Image Data-Based Surface Texture Characterization and Prediction Using Machine Learning Approaches for Additive Manufacturing

Abstract

The increase in the use of metal additive manufacturing (AM) processes in major industries like aerospace, defense, and electronics indicates the need for maintaining a tight quality control. A quick, low-cost, and reliable online surface texture measurement and verification system are required to improve its industrial adoption. In this paper, a comprehensive investigation of the surface characteristics of Ti-6Al-4V selective laser melted (SLM) parts using image texture parameters is discussed. The image texture parameters extracted from the surface images using first-order and second-order statistical methods, and measured 3D surface roughness parameters are used for characterizing the SLM surfaces. A comparative study of roughness prediction models developed using various machine learning approaches is also presented. Among the models, the Gaussian process regression (GPR) model gives an accurate prediction of roughness values with an R2 value of more than 0.9. The test data results of all models are presented.