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

TL;DR

This paper introduces a multi-sensor extended reality platform for arc welding training that enhances real-world interaction, safety, and learning effectiveness compared to traditional and virtual reality methods.

Contribution

The paper presents a novel multi-sensor XR system integrating HDR, depth sensing, and VR to improve arc welding training with real-time feedback and performance assessment.

Findings

Outperforms traditional training in accuracy and learning speed

Provides safer, real-time visualization of welding processes

Enhances trainee engagement and skill acquisition

Abstract

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