Process Development Part 2: Exploring the Dimensional Process via the DOE

Kulkarni, Suhas

doi:10.3139/9781569905876.009

Cited by 2 publications

(2 citation statements)

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“…Any input to the process is a factor which can be set to the desired value or can be selected from the available options. On the other hand, any output from a process is a response [13]. The general equation for the experimental factorial design is as follows:…”

Section: Methodsmentioning

confidence: 99%

Determining Optimum Design Parameters of Foldable Product Using Response Surface Methodology and Genetic Algorithm

Alam¹,

Amin²

2020

ENG

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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˚, "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˚, "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.

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Section: Methodsmentioning

confidence: 99%

Determining Optimum Design Parameters of Foldable Product Using Response Surface Methodology and Genetic Algorithm

Alam¹,

Amin²

2020

ENG

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“…Any input to the process is a factor which can be set to a desired value on the machine controller or can be selected from the available options. On the other hand, any output from a process is a response [7]. A response is the result ob- Table 1.…”

Section: Methodsmentioning

confidence: 99%

Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation

Anika¹,

Tanzeem²,

Gupta³

2020

ENG

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In the modern era of manufacturing, it is important to optimize every design parameter in product development stage to reduce cost, material usage and to achieve the desired efficacy level. There are various models which serve those purposes, for instance, Design of Experiment (DoE) is used to check the parameters after adopting optimization tactics which results in reduced cost or saving operating time. In this regard, this research aims to construct a DoE model on a portable workstation to optimize its design parameters. The methodology of DOE would be a 2 level 3 factors full factorial DOE which is conducted to determine the optimal value for three design parameters (factors) which are material density, the length of the table and the length of the table stand in terms of the response which is the required time of fold ability function of the portable workstation. Based upon the evaluated interactions between the parameters, the optimized parameters are chosen for responses. Here, the resultant design parameters are at their lowest level, so the goal of time efficiency in fold ability function is achieved. This similar sort of DoE can be implemented in the furniture and other manufacturing industries who wish to optimize their material usage as well as increase efficiency and reduce cycle time.

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Process Development Part 2: Exploring the Dimensional Process via the DOE

Cited by 2 publications

References 0 publications

Determining Optimum Design Parameters of Foldable Product Using Response Surface Methodology and Genetic Algorithm

Determining Optimum Design Parameters of Foldable Product Using Response Surface Methodology and Genetic Algorithm

Design of Experiment (DoE): Implementation in Determining Optimum Design Parameters of Portable Workstation

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