Abstract: In order to evaluate product design more reliably, a process for dynamic allocation of weights to experts is proposed. The group consensus model is introduced to measure the degree of difference between expert opinions. The level of similarity is used to detect and eliminate very distinct expert opinions, thereby allowing a consensus to be reached more easily and efficiently. Weights of expert opinions are determined dynamically using the clustering algorithm to sort out the quality of various product designs. The proposed method is applied to the real-world intelligent warning sign of vehicle. Results demonstrate the ability of the proposed method to capture the difference between expert opinions effectively, allocate weights to expert opinions dynamically and evaluate the product more reliably. Finally, the proposed method is compared with other competing methods to prove its superiority, yielding insights into the evaluation of product design.

Key words: industrial design, dynamic weights, coefficient of consensus, cluster analysis, design evaluation