It is desired to optimize design parameters in any product development for achieving the appropriate efficiency level in any manufacturing industry. To select the best materials used, reduce cost, and increase a prod...It is desired to optimize design parameters in any product development for achieving the appropriate efficiency level in any manufacturing industry. To select the best materials used, reduce cost, and increase a product’s sustainability, an analysis of all design parameters must be conducted. Suitable design parameters and their optimum ranges provide the feasibility in developing a specific product. Response Surface Methodology (RSM) provides the opportunity of checking the parameters after considering optimization strategies, which results in improving the production process. In this study, the research aims to construct a 3D model and a mathematical equation on a foldable product to optimize the design parameters. A 2-level 3 factors small Central Composite Design (CCD) method is used for planning experimental trials, and the primary objective is to determine the optimal value for three design parameters, which are fold angle, length of the cycle, and height between seat and paddle in terms of the response which is “time required to fold the product”. This paper directs attention towards response optimization to achieve minimum “time required to fold the product” using the desirability criteria of Response Surface Methodology (RSM) and the optimization approach of the Genetic Algorithm (GA). The optimum value of “time required to fold the product” is found to be 2.415 seconds with a combination of design parameters such as “fold angle” of 180<span style="white-space:nowrap;">°</span>, “length of the cycle” of 74.112 cm, and “height between seat and paddle” of 0.613 m using Response Surface Methodology (RSM). The Genetic Algorithm (GA) predicts the “time required to fold the product” is 2.39 seconds and design parameters of “fold angle” of 179.559<span style="white-space:nowrap;">°</span>, “length of the cycle” of 74.1 cm, and “height between seat and paddle” of 0.59 m. This similar sort of analysis can be implemented in different manufacturing industries for developing a specific product.展开更多
文摘It is desired to optimize design parameters in any product development for achieving the appropriate efficiency level in any manufacturing industry. To select the best materials used, reduce cost, and increase a product’s sustainability, an analysis of all design parameters must be conducted. Suitable design parameters and their optimum ranges provide the feasibility in developing a specific product. Response Surface Methodology (RSM) provides the opportunity of checking the parameters after considering optimization strategies, which results in improving the production process. In this study, the research aims to construct a 3D model and a mathematical equation on a foldable product to optimize the design parameters. A 2-level 3 factors small Central Composite Design (CCD) method is used for planning experimental trials, and the primary objective is to determine the optimal value for three design parameters, which are fold angle, length of the cycle, and height between seat and paddle in terms of the response which is “time required to fold the product”. This paper directs attention towards response optimization to achieve minimum “time required to fold the product” using the desirability criteria of Response Surface Methodology (RSM) and the optimization approach of the Genetic Algorithm (GA). The optimum value of “time required to fold the product” is found to be 2.415 seconds with a combination of design parameters such as “fold angle” of 180<span style="white-space:nowrap;">°</span>, “length of the cycle” of 74.112 cm, and “height between seat and paddle” of 0.613 m using Response Surface Methodology (RSM). The Genetic Algorithm (GA) predicts the “time required to fold the product” is 2.39 seconds and design parameters of “fold angle” of 179.559<span style="white-space:nowrap;">°</span>, “length of the cycle” of 74.1 cm, and “height between seat and paddle” of 0.59 m. This similar sort of analysis can be implemented in different manufacturing industries for developing a specific product.
