High technology industry must continuously improve product quality and multiple correlated product quality characteristics must be assessed simultaneously due to product complexity. While many Taguchi method applications have addressed a state system problem, dynamic multi-response problems have seldom been examined. This study presents a novel optimization procedure for dynamic multiple responses based on Taguchi's parameter design. The signal to noise (SN) ratio and system sensitivity are used to assess the performance of each response. Principal component analysis is then performed on the SN values and system sensitivity values to obtain a set of uncorrelated components. The optimization direction for each component is also determined based on the corresponding variation mode chart. Finally, the relative closeness to the ideal solution resulting from the technique for order preference by similarity to ideal solution is determined as an overall performance index for multiple responses. A case study obtained from biological reduction of an ethyl acetoacetate process demonstrates the effectiveness of the proposed procedure.
- Dynamic multiple responses
- Multiple attribute decision-making
- Principal component analysis
- Taguchi method