Abstract
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.
Highlights
The Taguchi method was widely applied for optimizing a single quality response of a product or process (Al-Refaie, et al, 2017; Dasgupta et al, 2014; Al-Refaie, 2012)
Because the factor levels were set to the optimal combination of factor settings (x1 (3) x2 (2) x3 (2) x4 (2) x5(1)), the quality responses Cof, WR, DR, and WCA were improved by 7.92, 5.02, 3.46, and 0.64 dB, respectively
This study proposed an effective fuzzy-backward-propagation neural networks (BPNNs) technique to optimize process performance with several responses
Summary
The Taguchi method was widely applied for optimizing a single quality response of a product or process (Al-Refaie, et al, 2017; Dasgupta et al, 2014; Al-Refaie, 2012). Been developed to optimize process performance for multiple responses of a product, including data envelopment analysis (Al-Refaie et al, 2009), fuzzy regression (Al-Refaie, 2013a), artificial neural networks (Al-Refaie, et al, 2016), fuzzy methods (Al-Refaie, 2015a; Bose et al, 2013), utility concepts (Sivasakthivel et al, 2014), and goal programming (Al-Refaie et al, 2014; Al-Refaie, 2015). 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)
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