Enhanced function-means modeling supporting design space exploration
Artikel i vetenskaplig tidskrift, 2019

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-A nd 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.

design space exploration

function modeling

DSM

Concept design

enhanced function-means modeling

radical innovation

Författare

Jakob Müller

Chalmers, Industri- och materialvetenskap, Produktutveckling

Ola Isaksson

Chalmers, Industri- och materialvetenskap, Produktutveckling

Jonas Landahl

Chalmers, Industri- och materialvetenskap, Produktutveckling

Visakha Raja

GKN Aerospace Services

Chalmers, Industri- och materialvetenskap, Produktutveckling

Massimo Panarotto

Chalmers, Industri- och materialvetenskap, Produktutveckling

Christoffer E Levandowski

Chalmers, Industri- och materialvetenskap, Produktutveckling

Dag Raudberget

Chalmers, Industri- och materialvetenskap, Produktutveckling

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM

0890-0604 (ISSN) 1469-1760 (eISSN)

Vol. 33 4 502-516

Ämneskategorier

Arkitekturteknik

Design

Systemvetenskap

DOI

10.1017/S0890060419000271

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2022-04-06