Volume 26, Issue 1 (IJIEPR 2015)                   IJIEPR 2015, 26(1): 1-11 | Back to browse issues page

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Mahakalkar S, Tatwawadi V, Giri J, Modak J. Corrugated Box Production Process Optimization Using Dimensional Analysis and Response Surface Methodology. IJIEPR 2015; 26 (1) :1-11
URL: http://ijiepr.iust.ac.ir/article-1-531-en.html
Corrugated Box Production Process Optimization Using Dimensional Analysis and Response Surface Methodology
Sachin Mahakalkar 1, Vivek Tatwawadi2 , Jayant Giri2 , Jayant Modak2
1- Nagpur University , sachu_gm@yahoo.com
2- Nagpur University
Abstract:   (5493 Views)
Response surface methodology (RSM) is a statistical method useful in the modeling and analysis of problems in which the response variable receives the influence of several independent variables, in order to determine which are the conditions under which should operate these variables to optimize a corrugated box production process. The purpose of this research is to create response surface models through regression on experimental data which has been reduced using DA to obtain optimal processing conditions. Studies carried out for corrugated sheet box manufacturing industries having man machine system revealed the contribution of many independent parameters on cycle time. The independent parameters include anthropometric data of workers, personal data, machine specification, workplace parameters, product specification, environmental conditions and mechanical properties of corrugated sheet. Their effect on response parameter cycle time is totally unknown. The developed model was simulated and optimized with the aid of MATLAB R2011a and the computed value for cycle time is obtained and compared with experimental value. The results obtained showed that the correlation R, adjusted R2 and RMS error were valid.
Keywords: Response Surface Methodology (RSM), Dimensional Analysis (DA), Independent Variables, Response Variable, Correlations, Root Mean Square Error (RMS) and MATLAB
Full-Text [PDF 672 kb]   (2340 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2013/06/2 | Accepted: 2015/08/10 | Published: 2015/08/22
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