting forces and surface roughness during the hard turning process of 42CrMo4 steel.
  • Full factorial experimental design with four input parameters: cutting speed, depth of cut, feed, and insert radius.
  • Applied backward linear regression, random forest, and XGBoost models for predictions.
  • Utilized five-fold cross-validation for model reliability assessments.
  • XGBoost model achieved better stability and predictive consistency compared to linear regression and random forest models.
  • Demonstrated the capability of different ML methods in predicting cutting performance with high reliability.
  • Variable influence was assessed through permutation feature importance and statistical significance testing.

Cite This Study

Pašić et al. (2026) studied this question.

synapsesocial.com/papers/6a5332f94f7abc118adede89https://doi.org/10.1108/ec-11-2025-1395
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A Hybrid Regression and Machine Learning-Based Multi-Output Predictive Modeling of Cutting Forces and Surface Roughness in Rotational Turning of C45 Steel2026
  2. 2Integrated Modeling and Multi-Criteria Analysis of the Turning Process of 42CrMo4 Steel Using RSM, SVR with OFAT, and MCDM Techniques2026 · 1 citations
  3. 3Predicting the Resultant Cutting Force in Hard Turning Using Machine Learning Techniques2025 · 2 citations
  4. 4Influence of turning parameters on residual stresses and roughness of 42CrMo4 + QT2024 · 4 citations
  5. 5Comparative Experimental Study of Cutting Forces and Surface Roughness in Tangential Turning of 42CrMo4 Low-Alloy Steel and X5CrNi18-10 Austenitic Stainless Steel from a Sustainability Perspective2026