2023/01/29 by Maolin Yang, Pingyu Jiang, Yang, Maolin +5
Engineering · #Additive Manufacturing and 3D Printing Technologies #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Manufacturing Process and Optimization
paper · pdf · doi:10.48550/arxiv.2301.12382
openalex publication_date 2023/01/29 · openalex created_date 2023/02/02 · openalex updated_date 2026/07/28
Data-driven intelligent computational design (DICD) is a research hotspot emerged under the context of fast-developing artificial intelligence. It emphasizes on 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 the purposes of 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 attentions from both academic and industrial fields. However, as an emerging research subject, there are still many unexplored issues that 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 full-process perspective is established, including a general workflow for DICD project planning, an overall framework for DICD project implementation, the computing mechanisms for DICD implementation, key enabling technologies for detailed DICD implementation, and three application scenarios of DICD. The road map reveals the common mechanisms and calculation principles of existing DICD researches, and thus it can provide systematic guidance for the possible DICD applications that have not been explored.