Robotic Welding Torch Learning for Complex Path Programming
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Solution Overview
Problem
Conventional robotic welding systems require tedious and time-consuming manual parameter input for programming, limiting their flexibility and suitability for low-volume manufacturing tasks with complex geometries and varied workpieces, such as shipbuilding.
Innovation Solution
A robotic welding system that learns desired operating parameters from a human operator's real-time demonstration, monitoring the motion of a welding torch to determine appropriate parameters and transitioning seamlessly from manual to autonomous operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual parameter input programming is used, then the robotic welding system can perform welding operations, but the programming process becomes tedious and time-consuming
Solution Approach 1:
The robotic welding system performs self-learning by automatically capturing motion data from manual welding demonstrations and generating control parameters without human intervention. The system serves itself by converting demonstrated motions into executable welding programs, eliminating the need for manual parameter input and reducing programming time.
Solution Approach 2:
The system performs preliminary learning by capturing and storing motion data during a demonstration phase before actual welding operations. This preliminary action of recording and analyzing human operator movements allows the system to pre-generate control parameters, so that subsequent welding tasks can be executed automatically without repeated programming.
2Adaptability or versatility
If conventional robotic systems are used, then welding operations can be automated, but flexibility for complex geometries and varied workpieces is limited
Solution Approach 1:
The system uses sensors to continuously monitor the actual welding torch position and compares it with the demonstrated trajectory. This feedback mechanism allows the system to learn deviations and adjustments made by the human operator, adapting the control parameters to achieve precise reproduction of complex welding paths while simplifying operation for future tasks.
Solution Approach 2:
The system replaces manual mechanical programming operations with automated sensor-based motion capture and digital processing. Instead of manually inputting parameters through mechanical interfaces, the system uses sensors to automatically record and digitize the welding torch trajectory, converting physical demonstrations into digital control data for automated execution.
3Adaptability or versatility
If manual parameter input is required, then the system can be programmed, but the process lacks adaptability for low-volume manufacturing with varied workpieces
Solution Approach 1:
The robotic system performs self-programming by automatically capturing motion data from manual demonstrations and generating control parameters without human intervention. This self-service capability eliminates the need for complex manual programming processes while enabling rapid adaptation to different workpieces and welding operations.
Data Source
AI summary
A robotic welding systems and related methods are described. In some embodiments, a robotic welding system may generate a target trajectory based at least partly on a trajectory of a welding torch during manual operation of the welding torch; and operate one or more actuators of the system to control movement of the welding torch based at least partly on the target trajectory.