.png){kind=icon}
Return
Critical joint identification for efficient sequencing
.png){kind=icon}
OA
AI
DOI：10.1007/s10845-020-01660-4.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Critical joint
Sequence
Machine learning
PCA
Assembly
SVM
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.4
Papers:
3.5K
Citations:
1.1W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more