A closed-loop human-computer interactive design method based on sequential human intention prediction and knowledge recommendation

Abstract

Designers working on large-scale complex engineering projects may encounter design errors due to limited experience and expertise. Therefore, during the design process, accurately identifying the designer’s design intention and recommending relevant design knowledge can enhance design efficiency and reduce errors. In this paper, we propose a closed-loop human–computer interactive design method based on sequential human intention prediction and knowledge recommendation. This method leverages the Function-Behaviour-Structure (FBS) model to reduce the dimensionality of design action sequences, so as to facilitate the analysis of potential design patterns. The processed action sequences are used to train Transformer to predict design intentions. In different input sequences, Transformer achieved a highest prediction accuracy of 92.09%. We construct a Design Knowledge Recommendation Framework (DKRF) and its corresponding design knowledge matching algorithm. This framework accurately recommends design knowledge to designers, solving design stagnation and improving design efficiency. Finally, a case study of four types of mechanical model design is conducted to demonstrate the feasibility and wide applicability of the proposed closed-loop human–computer interactive design method.

KEYWORDS:

• Human–computer interactive design
• design process mining
• intention prediction
• knowledge recommendation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Key Research and Development Program of China [grant number 2022YFB3402000], National Natural Science Foundation of China [grant numbers 52075479, 52105281] and Key Research & Development Program of Zhejiang Province [grant number 2023C01214].