Predictive Analysis of Wear Rate and Microhardness in Laser-Clad Titanium-Coated Carbon Steel at Variable Power Settings

• Published in: Annales de chimie (Paris. 1914) Vol. 49; no. 2; p. 133
• Main Authors: Aladesanmi, V.I., Laseinde, O.T.
• Format: Journal Article
• Language: English
• Published: Edmonton International Information and Engineering Technology Association (IIETA) 01.04.2025
• Subjects: Additive manufacturing; Adhesive wear; Bond strength; Carbon steel; Carbon steels; Coefficient of friction; Design; Humidity; Laser beam cladding; Lasers; Load; Machine learning; Mechanical properties; Microhardness; Optimization models; Parameter identification; Process parameters; Statistical analysis; Statistical correlation; Titanium; Wear rate
• ISSN: 0151-9107; 1958-5934; 1958-5934
• DOI: 10.18280/acsm.490203

The effect of laser power variation on the mechanical properties of laser-clad titanium materials was studied in this research. An optimization model was executed to identify the independent effect of laser power over other processing parameters. The mechanical properties of the microhardness and wear of the produced samples were derived. The resulting microhardness ranges from 131.38HV to 350.04HV. The wear experiment's working loads were 5N and 10N. The 5N wear load reveals a wear rate range of 0.66mm2/N to 0.186mm2/N, with a wear volume of 0.66mm3 to 5.58mm3 and a coefficient of friction of 0.074 to 0.172. The 10N wear load reveals a wear rate range of 0.039mm2/N to 0.249mm3, a wear volume of 1.17mm3 to 7.47mm3, and a coefficient of friction range of 0.204 to 0.245. An optimum hardness of 350.04HV was obtained at 1.5KW laser power with a wear rate of 0.067mm2/N, a wear volume of 2.01mm3, a coefficient of friction of 0.111 at 5N load, and a wear rate of 0.090mm2/N, a wear volume of 2.70mm3, and a coefficient of friction of 0.227mm3/N at 10N load. As the laser power increased, we observed an increase in wear rate, wear volume, and coefficient of friction. We used Python 3.9 of Google Collab to compute a multilinear regression predictive analysis of the correlative relationship between the clad microhardness, wear rate, and processing parameter. The model revealed a coefficient of determinant r2-score of 0.89, a mean square error of 0.0006, and a mean absolute error of 0.0229. The model result confirmed a significant statistical correlation. This research is useful for the additive shaping of industrial carbon steel machinery for maintenance and wear control measures.