Predicting the Resultant Cutting Force in Hard Turning Using Machine Learning Techniques

Chahrazed Hiba  Mimoun; Kamel Haddouche; Souâd Makhfi

doi:10.48084/etasr.11759

Authors

Chahrazed Hiba Mimoun Research Laboratory of Industrial Technologies, Faculty of Applied Sciences, University of Tiaret, Algeria
Kamel Haddouche Research Laboratory of Industrial Technologies, Faculty of Applied Sciences, University of Tiaret, Algeria
Souâd Makhfi Department of Mechanical Engineering, Faculty of Applied Sciences, University of Tiaret, Algeria

Volume: 15 | Issue: 5 | Pages: 26505-26510 | October 2025 | https://doi.org/10.48084/etasr.11759

Received: 27 April 2025 | Revised: 22 June 2025 and 28 June 2025 | Accepted: 8 July 2025 | Online: 29 July 2025

Corresponding author: Kamel Haddouche

Abstract

In the machining process, the cutting force is used for several purposes, including adaptive control, online tool wear observation, and monitoring. Its modeling and computation are the main facets of metal cutting theory, recognizing that many parameters influence its value. Despite the analytical and numerical approaches developed, the current trend is to use artificial intelligence tools for prediction. In this context, machine-learning techniques are used to predict the resulting cutting force during the hard longitudinal turning of AISI 52100 steel by a cBN insert. This study used Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machines (SVM), and Gaussian Process Regression (GPR). For each model, the response is the resultant cutting force, with machining conditions such as workpiece hardness, cutting speed, feed, and depth-of-cut as inputs. The predicted results are compared with the experimental data to determine the effectiveness of the predictive models, showing that ANFIS is the most promising, offering the best performance.

Keywords:

prediction, force, hard turning, machine learning, AISI 52100, cBN insert

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