Estimation of equivalent thermal conductivity of impregnated slots in electric machines using Artificial Neural Network Surrogate Model

The accurate prediction of temperature within the slot of an electric motor stands as a crucial yet intricate task. It presents a challenge due to its computational demands, particularly when numerous iterations are requisite to identify the optimal configuration for a specific application. In response to this challenge, our study delves into the utilization of an Artificial Neural Network (ANN) as a tool to predict thermal conductivity within the motor slot with a high degree of accuracy. Our approach involves training the ANN using data derived from Finite Element Analysis (FEA)-based numerical simulations, which provide a robust foundation for modeling the thermal behavior of the motor slot. By harnessing the power of machine learning techniques embedded within the ANN, we aim to achieve a more efficient and effective means of temperature prediction compared to conventional methods. One of the key advantages of our proposed model is its ability to adapt and learn from complex and nonlinear relationships inherent in thermal conductivity estimation. This adaptability is especially beneficial in scenarios where traditional analytical models, as commonly found in existing literature, may fall short in capturing the intricacies of thermal behavior within the motor slot. Through rigorous testing and comparison with established analytical models, we demonstrate the superiority of our ANN-based approach in terms of accuracy and reliability. Our findings not only contribute to advancing the field of thermal management in electric motors but also highlight the potential of Artificial Neural Networks as a powerful tool for predictive modeling in complex engineering systems.