A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

Al–Refaie, Abbas; Rawabdeh, Ibrahim A.; Abu-alhaj, Reema; Jalham, Issam S.

doi:10.4018/ijfsa.2012070102

Cited by 13 publications

(12 citation statements)

References 38 publications

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“…The centroid defuzzification method is utilized in this research. Several studies utilized this fuzzy logic approach for optimizing performance with multiple quality characteristics (AL-Refaie, 2010;AL-Refaie et al, 2012;Sun and Hsueh, 2011;Mandic et al, 2014).…”

Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Al–Refaie¹,

Fouad²,

Athamneh³

2019

MAS

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Thus far, the Taguchi technique is found only efficient in obtaining the combination of optimal factor settings when a single product/process response is considered. In today’s dynamic environment, customers are interested in multiple quality responses. This research, therefore, utilizes fuzzy logic and backward-propagation neural networks (BPNNs) to optimize process performance for products of multiple quality responses. In this research, quality characteristics are transformed to signal to noise (S/N) ratios, which are then used as inputs to a fuzzy model to obtain a single common output measure (COM). Next, BPNNs are employed to obtain full-factorial experimental data. Finally, the combination of factor levels that maximizes the average COM value is chosen as the optimal combination. Three case studies are provided for illustration; in all of which the proposed approach provided the largest total anticipated improvement. This indicates that the proposed approach is more efficient than Taguchi-fuzzy, grey-Taguchi, and Taguchi-utility methods. In conclusion, the fuzzy-BPNN approach may greatly assist process/product engineers in optimizing performance with multiple responses in a wide range of business applications.

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Section: Fuzzy Logicmentioning

confidence: 99%

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Al–Refaie¹,

Fouad²,

Athamneh³

2019

MAS

Self Cite

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“…In contrast, the fuzzy goal programming (FGP) is used when such values are not known precisely. [17][18][19][20][21][22][23][24][25][26][27][28][29] Therefore, the FGP approach will be used to define the combination of optimal factor settings of vegetable oil filling process that reduces the number of defective cans and enhances the production rate. The remaining of this research is outlined in the following sequence.…”

Section: Introductionmentioning

confidence: 99%

Optimizing performance of oil filling processes using fuzzy goal programming

Al–Refaie

Ghazaleh

2017

Proceedings of the Institution of Mechanical Engineers, Part E:

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This research aims at improving the performance of the filling line machine using fuzzy goal programming. Two main quality responses including the number of defective products and the production rate of the filling machine are of main interest. Initially, the control charts for number of nonconforming and production rate, np and I-MR, respectively, are established and indicate that the process is in statistical control. However, the process is found incapable. The fuzzy goal programming model is adopted to identify the combination of optimal factor settings utilizing the Taguchi's L 16 array. The optimal factor settings are 5.

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“…This method has been applied for optimizing performance of manufacturing processes in many business applications. [6][7][8][9][10] Nevertheless, this technique is found only effective to achieve optimal performance with a single response. [11][12][13][14][15] As a result, several approaches have been proposed for optimizing multiple responses concurrently.…”

Section: Introductionmentioning

confidence: 99%

Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming

Al–Refaie

2015

Proceedings of the Institution of Mechanical Engineers, Part E:

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The main objective of this research is to optimize performance of plastic pipes' extrusion process with two main quality responses, including pipe's diameter and thickness, using Min-Max model in fuzzy goal programming. First, the variables control charts are constructed at initial factor settings of extrusion process, where the results reveal that the extrusion process is in statistical control. However, the actual capability index values for diameter and thickness are estimated 0.7094 and 0.7968, respectively. The process capability for a complete product, MC pk , is calculated as 0.752. These values indicate that the extrusion process is incapable. To improve process performance, the L 18 array is utilized for experimental design with three 3-level process factors. Then, the Min-Max model is used to determine the combination of optimal factor settings. The estimated capability index values for diameter and thickness at the combination of optimal factor settings are estimated and found to be 1.504 and 1.879, respectively. The integrated process capability index, MC pk , is calculated as 1.681. Confirmation experiments should that the Min-Max model results in enhancing process capability for both responses. In conclusions, the Min-Max Model may provide valuable assistance to practitioners in optimizing performance while considering both product and process preferences.

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A Fuzzy Multiple Regression Approach for Optimizing Multiple Responses in the Taguchi Method

Cited by 13 publications

References 38 publications

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Fuzzy Logic and Back-Propagation Neural Networks for Optimal Performance

Optimizing performance of oil filling processes using fuzzy goal programming

Optimal performance of plastic pipes’ extrusion process using Min-Max model in fuzzy goal programming

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