DEFAINE (Design Exploration Framework based onAI for froNt-loaded Engineering)
Research Project , 2020 – 2023

Purpose and goal: The overall objective for the Swedish part of the project is to advance large scale design exploration capabilities using distributed and scalable computing infrastructures augmented by Artificial Intelligence (AI) techniques.
Such advancements based on front-loading principles aim to: Reduce recurring cost in design of aircraft by 10% and Reduce the lead-time for design updates by 50%. Expected results and effects: By collaborating on an international environment, the Swedish consortium aim to: - Bring in and develop state of the art intelligent automation design technologies to aerospace industry in Sweden. - Ensure and advance university participants to be in the forefront internationally and impact engineering education using results and test cases - Foster the Swedish software company to develop and validate results and implement SoA techniques into Swedish manufacturers’ existing software environments Approach and implementation: The project takes on an iterative approach, where in total 3 principal iterations are scheduled to develop the main software-based deliverables. These software deliverables form the components of the architecture for the front-loaded design process. The iterative approach allows for an agile development of the architecture and its components. The industrial use cases play an important role providing the specifications and benchmark as input to this development, as well as the verification & validation of the results of each iteration.

Participants

Ola Isaksson (contact)

Full Professor at Chalmers, Industrial and Materials Science, Product Development

Alejandro Pradas Gómez

Doctoral Student at Chalmers, Industrial and Materials Science, Product Development

Collaborations

GKN Aerospace Sweden

Trollhättan, Sweden

Linköping University

Linköping, Sweden

PE Geometry

Mölndal, Sweden

Saab

Stockholm, Sweden

Funding

VINNOVA

Project ID: 2020-01951
Funding Chalmers participation during 2020–2023

More information

Latest update

2022-03-01