Surface quality evaluation through new optical imaging system-based objective speckle for additive manufactured titanium samples

Laser cladding is an effective additive manufacturing technology used for materials’ surface modification to enhance their surface and mechanical properties. Surface roughness is a crucial feature that affects the materials’ quality and lifespan. Additionally, it is a real indicator of hardness and wear resistance. Although the existing methods may precisely measure surface roughness, they require delicate adjustment, can damage surfaces, and have limited working distances. This study presents a novel optical imaging system to quantitatively estimate the quality modifications of additive manufacturing samples by measuring their surface roughness based on objective speckle and advanced multivariate analysis methods. The raw speckle patterns are generated from deposited layers on Ti6Al4V titanium alloys obtained at different laser processing parameters. The proposed analysis approach produces collections of local statistical matrices from which histogram features are extracted. Canonical correlation analysis (CCA) is then proposed to distinguish the most significant features. The correlation between the features (with and without applying CCA) and surface roughness is established by using two machine learning regression algorithms, nonlinear support vector regression (SVR), random forest regression (RF), and k-nearest neighbor regression (kNN). The results confirmed that combining RF and CCA provided a feasible regression model to estimate the surface roughness, with 0.998 for R² in training samples and maximum values of 0.258, 0.484, and 4.593 %, respectively, for MAE, RMSE, and MAPE in test samples. This demonstrates that the proposed objective speckle imaging system is effective in estimating the quality modification of additive manufacturing samples by measuring their surface roughness.