Developing a selective assembly technique for sheet metal assemblies
Journal article, 2019

Applying the concept of Digital Twin in production processes supports the manufacturing of products of optimal geometry quality. This concept can be further supported by a strategy of finding the optimal combination of individual parts to maximise the geometrical quality of the final product, known as selective assembly technique. However, application of this technique has been limited to assemblies where the final dimensions are just function of the mating parts' dimensions and this is not applicable in sheet metal assemblies. This paper develops a selective assembly technique for sheet metal assemblies and investigates the effect of batch size on the improvements. The presented method utilises a variation simulation tool (Computer-Aided Tolerancing tool) and an optimisation algorithm to find the optimal combination of the mating parts. The approach presented is applied to three industrial cases of sheet metal assemblies. The results show that using this technique leads to a considerable reduction of the final geometrical variation and mean deviation for these kinds of assemblies. Moreover, increasing the batch size reduces the amount of achievable improvement in variation but increases the amount of achievable improvement in the mean deviation.

selective assembly

sheet metal assembly

computer-aided tolerancing

digital twin

Author

Abolfazl Rezaei Aderiani

Chalmers, Industrial and Materials Science, Product Development

Kristina Wärmefjord

Chalmers, Industrial and Materials Science

Rikard Söderberg

Chalmers, Industrial and Materials Science

Lars Lindkvist

Chalmers, Industrial and Materials Science, Product Development

International Journal of Production Research

0020-7543 (ISSN) 1366-588X (eISSN)

Vol. 57 22 7174-7188

Smart Assembly 4.0

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

Subject Categories

Production Engineering, Human Work Science and Ergonomics

DOI

10.1080/00207543.2019.1581387

More information

Latest update

2/25/2021