Abstract
The purpose of real-life problems is often to be able to find less expensive and more effective ways of production without compromising product quality because companies must provide competitive advantage to maintain existence. In order to improve quality, design of experiment techniques is employed. RSM is a widely used technique thanks to its minimum number of experiment requirement. Hence it is used especially with continuous solution spaces and high-cost experimentations. Moreover, in most cases there is more than one response that firms must optimize simultaneously. For instance companies want to reduce the costs while improving product quality. Decision making is more difficult when conflicting objectives exist. For this reason multi response optimization is an important field to study. In this study, optimization of a manufacturing problem with two responses was carried out by the application of response surface methodology (RSM) and desirability function.
Highlights
Customers tend to purchase quality products, timely and appropriate prices
When continuous process variables exist, response surface methodology (RSM) is an effective method to use with second order models based on statistical and mathematical techniques
Because of this property RSM is widely used in real life problems
Summary
Customers tend to purchase quality products, timely and appropriate prices. In the face of the world's growing needs, firms must provide competitive advantages to maintain existence. Second-order models have the ability to show, how to behave the quality characteristics of interest on a surface and are capable of determining the best parameter levels. When continuous process variables exist, RSM is an effective method to use with second order models based on statistical and mathematical techniques. Because of this property RSM is widely used in real life problems. Feature of the objective function to be used is determined and the appropriate experimental design is prepared which provides the ability to retain information necessary for the optimization of the problem and modeling of the objective function. Optimum parameter levels determined to obtain the optimal value of the objective function are created in the light of the experiment results
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