Showing 3 results for Response Surface Methodology
Amineh Zadbood, Kazem Noghondarian, Zohreh Zadbood,
Volume 24, Issue 2 (6-2013)
Abstract
Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface Optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far. This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.
Mahdi Bashiri, Masoud Bagheri,
Volume 24, Issue 3 (9-2013)
Abstract
The quality of manufactured products is characterized by many controllable quality factors. These factors should be optimized to reach high quality products. In this paper we try to find the controllable factors levels with minimum deviation from the target and with a least variation. To solve the problem a simple aggregation function is used to aggregate the multiple responses functions then an imperialist competitive algorithm is used to find the best level of each controllable variable. Moreover the problem has been better analyzed by Pareto optimal solution to release the aggregation function. Then the proposed multiple response imperialist competitive algorithm (MRICA) has been compared with Multiple objective Genetic Algorithm. The experimental results show efficiency of the proposed approach in both aggregation and non aggregation methods in optimization of the nonlinear multi-response programming.
Mr Sachin Mahakalkar, Dr Vivek Tatwawadi, Mr Jayant Giri, Dr Jayant Modak,
Volume 26, Issue 1 (3-2015)
Abstract
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.
