Integrated Robust Design Methodology and Dual Response Surface Methodology Approach in Optimization of Powder Coating Process

2021 ◽  
pp. 159-174
Author(s):  
Preetam Naik ◽  
Suraj Rane
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Robust Design ◽  
Design Methodology ◽  
Powder Coating ◽  
Coating Process ◽  
Dual Response

scholarly journals Determination of the Optimal Neural Network Transfer Function for Response Surface Methodology and Robust Design

2021 ◽  
Vol 11 (15) ◽  
pp. 6768
Author(s):  
Tuan-Ho Le ◽  
Hyeonae Jang ◽  
Sangmun Shin
Keyword(s):  
Response Surface Methodology ◽  
Transfer Function ◽  
Response Surface ◽  
Robust Design ◽  
Transfer Functions ◽  
Desirability Function ◽  
Likelihood Estimation ◽  
Statistical Simulation ◽  
Screening Procedure ◽  
Function Estimation

Response surface methodology (RSM) has been widely recognized as an essential estimation tool in many robust design studies investigating the second-order polynomial functional relationship between the responses of interest and their associated input variables. However, there is scope for improvement in the flexibility of estimation models and the accuracy of their results. Although many NN-based estimations and optimization approaches have been reported in the literature, a closed functional form is not readily available. To address this limitation, a maximum-likelihood estimation approach for an NN-based response function estimation (NRFE) is used to obtain the functional forms of the process mean and standard deviation. While the estimation results of most existing NN-based approaches depend primarily on their transfer functions, this approach often requires a screening procedure for various transfer functions. In this study, the proposed NRFE identifies a new screening procedure to obtain the best transfer function in an NN structure using a desirability function family while determining its associated weight parameters. A statistical simulation was performed to evaluate the efficiency of the proposed NRFE method. In this particular simulation, the proposed NRFE method provided significantly better results than conventional RSM. Finally, a numerical example is used for validating the proposed method.

Implicit memory-based technique in solving dynamic scheduling problems through Response Surface Methodology – Part II

2014 ◽  
Vol 7 (2) ◽  
pp. 143-174
Author(s):  
Manuel Blanco Abello ◽  
Zbigniew Michalewicz
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Project Scheduling ◽  
Design Methodology ◽  
Dynamic Scheduling ◽  
Variable Number ◽  
The Other ◽  
Scheduling Problems ◽  
Content Type ◽  
Dynamic Resource

Purpose – This is the second part of a two-part paper. The purpose of this paper is to report the results on the application of the methods that use the Response Surface Methodology to investigate an evolutionary algorithm (EA) and memory-based approach referred to as McBAR – the Mapping of Task IDs for Centroid-Based Adaptation with Random Immigrants. Design/methodology/approach – The methods applied in this paper are fully explained in the first part. They are utilized to investigate the performances (ability to determine solutions to problems) of techniques composed of McBAR and some EA-based techniques for solving some multi-objective dynamic resource-constrained project scheduling problems with a variable number of tasks. Findings – The main results include the following: first, some algorithmic components of McBAR are legitimate; second, the performance of McBAR is generally superior to those of the other techniques after increase in the number of tasks in each of the above-mentioned problems; and third, McBAR has the most resilient performance among the techniques against changes in the environment that set the problems. Originality/value – This paper is novel for investigating the enumerated results.

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