Robotic Arc Welding Systems and Seam Tracking Techniques

Robotic arc welding systems integrate advanced automation, sensing and control strategies to perform high-precision joining of metallic components. At their core, these systems employ programmable robot manipulators equipped with arc welding torches and a suite of sensors—optical, laser and acoustic—to monitor weld pool characteristics and seam geometry in real time. By coupling sensor feedback with adaptive control algorithms, such systems can compensate for variances in workpiece alignment, joint geometry and thermal distortion. Seam tracking techniques enable the robotic torch to maintain alignment with complex joint paths, ranging from straight butt joints to spatially intermittent seams on large structures. Common sensing modes include laser triangulation, structured light projection and vision-based image processing, often enhanced by filtering algorithms such as Kalman smoothing. Together, these innovations reduce reliance on manual teaching, enhance consistency, improve weld quality and accelerate production in industries such as automotive, shipbuilding and heavy fabrication.

Research from Nature Portfolio

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Research from all publishers

Recent studies have advanced vision-based detection for narrow gap joints in robotic gas tungsten arc welding. A crosshair laser coupled with auxiliary illumination enables simultaneous capture of 2D and 3D joint data, from which the joint centreline and surface orientation are computed. Kalman filtering of successive measurements smooths the seam path, significantly reducing torch dithering and improving tracking accuracy in narrow butt configurations.

A novel four-step seam tracking technique employs a laser line scanner to acquire groove profiles, applies a second-derivative algorithm for coarse feature localisation and performs linear fitting for precise path definition. Sensor-to-robot calibration transforms groove coordinates into the robot frame in real time, yielding sub-millimetre deviations on both straight and curved seams and enhancing adaptability to varying joint geometries.

Automated skip-welding of spatially discontinuous seams has been realised through laser scanning displacement sensing and shape-feature extraction via Euclidean distance discrimination. By constructing characteristic triangles from seam trajectories and analysing them in a feature library, the system achieves 100 % classification accuracy of discontinuous weld segments and guides the torch without pre-programmed paths, enabling high-speed metal active gas arc welding on complex box girder assemblies.

Robotic Arc Welding Systems and Seam Tracking Techniques publication trend

The graph below shows the total number of articles in robotic arc welding systems and seam tracking techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Robotic arc welding: Automated welding process employing robotic manipulators to guide an electric-arc torch along a joint.

Seam tracking: Active control method to detect and follow the weld path in real time, ensuring torch alignment with the joint.

Laser triangulation: Optical technique projecting a laser line onto a surface and capturing its deformation to reconstruct 3D profiles.

Structured light: Technique using projected light patterns to derive surface geometry by analysing pattern distortions.

Kalman filter: Recursive algorithm for estimating and smoothing system states from noisy sensor measurements.

References

1. Robotic arc welding sensors and programming in industrial applications. Journal of Materials Science: Materials in Engineering (2015).
2. A Vision Based Detection Method for Narrow Butt Joints and a Robotic Seam Tracking System. Sensors (2019).
3. A Novel Seam Tracking Technique with a Four-Step Method and Experimental Investigation of Robotic Welding Oriented to Complex Welding Seam. Sensors (2021).
4. Welding Seam Trajectory Recognition for Automated Skip Welding Guidance of a Spatially Intermittent Welding Seam Based on Laser Vision Sensor. Sensors (2020).