Using response surface methodology with response transformation in optimizing plasma spraying coatings

Lin, Bor-Tsuen; Jean, Ming-Der; Chou, Jyh–Horng

doi:10.1007/s00170-006-0599-y

Cited by 28 publications

(10 citation statements)

References 9 publications

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“…This RSM can also serve as a significant approach to establish the relationships between the response and independent variables of baking process. Moreover, using the RSM method, one can readily identify the most influential variables of a process, as it is reported to act as a powerful optimization and design tool in many engineering applications [4][5][6]. Therefore, the aim of the present work was using the RSM in optimizing the strain aging or baking treatment of AA5052 and determining the most important baking variables as well as the optimal combinations of these variables to attain the maximum bake-hardening amount and final yield stress after baking process.…”

Section: The Significance Of Modeling By Response Surface Methodologymentioning

confidence: 99%

Using response surface methodology to optimize the strain aging response of AA5052

Dehghani

Nekahi

Mirzaie

2010

Materials Science and Engineering: A

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Section: The Significance Of Modeling By Response Surface Methodologymentioning

confidence: 99%

Using response surface methodology to optimize the strain aging response of AA5052

Dehghani

Nekahi

Mirzaie

2010

Materials Science and Engineering: A

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“…RSM also quantifies the relationship between the controllable input parameters and the obtained response surfaces. 8 Lin et al 9 used RSM to study the optimization of plasma spray coating processes for partially stabilized zirconia (PSZ). Sarma 10 applied RSM to investigate the effect of cutting force in the turning of glass fiber-reinforced polymer pipes using CBN tools.…”

Section: Introductionmentioning

confidence: 99%

Modeling and optimization in precise boring processes for aluminum alloy 6061T6 components

Yang

Liao

Yang

et al. 2012

Int. J. Precis. Eng. Manuf.

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This investigation applies a Taguchi orthogonal table, integrating response surface methodology (RSM) to optimize parameters of a precise boring process using a computer numerical control (CNC) machine operation for the production of aluminum alloy 6061T6 components. By applying RSM analysis, the predictive mathematical model of the roughness average and roundness were developed in terms of the cutting speed, feed rate, and depth of cut. The error analysis and experimental results indicate that the proposed predictive mathematical models could adequately describe the performance indicators within the limits of the factors that are being investigated. In addition, the analysis of variance (ANOVA) was implemented to identify the significant factors and the response surface contours were constructed for determining the optimum conditions of precise boring processes using CNC machine operations.

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“…The issues surrounding parameter or tolerance design in the use of RSM have been addressed in previous works. Lin, Jean, and Chou (2007) formed a response function through RSM simulation to explore the space of zirconia performance for the plasma spraying process. Jeang (1999a) developed an application for parameter determination via RSM simulation to make the link between computer-aided design (CAD) and computeraided manufacturing (CAM) systems more useful and effective.…”

Section: Introductionmentioning

confidence: 99%

Robust optimisation for simultaneous process mean, process tolerance and product specification determination

Jeang

2014

International Journal of Computer Integrated Manufacturing

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Conventionally, process mean determination is performed prior to process tolerance design, independent of product design, such as product specification. In addition, previous studies considered the production costs and quality losses as deterministic values in mean and tolerance decisions. This study foregoes the assumptions of deterministic value and independent determination. For further cost reduction and quality improvement, process mean, process tolerance and product specification are simultaneously determined as controllable factors under the assumption that production costs and quality losses are random variables with given probabilistic distributions. At first, the various levels of mean, tolerance and specification are combined in accordance with the Box-Behnken experimental matrix, and used as inputs for a Monte Carlo simulation to obtain the simulated outputs. Then, these outputs are transferred to the total cost, which includes quality loss, production cost and penalty cost. Mean, tolerance and specification are treated as controllable factors, while total cost is a response value of interest. The design problem is analysed statistically using response surface methodology (RSM) in order to find the response function, which in turn is used as an objective function and optimised through mathematical programming (MP). A bicurve lens design is employed to demonstrate the proposed approach.

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Using response surface methodology with response transformation in optimizing plasma spraying coatings

Cited by 28 publications

References 9 publications

Using response surface methodology to optimize the strain aging response of AA5052

Using response surface methodology to optimize the strain aging response of AA5052

Modeling and optimization in precise boring processes for aluminum alloy 6061T6 components

Robust optimisation for simultaneous process mean, process tolerance and product specification determination

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