Critical joint identification for efficient sequencing
Artikel i vetenskaplig tidskrift, 2020

Identifying the optimal sequence of joining is an exhaustive combinatorial optimization problem. On each assembly, there is a specific number of weld points that determine the geometrical deviation of the assembly after joining. The number and sequence of such weld points play a crucial role both for sequencing and assembly planning. While there are studies on identifying the complete sequence of welding, identifying such joints are not addressed. In this paper, based on the principles of machine intelligence, black-box models of the assembly sequences are built using the support vector machines (SVM). To identify the number of the critical weld points, principle component analysis is performed on a proposed data set, evaluated using the SVM models. The approach has been applied to three assemblies of different sizes, and has successfully identified the corresponding critical weld points. It has been shown that a small fraction of the weld points of the assembly can reduce more than 60% of the variability in the assembly deviation after joining.

SVM

Critical joint

Machine learning

Assembly

PCA

Sequence

Författare

Roham Sadeghi Tabar

Chalmers, Industri- och materialvetenskap, Produktutveckling

Kristina Wärmefjord

Chalmers, Industri- och materialvetenskap, Produktutveckling

Rikard Söderberg

Chalmers, Industri- och materialvetenskap

Lars Lindkvist

Chalmers, Industri- och materialvetenskap, Produktutveckling

Journal of Intelligent Manufacturing

0956-5515 (ISSN) 1572-8145 (eISSN)

Vol. In Press

Smart Assembly 4.0

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

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Reglerteknik

Datavetenskap (datalogi)

Styrkeområden

Produktion

DOI

10.1007/s10845-020-01660-4

Mer information

Senast uppdaterat

2020-12-02