Surface Roughness Prediction in Interrupted Turning of TQM-shaped Workpiece Using Deep Neural Network

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Key Points

This research aims to analyze the impact of cutting parameters on surface roughness in interrupted turning processes using a deep neural network model.
Analyzed effects of cutting speed, feed rate, and cutting depth on surface roughness for SCM440 material.
Developed a deep learning-based DNN model for predicting surface roughness accuracy.
Utilized L<sub>9</sub>(3³) Taguchi design for optimal cutting conditions.
Optimal cutting conditions were identified as cutting speed 140 m/min, feed 0.1 mm/rev, and depth 1.0 mm.
Deep neural network model achieved R² of 0.984 and RMSE of 0.145, indicating high predictive accuracy.
The model is expected to effectively assist in quality control and optimal cutting condition selection in interrupted turning.

Analyzes the impact of cutting parameters on surface roughness using a deep learning model, indicating improved predictive performance.

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Lee et al. (2026) studied this question.

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