A Quadratic Regression Model with Interaction to Optimize the Turning Conditions of Mild Carbon Steel

Omar M. Bataineh, Maysa A. Al-Shraideh, and Abeer T. Latifeh
Jordan University of Science and Technology, Irbid, Jordan
Abstract—Surface roughness of turned products is an important quality measure in machining operations. To investigate the dependence of surface roughness on the turning process variables in the case of Mild Carbon Steel (MCS), experiments were carried out according to the Design Of Experiments (DOE) methodology. Three process variables were studied: depth of cut, feed rate and spindle speed. A 3x3x4 full factorial design with three replicates was generated and conducted. The average surface roughness of machined specimens was measured in these experimental runs. Results of surface roughness were then used to develop a quadratic regression model with interaction. This model was then examined using factorial plots and hypothesis tests. Accordingly, the model was revised and used to identify the optimal conditions of depth of cut, feed rate and spindle speed that minimize the surface roughness of turned parts.

Index Terms—optimization, design of experiments, regression analysis, ANOVA, turning process

Cite: Omar M. Bataineh, Maysa A. Al-Shraideh, and Abeer T. Latifeh, "A Quadratic Regression Model with Interaction to Optimize the Turning Conditions of Mild Carbon Steel," International Journal of Mechanical Engineering and Robotics Research, Vol. 7, No. 1, pp. 78-82, January 2018. DOI: 10.18178/ijmerr.7.1.78-82
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