Inspiring Designers’ Innovative Thinking: An Evolutionary Design Method for Product Forms

• Published in: Applied sciences Vol. 14; no. 17; p. 7818
• Main Authors: Liu, Shifeng, Su, Jianning, Zhang, Shutao, Qiu, Kai, Wang, Shijie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2024
• Subjects: Bibliometrics; Cognition & reasoning; Consumers; Design techniques; Designers; evolutionary algorithm; evolutionary process; Generations; Genetic algorithms; gray Markov model; Innovations; Integer programming; Neural networks; Philosophy; Product design; Product development; product form; quadratic curvature entropy; Trends
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14177818

The product form serves as a crucial information carrier for expressing design concepts and encompasses significant valuable references. During the product iteration process, changes in design subjects, such as designers and decision-makers, result in substantial variability and uncertainty in the direction of product form evolution. To address these issues, an evolutionary design method for product forms based on the gray Markov model and an evolutionary algorithm is proposed in this study. Firstly, quadratic curvature entropy is utilized to quantify historical form features of product evolution. Subsequently, the original data on product form feature evolution are fitted and predicted using the gray Markov model, thereby obtaining the predicted value of the latest generation of product form features, which is determined to be 0.14586. Finally, this study uses this predicted value to construct a fitness function in the framework of an evolutionary algorithm, which in turn identifies next-generation product forms that can stimulate designers’ creative thinking. The method’s application is illustrated using the side outer contour of the Audi A4 automobile as an example. The research findings demonstrate that combining the gray Markov model with an evolutionary algorithm can effectively simulate designers’ understanding of previous generations’ design concepts and achieve stable inheritance of these design concepts during product iteration. This approach mitigates the risk of abrupt changes in design concepts caused by designers and decision-makers due to personal cognitive biases, thereby enhancing product development efficiency.