Data-driven intelligent computational design for products: method, techniques, and applications

Abstract

Data-driven intelligent computational design (DICD) is a research hotspot that emerged under fast-developing artificial intelligence. It emphasizes utilizing deep learning algorithms to extract and represent the design features hidden in historical or fabricated design process data and then learn the combination and mapping patterns of these design features for design solution retrieval, generation, optimization, evaluation, etc. Due to its capability of automatically and efficiently generating design solutions and thus supporting human-in-the-loop intelligent and innovative design activities, DICD has drawn the attention of both academic and industrial fields. However, as an emerging research subject, many unexplored issues still limit the development and application of DICD, such as specific dataset building, engineering design-related feature engineering, systematic methods and techniques for DICD implementation in the entire product design process, etc. In this regard, a systematic and operable road map for DICD implementation from a full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the common mechanisms and calculation principles during DICD, key enabling technologies for detailed DICD implementation, and three case scenarios of DICD application. The road map can help academic researchers to locate their specific research directions for the further development of DICD and provide operable guidance for the engineers in their specific DICD applications.