Novel Surface Topography and Microhardness Characterization of Laser Clad Layer on TC4 Titanium Alloy Using Laser-Induced Breakdown Spectroscopy and Machine Learning

Abstract

This study was performed to characterize surface topography and microhardness of 40 wt pct NiCrBSiC-60 wt pct WC hard coating on TC4 titanium after coaxial laser cladding via Laser Induced Breakdown Spectroscopy (LIBS) and machine learning. The high content of the hard WC particles is accomplished to enhance the abrasion wear resistance of such alloy. Various powder feeding rates were carried out during laser cladding process. The energy-dispersive X-ray analysis assured that W content in the metal matrix notably increased from 26.19 to 53.49 pct while the Ti content decreased from about 15.16 to 0.46 pct for the clad layer processed at 20 and 60 g min⁻¹, respectively. The LIBS measurements successfully estimated such elements’ concentration as well as the clad layers' topography indicating that the effect of material matrix is a crucial challenge. Therefore, canonical correlation analysis and Belsley collinearity diagnostics were established to identify the essential emission lines from the whole spectra. Then, an optimized adaptive boosted random forest classifier was developed for microhardness investigation, with accuracy, sensitivity, and F1 score values of 0.9667. The results, confirmed by the metallurgical study, clarified that most of the titanium and tungsten emission lines have a significant impact on the surface topography as well as the microhardness values. The misclassification was attributed to the matrix effect such that the samples processed at 40 and 60 g min⁻¹ were comparable in microstructure and chemical characterization unlike the one processed at 20 g min⁻¹. Vickers microhardness of the metal matrix coating increased with the increase in the powder feeding rate, which is assured by the quantitative classification model.