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TRANSPARENCY OF DESIGN AUTOMATION SYSTEMS USING VISUAL PROGRAMMING – WITHIN THE MECHANICAL MANUFACTURING INDUSTRY

Published online by Cambridge University Press:  27 July 2021

Tim Heikkinen
Tim Heikkinen*
Affiliation:
Jönköping University
*
Heikkinen, Tim, Jönköping University, Industrial Product Development, Production and Design, Sweden, tim.heikkinen@ju.se

Abstract

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One challenge with design automation is system transparency with adjustable granularity because of the many different forms of representation from multiple disciplines. Previous research has focused on visualization through the generation of graphs, packaging into electronic books, and model highlighting. The research presented in this paper focuses instead on a visual programming approach, commonly applied in the building industry, where design assets and external references are wrapped into visual components and managed on a canvas with information input/output relations displayed. This entails additional documentation efforts, but the visualization is arguably more useful as groups and levels of granularity are adjusted by the engineers themselves as a part of the development work. To explore visual programming and its potential benefits as a way of enabling transparency with adjustable granularity of DA systems within mechanical manufacturing industry, an existing textual design automation system was transformed into a visual one using Grasshopper® (a visual programming environment) and discussed with respect to DA system transparency, feature-based CAD, and DA system development.

Keywords

Design automationVisual programmingKnowledge managementVisualisationDesign for Additive Manufacturing (DfAM)
Type
Article
Information
Proceedings of the Design Society , Volume 1: ICED21 , August 2021 , pp. 3249 - 3258
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), 2021. Published by Cambridge University Press

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