Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach

Reséndiz‐Flores, Edgar O.; López-Quintero, Mateo E.

doi:10.1007/s00170-016-9395-5

Cited by 14 publications

(8 citation statements)

References 15 publications

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“…A positive degree of contribution means that the use of a parameter produces the effect of elevating the output of MD whereas a negative degree of contribution means that the use of a parameter produces the effect of lowering the output of MD. Consequently, parameter 22 is positive degree of contribution whereas parameter 1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,23,24,25,26,27,28,29,30,31,32,33, and 34 are negative degree of contribution. This research suggests that in order to obtain lower MD, the positive degree of contribution should be decreased while the negative degree of contribution should be increased.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 9 shows the degree of contribution in the first sample of unknown data in the liver function profile. Consequently, parameter 2, 4, 6, 8, 9, 13, 15, 22, 23, and 24 are positive degree of contribution whereas parameter 1, 3,5,7,10,11,12,14,16,17,18,19,20,21,25,26,27,28,29,30,31,32,33, and 34 are negative degree of contribution. This research suggests that in order to obtain lower MD, the positive degree of contribution should be decreased while the negative degree of contribution should be increased.…”

Section: Resultsmentioning

confidence: 99%

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Application of Mahalanobis-Taguchi System in Liver Function Profile of Methadone Flexi Dispensing Program

Zaini

Saad²,

Abu³

2022

J. Appl. Sci. Process Eng.

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Patients under the methadone flexi dispensing (MFlex) program are required to do blood tests like liver function profile. A doctor assesses 3 parameters like Alk phosphatase, ALT (SGPT), and AST (SGOT) to ensure the patient has a liver problem. Consequently, the existing system does not have a stable ecosystem towards classification and optimization. The objective is to apply the Mahalanobis-Taguchi system (MTS) in the MFlex program. The data is collected at Bandar Pekan clinic with 34 parameters. Two types of MTS methods are used like RT-Method and T-Method for classification and optimization respectively. The average Mahalanobis distance (MD) of healthy is 1.00 and unhealthy is 352.58. A positive degree of contribution has only 1 parameter. 15 unknown samples have been diagnosed. Type 2 of 6 modifications has been selected as the best-proposed solution. In conclusion, a pharmacist from Bandar Pekan clinic confirmed that MTS can solve problems in the classification and optimization of MFlex program.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Application of Mahalanobis-Taguchi System in Liver Function Profile of Methadone Flexi Dispensing Program

Zaini

Saad²,

Abu³

2022

J. Appl. Sci. Process Eng.

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“…The enhancement on OA are widely discussed and been emphasized continuously up to recent but most of its application is only for adaption into MT Method which been called Mahalanobis Taguchi system (MTS) by most of the practitioner. The enhancement since the past 15 years covered a broad area of feature selection optimization tools [17]- [25]. In the context of T-method, no published work on the enhancement of OA within T-method being reported yet up to this study been written.…”

Section: Alternative Feature Selection Methods Replacing Orthogonal Armentioning

confidence: 99%

Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy

Harudin¹,

Jamaludin²,

Muhtazaruddin³

et al. 2018

IJET

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The study of prediction has drawn great interest in a wide range of field. T-Method which was developed specifically for prediction of the multidimensional case using historical data to develop its baseline model proved that making a prediction is possible even with limited sample size. The element of the signal to noise ratio (SNR) adopted into the T-Method strengthens its robustness. Orthogonal array (OA) in T-Method was used as features selection optimization in improving the analysis speed, cost and computer burden during the analysis. However, the limitation of OA in dealing with higher dimensionality and complex combination factors restraint the optimization accuracy. Artificial Bee Colony (ABC) was adopted in this study to overcome this limitation. The result of this study shows that T-method +ABC provide the best error% accuracy with only 2.45% and 2.53% (3 optimized features out of 15) compared to T-Method +OA which 2.81% and 2.67% and T-Method +Spearman Correlation as 3.16% and 3.06%. The power consumption prediction case study is a good example for cases that deal with high correlation coefficient (R2) baseline model (>0.8). If the R2 is lower than 0.8, further enhancement needs to be done to ensure a low risk of high error% prediction.

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“…It can be converted into a single objective by combining the objectives or solving by a Pareto approach. Some authors focused on the optimization of the important manufacturing process of welding [10][11][12][13][14][15][16]. The aim of the current work is to deal with the multi-objective optimization problem of the submerged arc welding process (SAW) [11,16].…”

Section: Introductionmentioning

confidence: 99%

Accelerated cuckoo optimization algorithm for the multi-objective welding process

Mellal

Salhi

Williams

2019

J Braz. Soc. Mech. Sci. Eng.

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Welding is a well-known process in manufacturing industries due to its importance. Several process parameters should be tuned in order to perform a high-quality welding. Usually, the problem is described as an optimization one and the challenge is to reconcile conflicting objectives. This paper deals with a multi-objective welding process namely the submerged arc welding process, involving five objectives. The weighted sum approach is used to handle it. An accelerated cuckoo optimization algorithm is implemented for this process model and applied to a practical instance of it. On this practical example, the superiority of the proposed optimization technique has been demonstrated in terms of better solutions and fewer required generations of the cuckoos relative to the basic COA and four other optimization algorithms.

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Optimal identification of impact variables in a welding process for automobile seats mechanism by MTS-GBPSO approach

Cited by 14 publications

References 15 publications

Application of Mahalanobis-Taguchi System in Liver Function Profile of Methadone Flexi Dispensing Program

Application of Mahalanobis-Taguchi System in Liver Function Profile of Methadone Flexi Dispensing Program

Artificial Bee Colony for Features Selection Optimization in Increasing T-Method Accuracy

Accelerated cuckoo optimization algorithm for the multi-objective welding process

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