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Paper Title: comparative analysis and optimization of process parameters in machining operation using central composite design and box behnken design over steel 1018
Authors Name: Abhinay Kumar , Dr Ajay Kumar Sarathe
Unique Id: IJRTI1711011
Published In: Volume 2 Issue 11, November-2017
Abstract: Surface roughness is a parameter which determines the quality of machined product. Now a days the general manufacturing problem can be described as the attainment of a predefined product quality with given equipment, cost and time constraints. So in recent years, a lot of extensive research work has been carried out for achieving predefined surface quality of machined product to eliminate wastage of over machining. Response surface methodology is used for prediction of surface roughness of machined part. This paper particularly shows the main findings of an experimental investigation into the effects of the cutting speed, feed rate, depth of cut, nose radius and cutting environment in turning. Design of experiment techniques, i.e. Response Surface Methodology (RSM) is going to be used to accomplish the objective of the experimental study. In this research work a new predictive model is proposed which is based on Central composite design and box behnken design. These both the techniques use statistical analysis and quadratic model for optimization of parameters in turning operation. Quadratic model gives best fits for the regression to find the optimal solution of equation and the proposed quadratic equation for predictive model
Keywords: Response Surface Methodology (RSM), DOE, CCD, BBD, ANOVA
Cite Article: "comparative analysis and optimization of process parameters in machining operation using central composite design and box behnken design over steel 1018", International Journal of Science & Engineering Development Research (, ISSN:2455-2631, Vol.2, Issue 11, page no.58 - 68, November-2017, Available :
Downloads: 000666
Publication Details: Published Paper ID: IJRTI1711011
Registration ID:170581
Published In: Volume 2 Issue 11, November-2017
DOI (Digital Object Identifier):
ISSN Number: 2456 - 3315
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