A method for identification and sequence optimisation of geometry spot welds in a digital twin context
Artikel i vetenskaplig tidskrift, 2019

Geometrical variation is the main cause of the aesthetic and functional problems in the product geometry. Variation and disturbances are caused by several sources during the manufacturing process. In the automotive industry, one of the main sources of variation is the spot welding sequence. Optimising this sequence is of combinatorial Nondeterministic Polynomial (NP)-hard problems. In a typical automotive sheet metal assembly, there are a large number of spot welds. Today, if the number of spot welds in a sub-assembly is more than 10, the sequence optimisation will be a challenging and time-consuming task. Therefore, industry is mainly dependent on the experiential approach or simultaneous welding simulations for predicting the geometrical outcome. In this paper, a method is introduced to identify the geometry weld points to reduce the optimisation problem size in a geometry assurance digital twin context. This method is then applied to three automotive body-in-white assemblies and optimisation is performed. The results show that reducing the size of the problem by the proposed approach can help to save a considerable amount of time while getting geometrical outcomes within the satisfactory error levels.

tolerance design

Manufacturing and assembly

spot welding sequence

optimisation

geometrical variation

Författare

Roham Sadeghi Tabar

Chalmers, Industri- och materialvetenskap, Produktutveckling

Kristina Wärmefjord

Chalmers, Industri- och materialvetenskap, Produktutveckling

Rikard Söderberg

Chalmers, Industri- och materialvetenskap

Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science

0954-4062 (ISSN)

Smart Assembly 4.0

Stiftelsen för Strategisk forskning (SSF), 2016-05-01 -- 2021-06-30.

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Bearbetnings-, yt- och fogningsteknik

Datavetenskap (datalogi)

Styrkeområden

Produktion

DOI

10.1177/0954406219854466

Mer information

Skapat

2019-06-17