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COMPARING COLLABORATIVE CAD MODELLING PATTERNS OF HIGH-PERFORMING AND LOW-PERFORMING TEAMS

Published online by Cambridge University Press:  19 June 2023

Robert Celjak ,
Nikola Horvat  and
Stanko Škec
Robert Celjak*
Affiliation:
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture
Nikola Horvat
Affiliation:
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture
Stanko Škec
Affiliation:
University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture
*
Celjak, Robert University of Zagreb, Faculty of Mechanical Engineering and Naval Architecture, Croatia, [email protected]

Abstract

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The development of cloud-based Computer-aided design (CAD) enabled real-time CAD collaboration between multiple designers. While this technology has great potential to change the way CAD work is done, it is still little explored. This paper presents a case with two high-performing and two low-performing three-member teams monitored with non-invasive methods (log data) during a six-week design project. The results show that high-performing teams focused more on the editing of assembly, while low-performing teams focused on creating and editing a part. Furthermore, high-performing teams tended to perform consecutive deleting actions and to transition to creating and editing classes of CAD actions after performing viewing actions. Two modelling approaches which lead to high-quality CAD models were identified. One approach is characterized by frequent use of transitions between editing and Organizing-Design (collaborative actions) classes, while the other between creating, editing and reversing classes. Presented results allow design teams to gain insight into sequential patterns which led to the generation of a high-quality CAD model and to better understand the CAD modelling process.

Keywords

Computer Aided Design (CAD)Virtual Engineering (VE)Collaborative designNon-invasive monitoringSequence analysis
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1007 - 1016
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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