On the role of generating textual description for design intent communication in feature-based 3D collaborative design. (January 2019) - Cheng, Yuan; He, Fazhi; Lv, Xiao; Cai, Weiwei
Highlights: Extending the natural language to the design intent representation for collaborative product design. Supporting designers to share and exchange the design intents in knowledge-based collaborative CAD. Combining collaborative design, ontology, semantic network and natural language generation together. The ability in recognizing and repairing features, fixing design history and picking out features. Abstract: Modern manufacturing firms are more inclining to promote the product quality, save costs and reduce times of product design by both collaborative designing and model reuse. If CAD components constructed collaboratively have information representing their developers' design intents embedded in the model, people's understanding over the product should be improved and the product model should be best reused. Until now, capturing, recording and presenting design intents still remains a challenge. It has been shown by empirical studies that textual summarisations can lead to improved decision making. In this paper, we propose an approach to generation the natural language description about design intents of collaboratively developed product. The approach brings together techniques from different areas of collaborative designing, ontology and semantic network, and natural language generation. The language generation process is guided by an information model we established to give a structured description about design intents of collaboratively products. In order to record information related to the design intents, we build a common CAD model ontology and then generate a semantic network to describe dependencies, component structures and design history which are components of the design intent information model. The techniques of natural language generation, namely discourse planning and sentence planning, are adopted for the eventual linguistic generation of design intents. Finally, we use several case studies to prove the advantages of natural language in helping people better understanding the design intents.