Design of integrated neural network model for weld seam tracking and penetration monitoring

Ding, Dukun

doi:10.1007/s10586-017-1084-0

Cited by 7 publications

(1 citation statement)

References 12 publications

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“…Similarly, the laser feature extraction algorithm based on pixels' intensity distribution and neighborhood search is proposed in Muhammad et al (2018); however, the method lacks discussion on different types of welding profile. Ding (2017) proposed a neural network model to extract the welding seam features and obtained a well-experimental result, but the drawback of this method is requirement of a great deal of prior knowledge. In Sicard and Levine (1989), to classify various modes of joints that were detected by a laser scanners [1], a character string method was developed.…”

Section: Introductionmentioning

confidence: 99%

A welding seam identification method based on cross-modal perception

Pei

Khyam

et al. 2019

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Purpose As an automatic welding process may experience some disturbances caused by, for example, splashes and/or welding fumes, misalignments/poor positioning, thermally induced deformations, strong arc lights and diversified welding joints/grooves, precisely identifying the welding seam has a great influence on the welding quality. This paper aims to propose a robust method for identifying this seam based on cross-modal perception. Design/methodology/approach First, after a welding image obtained from a structured-light vision sensor (here laser and vision are integrated into a cross-modal perception sensor) is filtered, in a sufficiently small area, the extended Kalman filter is used to prevent possible disturbances to search for its laser stripe. Second, to realize the extraction of the profile of welding seam, the least square method is used to fit a sequence of centroids determined by the scanning result of columns displayed on the tracking window. Third, this profile is then qualitatively described and matched using a proposed character string method. Findings It is demonstrated that it maintains real time and is clearly superior in terms of accuracy and robustness, though its real-time performance is not the best. Originality/value This paper proposes a robust method for automatically identifying and tracking a welding seam.

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Section: Introductionmentioning

confidence: 99%