Machine learning-based prediction of single clad characteristics and non-destructive characterization of multi-layer deposited FeCoNiCrMo HEA on EN24 via laser cladding - Diwakar V.; Sharma A.; Yusufzai M.Z.K.; Vashista M. (My University, 2025)
The prediction of the proper metallurgical bonding and bead morphology in a laser cladding is very crucial to get a best profile of multi-clad layer. This work explores the laser cladding performance of FeCoCrNiMo high entropy alloy (HEA) on EN24 by analyzing the bead morphology with various process parameters. Machine learning (ML) technique is applied for two set of data where feasibility of the metallurgical bond is predicted and tested. From second set of data mainly three ML models, Support vector machine (SVM) radial kernels, K-Nearest neighbor (KNN), and multilayer perceptron (MLP) were applied to predict the outcomes. Sequential Minimal Optimization (SMO) algorithm is applied to identify the proper bonding between the substrate and cladded materials, which is classification-based ML model. The KNN model consistently attained the highest predictive accuracy across all response variables. It achieved perfect R² values and exhibited the lowest error metrics for clad width (w), clad height (h), clad depth (d), heat affected zone (HAZ), and Microhardness, establishing it as the most reliable and accurate model among the three applied ML model. To minimize the HAZ, the best conditions are lower laser powers, higher scanning speeds, and lower powder feed rate which is observed by contour plot. This reduces thermal diffusion into the substrate, maintaining material properties. Also, a non-destructive approach is used to characterize the multi-layer cladded before and after the deposition. The cladding process has significantly altered the microstructural and magnetic properties of the EN24 substrate, resulting in more refined grain structure and increased microhardness in the cladded region. © 2024 Elsevier Ltd