Welding Robot Point-Cloud Feedback for Dynamic Weld Path Correction
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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 that uses sensors to generate point cloud data of the workspace, compares an estimated state to a desired state, and updates welding instructions in real-time, including adjustments to the welding robot's motion and parameters, allowing for precise welding without prior knowledge of the seam shape and accommodating occlusions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If model-based welding instructions (CAD/kinematic models) are used to control the welding robot, then the welding process can be automated and executed without real-time intervention, but the welding precision deteriorates due to discrepancies between model dimensions and actual part dimensions
Solution Approach 1:
The system employs sensors (laser emitter, camera) to capture real-time workspace data and compares the actual part geometry against the CAD model, generating feedback signals that dynamically adjust welding instructions to compensate for dimensional discrepancies between the model and actual part
Solution Approach 2:
The welding instructions are transformed from static model-based paths to dynamic real-time adjusted trajectories, allowing the system to adapt the welding robot's motion and parameters continuously during the welding process based on actual part geometry detection
2Ease of manufacture
If fixed welding instructions are provided to the welding robot, then the programming process is simplified and execution is straightforward, but the system loses adaptability when changes occur to the welding operation, seam, or part
Solution Approach 1:
Real-time sensor feedback enables the system to detect changes in the welding operation, seam position, or part geometry and dynamically generate adjusted welding instructions, maintaining adaptability while preserving the simplicity of initial programming
Solution Approach 2:
The system performs preliminary sensing and comparison of actual versus model geometry before welding begins, pre-adjusting the welding instructions to account for anticipated variations, thereby maintaining both programming simplicity and operational adaptability
3Manufacturing precision
If real-time sensor data and dynamic adjustment are implemented, then welding precision and adaptability are improved, but the system complexity increases due to additional sensors, data processing, and control mechanisms
Solution Approach 1:
The sensor system and control architecture are designed to perform multiple functions: workspace data acquisition, geometry comparison, deviation detection, and dynamic instruction generation, thereby achieving high welding precision while managing system complexity through multi-functional integration
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.