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Wear Mechanism Classification Using Artificial Intelligence.

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Artificial intelligence (AI) offers a data-driven alternative for classifying wear mechanisms from scanning electron microscopy (SEM) images. This study investigates the feasibility of AI models for automated wear analysis, reducing reliance on expert interpretation.

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Tribology

Background:

  • Understanding wear mechanisms is crucial for predicting component lifetime and performance under tribological loading.
  • Current methods rely on manual analysis of scanning electron microscopy (SEM) images by experts, which is time-consuming and subjective.
  • Automated classification of wear mechanisms could significantly improve efficiency and consistency.

Purpose of the Study:

  • To investigate the feasibility of using artificial intelligence (AI) for the automated classification of wear mechanisms.
  • To explore the potential of artificial neural networks (ANNs) for analyzing SEM images of wear patterns.
  • To assess the performance of AI models after hyperparameter optimization.

Main Methods:

  • Utilized artificial neural networks (ANNs) for classifying wear mechanisms based on SEM images.
  • Performed hyperparameter optimization to enhance the performance of the ANNs.
  • Trained and evaluated the AI model on a dataset of SEM images depicting various wear appearances.

Main Results:

  • Demonstrated the potential of AI-based models for automated wear mechanism classification.
  • Achieved accurate classification of wear mechanisms directly from SEM image data.
  • Showcased the feasibility of a data-driven approach, minimizing the need for expert knowledge.

Conclusions:

  • AI, specifically ANNs, presents a viable and promising alternative to manual expert analysis for wear mechanism identification.
  • Automated classification using AI can lead to more objective and efficient wear analysis in tribology.
  • Further development of AI models can enhance the prediction of component lifetime and material performance.