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  4. Vol 9, Issue 3, 2025

Industry-Education Integration Case for AI+ Practical Teaching: Machine Tool Vibration Signal Recognition

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DOI: 10.23977/acss.2025.090312 | Downloads: 11 | Views: 535

Author(s)

Chunhua Feng 1, Jiaqi Chen 1

Affiliation(s)

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

Corresponding Author

Chunhua Feng

ABSTRACT

With the continuous and rapid development of artificial intelligence (AI) technologies, educators are increasingly faced with the pressing challenge of how to effectively incorporate AI into professional instruction. Using the course "Mechanical Testing Technology" as an example, this study investigates how AI techniques can be applied to analyze vibration signals from machine tools, adopting an approach that integrates academic instruction with industry practices. Vibration signals often display nonlinear and time-dependent behaviors due to multiple variables such as tool degradation, workpiece material differences, and variations in cutting conditions. In such intricate environments, artificial intelligence shows considerable promise. This study emphasizes key processes including the real-time collection, filtering, and noise reduction of vibration data, along with the evaluation of machine tool vibration conditions using both time-domain and frequency-domain analytical methods. It not only confirms the effectiveness of AI-based approaches in recognizing vibration patterns in machine tools but also provides valuable insights and practical references for future research and applications in this area.

KEYWORDS

Industry-Education Integration, Vibration Signal Recognition, AI

CITE THIS PAPER

Chunhua Feng, Jiaqi Chen, Industry-Education Integration Case for AI+ Practical Teaching: Machine Tool Vibration Signal Recognition. Advances in Computer, Signals and Systems (2025) Vol. 9: 98-103. DOI: http://dx.doi.org/10.23977/acss.2025.090312.

REFERENCES

[1] Mohanraj, T., Shankar, S., Rajasekar, R., Sakthivel, N. R., & Pramanik, A. (2020). Tool condition monitoring techniques in milling process—A review. Journal of Materials Research and Technology, 9(1), 1032-1042.
[2] Hassan, I. U., Panduru, K., & Walsh, J. (2024). An in-depth study of vibration sensors for condition monitoring. Sensors, 24(3), 740.
[3] Chen, H. Y., & Lee, C. H. (2021). Deep learning approach for vibration signals applications. Sensors, 21(11), 3929.
[4] Umar, M., Siddique, M. F., Ullah, N., & Kim, J. M. (2024). Milling machine fault diagnosis using acoustic emission and hybrid deep learning with feature optimization. Applied Sciences, 14(22), 10404.

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