Efficient Spot Welding Sequence Optimization in a Geometry Assurance Digital Twin
Journal article, 2020

A digital twin for geometry assurance contains a set of analyses that are performed to steer the real production for securing the geometry of the final assembly. In sheet metal assemblies, spot welding is performed to join the parts together. The sequence of the welding has a considerable influence on the geometrical outcome of the final assembly. In industry, the sequence of welding to secure the geometry is mainly derived by tacit manufacturing knowledge. Including such knowledge to mimic the production process requires extensive knowledge management, and the result might be just a good enough solution. Theoretically, spot welding sequence optimization for the optimal geometrical quality is among NP-hard combinatorial problems. In a geometry assurance digital twin, where assembly parameters are selected for the individual assemblies, time constraints define the quality of the optimal sequence. In this paper, an efficient method for spot welding sequence optimization with regards to the geometrical quality is introduced. The results indicate that the proposed method reduces 60–80% of the time for the sequencing of the spot welding process to achieve the optimal geometrical quality.

simulation-based design

digital twin

design optimization

robust design

compliant mechanisms

joining sequence

tolerance analysis and design

Author

Roham Sadeghi Tabar

Chalmers, Industrial and Materials Science, Product Development

Kristina Wärmefjord

Chalmers, Industrial and Materials Science, Product Development

Rikard Söderberg

Chalmers, Industrial and Materials Science

Lars Lindkvist

Chalmers, Industrial and Materials Science, Product Development

Journal of Mechanical Design - Transactions of the ASME

1050-0472 (ISSN)

Vol. 142 10 102001

Smart Assembly 4.0

Swedish Foundation for Strategic Research (SSF) (RIT15-0025), 2016-05-01 -- 2021-06-30.

Subject Categories

Mechanical Engineering

Areas of Advance

Production

DOI

10.1115/1.4046436

More information

Latest update

12/4/2020