Thermographic and Machine Learning approaches for a rapid estimation of gears bending fatigue strength

Abstract The estimation of the tooth root bending fatigue strength of gears is a topic of great interest in the field of mechanical engineering. The assessment of this mechanical property is generally conducted through the execution of a series of tests and, in many cases, a long-time experimental campaign is necessary for the bending fatigue strength evaluation. The present study aims at the estimation of the bending fatigue strength in gears by using the well-known Thermographic Method with integrated Machine Learning techniques implementing Gaussian process regression and artificial neural networks. This approach allows for the combination of a Non-Destructive, green technique with Artificial Intelligence algorithms, determining a rapid and reasonable estimation of the bending fatigue strength for gears. Among all methods, the statistical analyses confirm that all models have high accuracy. However, Gaussian process regression and deep neural networks may be superior in comparison with other methods, and their precision and reliability may be higher for advanced fatigue assessment. This tool could be helpful to cut down experimental workload with the help of Thermographic Method for the tooth root bending fatigue strength estimation, hence enabling very fast Non-Destructive evaluation of gear performance. Thermography approach combined with Machine Learning agrees sustainability by saving critical resource-intensive testing and leads to an advanced mechanical properties evaluation framework in gear systems, hence offering important alternative to the classical methods.