A proposed weighted additive model to optimize multiple quality responses in the Taguchi method with applications

Abstract

Optimizing multiple responses received significant research attention. Nevertheless, most of the proposed approach ignored engineer’s preferences about factor settings as well as the mathematical relationship between quality responses and process factors. This research utilizes the weighted additive model in fuzzy goal programming for optimizing multiple responses in the Taguchi method. The mathematical relationships between each quality response and process factors are first formulated. Then, each of quality responses and process factors is represented by a proper membership function with desired preference settings. A general optimization model is finally proposed. Four case studies are provided for illustration; in all of which the proposed approach efficiently optimized multiple responses. Compared to previous techniques, such as fuzzy logic, grey relational analysis, and multiple response signal-to-noise approach, the proposed approach provides more reliable optimal factor settings, considers engineer’s preferences about process factors, and provides mathematical relationships that can enable process engineers predict process performance accurately. In conclusion, the proposed approach may provide process engineers great assistance in optimizing process performance in the manufacturing applications on the Taguchi method.