Machine learning regression tools for erosion prediction of WC-10Co4Cr thermal spray coating

Abstract

The prediction of erosion in WC-10Co4Cr thermal spray coating is predicted using regression machine learning technique. A pot tester helped to examine the erosion rate of WC-10Co4Cr thermal spray coatings. WC-10Co4Cr thermal spray powder was sprayed onto the SS316L steel. Different impingement conditions (30, 45, and 60°) were tested by using textures designed to simulate erosion. The collected data is used to construct a robust Gaussian Process Regression (GPR) model. The projected values are compared to the actual values obtained via experimentation. To further demonstrate the accuracy of the suggested model, the produced model is compared to various state-of-the-art machine learning methods. The GPR outperforms more commonplace methods of other regression techniques like decision trees, Ensemble boosted trees, and linear regression models. The erosion of coated and bare SS316L austenitic steel was effectively predicted using a GPR model.

Keywords

• Wear
• Slurry erosion
• Thermal spray
• HVOF coatings
• Machine learning
• Regression learning