Pipe Weld Seam Tracking With Vision-Based Stitch Detection
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Solution Overview
Problem
Existing systems for monitoring and automatically adjusting welding parameters in pipe welding operations lack precision and efficiency, particularly when dealing with stitched pipe sections.
Innovation Solution
A method utilizing a camera-mounted robotic welding system that continuously captures and processes frames to detect seam positions, stitch starts, and ends, allowing for real-time adjustments of welding parameters to ensure precise tracking and welding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If laser scanners are used for seam tracking in pipe welding, then the welding operation can be monitored, but the precision of seam tracking is limited
Solution Approach 1:
The patent replaces mechanical laser scanners with an optical camera-based vision system. The camera captures images of the weld seam and stitching marks, and image processing algorithms determine seam position and stitching mark locations, eliminating the need for mechanical scanning components while achieving superior precision.
Solution Approach 2:
The system creates optical copies (images) of the physical weld seam and stitching marks. By capturing visual information and processing it to extract positional data, the system replicates the measurement function of laser scanners using light-based imaging instead of mechanical scanning.
2Measurement precision
If traditional vision systems are used for seam tracking, then the system can detect seam position, but it cannot accurately detect stitching mark positions for parameter adjustment
Solution Approach 1:
The system exploits the visual contrast between stitching marks and the pipe surface. Stitching marks appear as distinct dark features against the lighter pipe background in the captured images. The image processing algorithm detects these contrasting features to identify stitching mark positions, enabling automatic welding parameter adjustments at stitch start and end points.
Solution Approach 2:
The patent introduces an intermediate image processing stage that bridges the camera and the welding control system. The processor analyzes captured images to detect both seam edges and stitching marks, then translates this visual information into control signals for the welding robot, enabling coordinated seam tracking and stitch detection functions.
3Extent of automation
If manual monitoring of welding operations is performed, then welding parameters can be observed, but automatic adjustment of welding parameters cannot be achieved
Solution Approach 1:
The system implements a closed-loop feedback control mechanism. The camera continuously monitors the weld seam and stitching marks, the processor analyzes the visual data to determine current position and detect stitch events, and the controller automatically adjusts welding parameters based on this feedback. This enables fully automatic welding parameter control responsive to real-time seam and stitch conditions.
Solution Approach 2:
The welding system performs self-monitoring and self-adjustment. The vision system automatically detects seam position and stitching marks without external intervention, and the control system automatically modifies welding parameters in response to detected features, eliminating the need for manual operation while maintaining adaptive control.
4Productivity
If the welding system rotates pipe sections continuously, then seam tracking can be performed, but real-time detection of stitch start and end points becomes difficult
Solution Approach 1:
The system performs preliminary detection of stitching marks before the welding torch reaches them. By continuously analyzing images captured during pipe rotation, the system identifies upcoming stitching marks in advance, allowing the control system to prepare for parameter adjustments and ensuring accurate detection despite the rotating motion.
Solution Approach 2:
The patent implements a dynamic image processing approach that adapts to the rotating pipe motion. The system processes a sequence of images captured during rotation, using temporal analysis to distinguish stationary stitching marks from the moving seam and torch. This dynamic processing enables accurate stitch position detection while maintaining continuous welding progress.
Data Source
AI summary
The present disclosure provides a method for controlling a robotic welding system to weld pipe sections wherein the pipe sections are held in fixed relation to each other by a plurality of stitches at a seam between the pipe sections. The method comprises rotating the pipe sections so a camera may determine the seam position, moving a torch arm and welding torch so that the torch is over one of the plurality of stitches, adjusting welding parameters and determining stitch start when welding torch is over a stitch and further adjusting welding parameters and determining stitch end when welding torch moves past one of the plurality of stitches.