Prediction of residual life and critical crack length using the forward/inverse machine learning based on the configurational force fatigue model

Key Points

• Predicting residual life shows promise for improving safety and maintenance schedules in engineering applications.
• The model effectively estimated critical crack length in materials using a machine learning approach, enhancing predictive capabilities.
• Observational analysis blends forward and inverse machine learning with a configurational force fatigue model to derive valuable insights.
• These findings highlight the potential of machine learning in optimizing material life, suggesting new avenues for research in predictive modeling.