Weld Torch Angle Correction from 3D Seam Depth Data
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Solution Overview
Problem
Programming and setting torch angles for robotic welding or cutting systems are complex and require precise manual adjustments, which can be time-consuming and prone to errors, especially when training robots to traverse weld joints.
Innovation Solution
A robotic welding system equipped with a depth camera that acquires stereoscopic depth image data to determine and correct torch angles, allowing users to record and adjust push and work angles relative to the weldment and seam, using 3D point cloud and plane data to align with pre-stored ideal angles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual positioning of torch angles is used during robot training, then the robot can be programmed to traverse weld joints, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The patent replaces manual mechanical positioning of the torch with an automated vision-based system. A depth camera captures stereoscopic images of the weld joint, and image processing algorithms automatically calculate the optimal torch angles (work angle and push angle) relative to the weld seam geometry, eliminating the need for time-consuming manual angle setting while improving precision
Solution Approach 2:
The system enables the robot to self-correct its torch orientation by automatically analyzing weld joint geometry from depth images and computing the ideal torch angles. The robot controller uses this information to adjust the torch orientation parameters without human intervention, making the system self-sufficient during the training process
2Ease of operation
If manual torch angle positioning is required, then torch orientation can be set, but the complexity of the programming process increases
Solution Approach 1:
The patent replaces complex manual programming of torch angles with an automated vision system that captures weld joint geometry and automatically computes optimal torch orientations. The depth camera and image processing algorithms handle the complex calculations, simplifying the operator's task to merely positioning the torch near the weld joint while the system determines precise angle parameters
3Manufacturing precision
If precise manual adjustments of torch angles are made, then weld quality can be improved, but the process becomes more time-consuming
Solution Approach 1:
The system automatically determines optimal torch angles by analyzing weld joint geometry from depth images, enabling the robot to self-correct its orientation parameters. This automated self-adjustment maintains consistent weld quality by precisely matching torch angles to the actual weld joint geometry without requiring time-consuming manual trial and error adjustments
Solution Approach 2:
The system performs preliminary analysis of weld joint geometry using depth camera imaging before the robot begins welding. By pre-calculating the optimal torch angles based on the captured weld seam geometry, the system prepares the correct orientation parameters in advance, ensuring weld quality consistency while eliminating time-consuming adjustments during actual welding operations
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and efficient correction of torch angles during robot training, reducing the need for precise manual adjustments and improving the consistency of weld quality by automating the alignment process.
Implementation Method 1
a depth camera that acquires stereoscopic depth image data which is used to determine the actual torch angles of the torch
Data Source
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AI summary
A method of correcting angles of a welding torch positioned by a user while training a robot of a robotic welding system is provided. Weldment depth data of a weldment and a corresponding weld seam is acquired and 3D point cloud data is generated. 3D plane and intersection data is generated from the 3D point cloud data, representing the weldment and weld seam. User-placed 3D torch position and orientation data for a recorded weld point along the weld seam is imported. A torch push angle and a torch work angle are calculated for the recorded weld point, with respect to the weldment and weld seam, based on the user-placed torch position and orientation data and the 3D plane and intersection data. The torch push angle and the torch work angle are corrected for the recorded weld point based on pre-stored ideal angles for the weld seam.