2022
DOI: 10.1007/s11661-022-06772-5
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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 nota… Show more

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Cited by 21 publications
(3 citation statements)
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“…Moreover, when the molten pool is cooled, there is a large degree of undercooling, so that nanoparticles with dispersion enhancement can be obtained, and even the amorphous structure [12], which makes the cladding layer have excellent wear resistance and high hardness. Al-sayed [13] laser-claded NiCrBSiC-60wt.%WC composite layers; it was found that with the increasing of powder Materials 2022, 15, 8200 2 of 10 feeding rate, the dilution between the cladding layer and matrix was weakened. Machine learning simulation results were consistent with the experimental results.…”
Section: Introductionmentioning
confidence: 99%
“…Moreover, when the molten pool is cooled, there is a large degree of undercooling, so that nanoparticles with dispersion enhancement can be obtained, and even the amorphous structure [12], which makes the cladding layer have excellent wear resistance and high hardness. Al-sayed [13] laser-claded NiCrBSiC-60wt.%WC composite layers; it was found that with the increasing of powder Materials 2022, 15, 8200 2 of 10 feeding rate, the dilution between the cladding layer and matrix was weakened. Machine learning simulation results were consistent with the experimental results.…”
Section: Introductionmentioning
confidence: 99%
“…The specimen's tensile strength gradually rose as the power index declined. The degree of crystallinity somewhat determined the tensile strength [32,33]. The internal grains of the specimens formed by the variable reference process were smaller.…”
Section: Analysis Of Tensile Propertiesmentioning
confidence: 99%
“…The accuracy of the RBM-PCA model can reach 100%, and the maximum dimensionality reduction time is 33.18 s. 26 Alsayed et al employed typical correlation analysis (CCA) to reduce dimensionality, subsequently utilizing an optimized adaptive augmented random forest classifier to obtain representative information for quantitative microhardness estimation of the samples. 27 Yuan et al found that the recognition accuracy using the PCA-SVM model gradually improved as the principal component (PC) increased. When 13 PCs were extracted as input, the recognition accuracy reached 100%.…”
Section: Introductionmentioning
confidence: 99%