Critical joint identification for efficient sequencing
Journal article, 2021

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

PCA

Critical joint

Assembly

Machine learning

Sequence

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 Intelligent Manufacturing

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

Vol. 32 3 769-780

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

Control Engineering

Computer Science

Areas of Advance

Production

DOI

10.1007/s10845-020-01660-4

More information

Latest update

3/17/2021