Welding Robot Feedback Control for Real-Time Seam Adjustment
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Solution Overview
Problem
Conventional robotic welding systems lack real-time feedback and dynamic adjustment capabilities, leading to inaccuracies and poor weld quality due to discrepancies between model-based instructions and actual part dimensions, occlusions, and shifts during the welding process.
Innovation Solution
A computer-implemented method using sensors to generate point cloud data of the workspace, comparing estimated and desired states to update welding instructions dynamically, including adjustments to the welding robot's motion and parameters in real-time, without requiring precise pre-defined seam shapes or user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If model-based instructions (CAD models, kinematic models) are used to determine welding paths, then the welding robot can operate automatically, but the manufacturing precision deteriorates due to discrepancies between model dimensions and actual part dimensions
Solution Approach 1:
The system continuously captures images of the part during welding operations, compares actual features to expected features from CAD models, and dynamically adjusts welding instructions based on detected discrepancies. This closed-loop feedback mechanism resolves the contradiction by maintaining automation while compensating for dimensional variations in real-time.
Solution Approach 2:
The welding instructions are transformed from static pre-programmed paths to dynamic adaptive paths that adjust in real-time based on actual part geometry. The system dynamically modifies welding parameters and robot motion based on continuously updated visual feedback, allowing the system to adapt to dimensional variations while maintaining automation.
2Productivity
If pre-defined welding instructions are used, then the welding process can proceed efficiently, but the reliability deteriorates when parts shift or occlusions occur during welding
Solution Approach 1:
The system monitors the welding area continuously during operation, detecting part shifts, occlusions, and other deviations from the planned welding path. When deviations are detected, the system automatically adjusts the welding instructions to maintain weld quality, thereby preserving reliability while continuing efficient automated operation.
Solution Approach 2:
The system autonomously detects and corrects welding deviations without requiring external intervention. The automated visual inspection and dynamic instruction adjustment enable the system to self-correct for part shifts and occlusions, maintaining reliability while preserving productive automated operation.
3Manufacturing precision
If sensors and real-time processing are added to enable dynamic adjustment, then manufacturing precision and reliability improve, but device complexity increases
Solution Approach 1:
The system uses a multi-functional integrated approach where the camera system serves both inspection and guidance functions, and the same processing infrastructure supports both real-time monitoring and dynamic instruction adjustment. This reduces overall system complexity compared to having separate dedicated systems for each function.
Solution Approach 2:
The system introduces an intermediate visual inspection layer that bridges the gap between pre-programmed welding instructions and actual welding execution. This intermediary system processes visual data and generates corrected instructions, acting as a mediator that improves precision without requiring direct complex modifications to the core welding system.
Data Source
AI summary
Systems and methods for real time feedback and for updating welding instructions for a welding robot in real time is described herein. The data of a workspace that includes a part to be welded can be received via at least one sensor. This data can be transformed into a point cloud data representing a three-dimensional surface of the part. A desired state indicative of a desired position of at least a portion of the welding robot with respect to the part can be identified. An estimated state indicative of an estimated position of at least the portion of the welding robot with respect to the part can be compared to the desired state. The welding instructions can be updated based on the comparison.