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.

enhanced function-means modeling

function modeling

design space exploration

radical innovation

Concept design

DSM

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

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)

Ämneskategorier

Arkitekturteknik

Design

Systemvetenskap

DOI

10.1017/S0890060419000271

Mer information

Senast uppdaterat

2019-11-11