Ozdemir, Akin

Optimal experimental designs are effective offline quality improvement techniques used to enhance existing products and develop new products for a constrained design region. For some practical situations, I-optimal experimental designs may be the most suitable way to construct optimal experimental design points, by addressing prediction variance to generate a measure of prediction performance over the non-standard experimental design region. The purpose of this article is three-fold: (1) the I-optimality criterion is selected for minimizing the average prediction variance; (2) fitted mean and variance response models are found; and (3) a nonlinear lexicographic goal programming model incorporating an I-optimal experimental design is proposed to find the optimum settings of controllable design factors. Comparison studies and a numerical example are provided to illustrate the effectiveness of the proposed methodology for improvement of the quality of products.

AtıfScopus15·WoS15·OpenAlex16

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Ozdemir, Akin; Cho, Byung Rae

Finding optimum conditions for process factors in an engineering optimization problem with response surface functions requires structured data collection using experimental design. When the experimental design space is constrained owing to external factors, its design space may form an asymmetrical and irregular shape and thus standard experimental design methods become ineffective. Computer-generated optimal designs, such as D-optimal designs, provide alternatives. While several iterative exchange algorithms for D-optimal designs are available for a linearly constrained irregular design space, it has not been clearly understood how D-optimal design points need to be generated when the design space is nonlinearly constrained and how response surface models are optimized. This article proposes an algorithm for generating the D-optimal design points that satisfy both feasibility and optimality conditions by using piecewise linear functions on the design space. The D-optimality-based response surface design models are proposed and optimization procedures are then analysed.

AtıfScopus18·WoS13·OpenAlex16

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Ozdemir, Akin; Turkoz, Mehmet

The use of response surface analysis is an effective method to improve the quality of products and processes. Along the same line, a dual response surface-based robust parameter design model may reduce product or process variability by selecting levels of input variables. In many products and processes, the researchers and practitioners may need to deal with a desirable response. Therefore, this paper proposes a new dual response surface model-based robust parameter design model with a desirability function to obtain a set of optimal operating conditions for multiple responses while minimizing the product or process variation as possible as. The proposed methodology is efficient and it improves the quality of the products by decreasing the variability in the process. The comparative studies show that proposed method is effective and outperforms the existing procedures.

AtıfScopus1

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