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Statistical Modelling and Optimization of Thermal Conductivity of Low Carbon Steel Weldment using Response Surface Methodology

Authors: Ajato V, Achebo JI, Obahiagbon KO


Thermal conductivity of a material is important because of its impact on the quality of weldments. Thus, there is a motivation to optimize the process variables that influence the thermal conductivity of the material. This study was carried out to model and optimize thermal conductivity of low carbon steel weldments using response surface methodology (RSM). A three variable central composite design (CCD) was used to design and plan the welding experiment and resulted in a total of 20 experimental runs as obtain from the Design Expert software. The independent variables studies were current, voltage and gas flow rate while the chosen response was thermal conductivity. A statistical model was developed for the optimization of the process variables using RSM and the model was experimentally validated by carrying out analysis of variance (ANOVA). The model was statistically significant (p<0.05) with a high coefficient of determination (R2=0.9976) and low standard deviation (0.0042) with respect to the mean (51.7394). Thermal conductivity was significantly influenced by all three factors (p<0.05); although current and gas flowrate had overall antagonistic effect while voltage had a positive effect. Numerical optimization of the statistical model revealed the optimum welding condition and this was a current of 150.27 A, a voltage of 23 V, and a gas flow rate of 10 L/min, and this yielded low carbon steel weldment having thermal conductivity of 51.85 W/moC.

Affiliations: Department of Production Engineering, Faculty of Engineering, University of Benin, Benin City, Nigeria
Keywords: Thermal Conductivity, ANOVA, Weldment, GTAW, Optimization
Published date: 2019/06/30

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ISSN: 2635-3342 (Print)

ISSN: 2635-3350 (Online)

DOI: In progress

ISI Impact Factor: In progress

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Chemical Engineering Department, Faculty of Engineering, University of Benin, PMB 1154, Ugbowo, Benin City, Edo State, Nigeria.