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Context-aware cognitive design assistant: Implementation and study of design rules recommendations
Affiliation:1. Arts et Metiers Institute of Technology, LCPI, HESAM Université, 75013 Paris, France;2. Univ. Grenoble Alpes, CNRS, Grenoble INP, G-SCOP, Grenoble, France;3. Arts et Metiers Institute of Technology, LISPEN, HESAM Université, F-13617 Aix-en-Provence, France;4. Capgemini DEMS, Toulouse, France;5. LaPEA, Université de Paris and Univ Gustave Eiffel, Boulogne-Billancourt, France.;1. College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;2. Key Laboratory of Optimal Design of Modern Agricultural Equipment, College of Engineering, China Agricultural University, No.17 Tsinghua East Road, Haidian District, Beijing 100083, China;1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;2. College of Mechanical Engineering, Chongqing University, Chongqing 400044, China;3. School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing 402160, China;1. Department of Construction Management, Louisiana State University, Baton Rouge 70803, USA;2. Department of Electrical Engineering and Computer Science, Louisiana State University, Baton Rouge 70803, USA
Abstract:Design rules are an essential interface to facilitate the information exchange between designers and experts. Despite many innovations in Knowledge-Based Engineering and Knowledge Management, unstructured design rules documents are still widely used in the manufacturing industry. Due to the complexity of the design process, these documents often contain hundreds of design rules, applicable in varying design contexts. Searching for the right rules according to a design context is demanding in time and cognitive resources. In this paper, we propose a Context-Aware Cognitive Design Assistant (CACDA) to capture the design context and perform tasks such as the recommendation of design rules, the verification of design solutions, or the automation of design routines. Contrary to existing works in model quality testing, the CACDA uses a proactive approach of design rules application and helps designers to provide error-free designs on first attempt. In this paper, we present the design rules recommendation system of the CACDA, its capabilities and its implementation. Then, to measure the impact of design rules recommendations on the design process, we compare our approach with the use of traditional design rules documents. Results show that the use of the CACDA’s design rules recommendations lower the perceived difficulty of design rules retrieval from 75 to 43.5 on a scale of 100. On average, participants that used the demonstrator successfully applied 8.6 design rules on the 25 applicable design rules of our set. Participants that used unstructured documentation correctly applied 4.3 design rules. The global cognitive weight of the design activity as well as the design rules retrieval performances appear to be unchanged. These results demonstrate the usability of the demonstrator and show a positive impact on the design process and on the quality of CAD models. Future works will focus on the overcome of the main limitations of our current experiments, with a panel of professional designers, a lager design rules set and the implementation of several lacking features of the CACDA into the demonstrator.
Keywords:Design rule  Computer-aided design  Knowledge graph  Knowledge management  Context-Awareness  Cognitive assistant  Recommendation system  CACDA
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