A comparative analysis of CAD modeling approaches for design solution space exploration

Abstract

Design solution space (DSS) exploration is a pivotal process for comprehending design challenges and identifying diverse solution alternatives based on varying requirements. Computer-aided design (CAD) approaches, such as parametric design, knowledge-based design, and generative design, have proven successful in DSS exploration. However, a comparative study evaluating their performance is lacking in the technical literature. This paper addresses this gap by conducting a comparative analysis of these approaches regarding their performance in exploring DSS. The research begins by providing an overview of parametric design, knowledge-based design, and generative design, establishing the foundation for the study. Six evaluation criteria are identified based on the DSS exploration process. A qualitative analysis is then conducted, considering these criteria, to objectively assess the performance of each modeling approach. The results highlight the strengths and weaknesses of each approach, revealing that DSS exploration success is directly tied to the quantity of implemented knowledge. The results also emphasize the complementarity of those approaches, as their strengths and weaknesses are based on different problem-solving logics, demonstrating the synergy that can be achieved through strategic combinations of them. Additionally, the paper discusses open issues related to DSS exploration, contributing valuable insights for future developments in this field.