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Enhanced function-means modeling supporting design space exploration.

Authors :
Müller, Jakob R.
Isaksson, Ola
Landahl, Jonas
Raja, Visakha
Panarotto, Massimo
Levandowski, Christoffer
Raudberget, Dag
Source :
AI EDAM. Nov2019, Vol. 33 Issue 4, p502-516. 15p.
Publication Year :
2019

Abstract

One problem in incremental product development is that geometric models are limited in their ability to explore radical alternative design variants. In this publication, a function modeling approach is suggested to increase the amount and variety of explored alternatives, since function models (FM) provide greater model flexibility. An enhanced function-means (EF-M) model capable of representing the constraints of the design space as well as alternative designs is created through a reverse engineering process. This model is then used as a basis for the development of a new product variant. This work describes the EF-M model's capabilities for representing the design space and integrating novel solutions into the existing product structure and explains how these capabilities support the exploration of alternative design variants. First-order analyses are executed, and the EF-M model is used to capture and represent already existing design information for further analyses. Based on these findings, a design space exploration approach is developed. It positions the FM as a connection between legacy and novel designs and, through this, allows for the exploration of more diverse product concepts. This approach is based on three steps – decomposition, design, and embodiment – and builds on the capabilities of EF-M to model alternative solutions for different requirements. While the embodiment step of creating the novel product's geometry is still a topic for future research, the design space exploration concept can be used to enable wider, more methodological, and potentially automated design space exploration. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
08900604
Volume :
33
Issue :
4
Database :
Academic Search Index
Journal :
AI EDAM
Publication Type :
Academic Journal
Accession number :
140961124
Full Text :
https://doi.org/10.1017/S0890060419000271