Dynamic multi-response optimization using principal component analysis and multiple criteria evaluation of the grey relation model

The application of parameter design methodology has been considerable in recent years to make system performance robust over a wide range of input conditions. This notion has been referred to as a robust design with dynamic characteristics. Due to product complexity, multiple correlated characteristics must be simultaneously evaluated for improving product quality. Dynamic multi-response optimization is becoming an important issue to contemporary industry. This study developed a novel procedure of optimizing dynamic multi-responses using principal component analysis (PCA) and multiple criteria evaluation of the grey relation model. PCA can consider the correlations among multiple quality characteristics to obtain uncorrelated components. These components are then substituted into multiple criteria evaluation of the grey relation model to determine the optimal factor level combination. A case study demonstrates the effectiveness of the proposed procedure for optimizing dynamic multi-response processes.

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