English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

High-frequency mechanical impact (HFMI) treatment is a well-documented post-weld treatment to improve the fatigue life of welds. Treatment of the weld toe must be performed by a skilled operator due to the curved and inconsistent nature of the weld toe to ensure an acceptable quality. However, the process is characterised by noise and vibrations; hence, manual treatment should be avoided for extended periods of time. This work proposes an automated system for applying robotised 3D scanning to perform post-weld treatment and quality inspection of linear welds. A 3D scan of the weld is applied to locally determine the gradient and curvature across the weld surface to locate the weld toe. Based on the weld toe position, an adaptive robotic treatment trajectory is generated that accurately follows the curvature of the weld toe and adapts tool orientation to the weld profile. The 3D scan is reiterated after the treatment, and the surface gradient and curvature are further applied to extract the quantitative measures of the treatment, such as weld toe radius, indentation depth, and groove deviation and width. The adaptive robotic treatment is compared experimentally to manual and linear robotic treatment. This is done by treating 600-mm weld toe of each treatment type and evaluating the quantitative measures using the developed system. The results showed that the developed system reduced the overall treatment variance by respectively 26.6% and 31.9%. Additionally, a mean weld toe deviation of 0.09 mm was achieved; thus, improving process stability yet minimising human involvement.

Development of an automated system for adaptive post-weld treatment and quality inspection of linear welds