Autonomous Decision-Making of Welding Position During Multipass GMAW With T-Joints: A Bayesian Network Approach

This work investigates a Bayesian network model (BNM) to implement the autonomous decision-making process of welding positions in gas metal arc multipass welding with T-joints for automated manufacturing. The laser vision sensor is used to profile the weld seam, and the weld seam profile (WSP) is ex...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on industrial electronics (1982) 2022-04, Vol.69 (4), p.3909-3917
Main Authors: He, Yinshui, Ma, Guohong, Chen, Shanben
Format: Article
Language:English
Subjects:
Algorithms
Analytic hierarchy process
Automatic welding
Bayesian analysis
Bayesian networks (BNs)
Decision analysis
Decision making
Decision trees
Feature extraction
Gas metal arc welding
gas metal arc welding (GMAW)
Importance sampling
Laser beam welding
laser visual sensing
Manufacturing
Mutation
Planning
Seams
Software
Tee joints
thick plate T-joints
Visualization
Welded joints
Welding
welding position decision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work investigates a Bayesian network model (BNM) to implement the autonomous decision-making process of welding positions in gas metal arc multipass welding with T-joints for automated manufacturing. The laser vision sensor is used to profile the weld seam, and the weld seam profile (WSP) is extracted with a novel scheme based on scale-invariant feature transform and orientation feature detection. The feature points of the extracted WSP are effectively identified through slope mutation detection. A BNM is built with these points and the determined welding state online, and the priori probabilities of the model variables are acquired in a computational manner integrated with welding experience. The feature point with the maximum posteriori probability is selected as the current welding position using the evidence prepropagation importance sampling inference algorithm. The analytic hierarchy process and the C4.5 decision tree algorithm are used to compare with the proposed BNM regarding decision effectiveness. Experimental results show that the proposed BNM can give the effective decision-making results of welding positions with T-joints of different thicknesses and show great potential for higher manufacturing efficiency and automatic levels.
ISSN:0278-0046
1557-9948
DOI:10.1109/TIE.2021.3076710