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  4. Vol 8, Issue 3, 2025

Case Analysis in AI Practice Courses: A Comparative Study of Tool Wear Prediction Methods

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DOI: 10.23977/jaip.2025.080315 | Downloads: 1 | Views: 21

Author(s)

Chunhua Feng 1, Hui Ye 1

Affiliation(s)

1 School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Chunhua Feng

ABSTRACT

Artificial intelligence is anticipated to play a crucial role in shaping the future of education and academic research. Using a mechanical testing course as a case study, this paper illustrates how AI can be effectively integrated into engineering education by examining various AI methodologies through the application of tool wear prediction. In predictive modelling, commonly used intelligent algorithms mainly come from traditional machine learning techniques, such as support vector machines (SVM) and random forests, as well as deep learning methods like long short-term memory (LSTM) and gated recurrent units (GRU). Traditional models generally rely on manual feature extraction, which provides a degree of interpretability but often falls short in capturing the dynamic characteristics of time-series data. This study assesses the performance of several deep learning algorithms in predicting tool wear. The CNN-LSTM hybrid model consistently outperformed other models across all evaluation metrics. Specifically, compared to the GRU model, it reduced RMSE by 42.07% and MAE by 52.06%, while improving R² by 4.43%. When compared to the standalone LSTM model, the CNN-LSTM model achieved a 17.72% reduction in RMSE and a 42.25% decrease in MAE, along with a 2.97% increase in R². These results indicate that the CNN-LSTM architecture successfully combines CNN's proficiency in automatic local feature extraction with LSTM's capacity to model long-term temporal dependencies, thereby providing a highly effective and accurate method for tool wear prediction.

KEYWORDS

AI, Tool Wear Prediction, CNN-LSTM

CITE THIS PAPER

Chunhua Feng, Hui Ye, Case Analysis in AI Practice Courses: A Comparative Study of Tool Wear Prediction Methods. Journal of Artificial Intelligence Practice (2025) Vol. 8: 120-125. DOI: http://dx.doi.org/10.23977/jaip.2025.080315.

REFERENCES

[1] Li B. (2012). A review of tool wear estimation using theoretical analysis and numerical simulation technologies. International Journal of Refractory Metals and Hard Materials, 35, 143-151.
[2] Li Y, Liu C, Hua J, Gao J, Maropoulos P. (2019). A novel method for accurately monitoring and predicting tool wear under varying cutting conditions based on meta-learning. CIRP Annals, 68(1), 487-490.
[3] Ghosh S, Naskar SK, Mandal NK. (2018). Estimation of residual life of a cutting tool used in a machining process. MATEC Web of Conferences (Vol. 192, p. 01017). EDP Sciences.
[4] Karandikar J, McLeay T, Turner S, Schmitz T. (2015). Tool wear monitoring using naive Bayes classifiers. The International Journal of Advanced Manufacturing Technology, 77, 1613-1626.

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