Improving Temperature Estimation Models using Machine Learning Techniques
Temperature estimation models are crucial for various products manufactured by BorgWarner. These models often require manual calibration, where experts adjust parameters to ensure accuracy. However, this process can be slow and prone to errors. This thesis investigates how Machine Learning techniques can be used to improve accuracy and efficiency of temperature estimation models. Both black-box an