Synthesis and characterization of carbon steel matrix composites by laser cladding techniques of additive manufacturing

D.Phil. (Mechanical Engineering Science)Abstract: This research work utilized the additive manufacturing techniques (laser cladding) to fabricate metal matrix composite of titanium and titanium diboride on substrate of carbon steel. Different admixed titanium and titanium diboride composite were done at different process parameters. Design of experiment was used for the analysis of the experiments to derive the optimization process parameters. The working parameters of laser power were from 1250 W to 1500 W and scanning speed of 1.0 to 1.2. m/min. The analysis showed a microhardness response range of 781 HV to 1254 HV. The microstructural view of TiB2 highly rich samples revealed phases of columnar dark phase, cracks, and pores. SEM of even mix metal matrix sample revealed widmannstetter grain structure with alpha and gamma martensitic phases. SEM of Titanium rich samples revealed alpha acicular grain structure. The X-ray diffraction results of TiB2 rich samples reveals peak phase of cubic Titanium diboride, hexagonal Titanium, cubic alpha-Iron, tetragonal Iron 2 boride and hexagonal Titanium diboride. XRD of even mix sample revealed clad phases of hexagonal Titanium and orthorhombic Titanium diboride. XRD of Ti rich clad revealed phases of cubic gamma-Iron-austenite, hexagonal Titanium, hexagonal titanium diboride, cubic Khamrabaevite and hexagonal alpha-Titanium phases. The highest average hardness was at even mix ratio clad sample of 1221 HV. The tribological test was carried out at different loads of 2N, 5N and 10 N, respectively. The least wear rate and wear volume was derived on even mix ratio clad sample. The sample coefficient of friction was found to increase as the force loads increases and as the mix vii ratio of TiB2 content increases and vice versa with content of Ti clad samples. The highest roughness value was derived in highly rich TiB2 sample with 2.71 μm and the lowest was noticed in even mix ratio sample with 1.01 μm average roughness. A predictive statistical correlation and relationship between the wear rate and the hardness was carried out. A linear and quadratic polynomial regression machine learning details of the factor’s relationships were studies and stated. An independent variable of hardness property and dependent variable of wear rate property of cladded Ti and TiB2 on carbon steel were proposed for both linear and quadratic models. The predictive equation for the linear and quadratic polynomial regression were given to enable predictive determination of dependent variable of the wear rate from their dependent values of the micro-hardness property values evaluation. A clear optimization relevance of higher order polynomial regression analysis of the quadratic for maximized analytical results were stated and emphasized. The processing parameters employed in this research were optimized and can be recommended for manufacturing and production of these clads for surface engineering