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

TL;DR

This paper reviews data-driven intelligent computational design (DICD), highlighting its use of deep learning for design feature extraction and pattern learning, and proposes a systematic roadmap for its full-process implementation in product design.

Contribution

It introduces a comprehensive, systematic roadmap for implementing DICD across the entire product design process, addressing current research gaps.

Findings

Provides a general workflow for DICD project planning

Establishes an overall framework for DICD implementation

Identifies key enabling technologies and application scenarios

Abstract

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.