Machine Learning Optimization of IN625 Coating Properties in Cold Spray Process

Abstract In this study, we focus on developing effective repair procedures by optimizing key processing parameters such as gas pressure, gas temperature, and traverse speed. To achieve this, a combination of machine learning and experimental testing was employed on cold-sprayed Ni-based superalloy (IN 625). The prepared samples were assessed for microhardness, adhesion strength, and porosity, and these experimental results were subsequently used to train a machine learning model. This model predicts material properties under varying process conditions, ensuring precision in parameter selection.