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Comparison and Optimization of Generalized Stamping Machine Fault Diagnosis Models Using Various Transfer Learning

Po-Wen Hwang1, Yuan-Jen Chang1,2, Hsieh-Chih Tsai2

  • 1Department of Aerospace and Systems Engineering, Feng Chia University, Taichung City 407102, Taiwan.

Sensors (Basel, Switzerland)
|April 28, 2025
PubMed
Summary

A new generalized artificial intelligence (AI) model accurately predicts stamping machine faults using vibration data. This AI approach enables predictive maintenance across diverse stamping equipment, enhancing manufacturing quality and efficiency.

Keywords:
data analysisdeep learninggeneralized modelpredictive maintenancestamping production

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Area of Science:

  • Manufacturing Engineering
  • Artificial Intelligence
  • Machine Learning

Background:

  • Stamping operations require precise total clearance for quality and equipment longevity.
  • Diverse stamping machine designs hinder the development of universal fault diagnosis models.
  • Real-time monitoring of total clearance is crucial for process control and fault detection.

Purpose of the Study:

  • To develop a generalized fault diagnosis model for stamping machines.
  • To enable effective process control and predictive maintenance across different machine types.
  • To overcome the challenge of machine-specific model development in smart manufacturing.

Main Methods:

  • Utilized vibration data from accelerometers on four distinct stamping machine models (OCP-110, G2-110, G2-160, ST1-110).
  • Evaluated four deep learning architectures: CNN, CNN-Res, VGG16, and ResNet50, with fine-tuning strategies.
  • Developed a generalized fault diagnosis model applicable across multiple stamping machine types.

Main Results:

  • The generalized fault diagnosis model achieved average accuracy, recall, and F1 scores exceeding 99%.
  • Demonstrated high efficacy and reliability in real-world stamping fault diagnosis.
  • Validated the model's performance across OCP-110, G2-110, G2-160, and ST1-110 machine models.

Conclusions:

  • The developed generalized model effectively diagnoses faults in diverse stamping machines.
  • This AI-driven approach streamlines predictive maintenance deployment in smart manufacturing.
  • The model shows potential for scalability to more machine types and operational conditions.