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

This paper presents the development of a multi-sensor user interface tofacilitate the instruction of arc welding tasks. Traditional methods to acquirehand-eye coordination skills are typically conducted through one-to-oneinstruction where trainees must wear protective helmets and conduct severaltests. This approach is inefficient as the harmful light emitted from theelectric arc impedes the close monitoring of the process; Practitioners canonly observe a small bright spot. To tackle these problems, recent trainingapproaches have leveraged virtual reality to safely simulate the process andvisualize the geometry of the workpieces. However, the synthetic nature ofthese types of simulation platforms reduces their effectiveness as they fail tocomprise actual welding interactions with the environment, which hinders thetrainees' learning process. To provide users with a real welding experience, wehave developed a new multi-sensor extended reality platform for arc weldingtraining. Our system is composed of: (1) An HDR camera, monitoring the realwelding spot in real-time; (2) A depth sensor, capturing the 3D geometry of thescene; and (3) A head-mounted VR display, visualizing the process safely. Ourinnovative platform provides users with a "bot trainer", virtual cues of theseam geometry, automatic spot tracking, and performance scores. To validate theplatform's feasibility, we conduct extensive experiments with several weldingtraining tasks. We show that compared with the traditional training practiceand recent virtual reality approaches, our automated multi-sensor methodachieves better performances in terms of accuracy, learning curve, andeffectiveness.

A Multi-Sensor Interface to Improve the Learning Experience in Arc Welding Training Tasks