Automated Welding Controller Using Image Masking for Airfoil Repair
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Solution Overview
Problem
Many known welding systems, particularly airfoil welding systems, are not fully automated, leading to inefficiencies and excess process scrap due to the need for frequent re-training of welders and lack of precision in repairing worn airfoils.
Innovation Solution
An automated welding system that uses an imaging device to acquire data, compares it to stored master model data to identify areas to be welded, and generates control instructions for the mounting platform and welding tool, excluding standard features from the welding process while heating the object to a workable temperature.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual welding operations are used with trained welders, then flexibility and adaptability are maintained, but productivity is reduced and process scrap increases due to frequent re-training requirements
Solution Approach 1:
The welding system performs self-learning by automatically capturing weld data, analyzing it through machine learning algorithms, and generating control instructions without requiring human re-training. The system serves itself by continuously improving its welding capabilities through data accumulation and algorithmic processing, eliminating the need for external training interventions while maintaining high productivity
Solution Approach 2:
The patent replaces the mechanical training process (human welders being trained and re-trained) with an automated digital system that uses machine learning algorithms to learn and adapt. The control system substitutes human cognitive processes with computational algorithms that can process weld data and generate instructions automatically, thereby eliminating the productivity loss associated with human re-training cycles
2Adaptability or versatility
If manual welding operations are used, then complex welding tasks can be performed, but manufacturing precision is reduced due to lack of automation and frequent re-training
Solution Approach 1:
The system implements a closed-loop feedback mechanism where weld data is continuously captured during the welding process, analyzed by machine learning algorithms, and used to generate optimized control instructions. This feedback loop ensures that the system learns from actual welding outcomes and continuously improves precision, maintaining both adaptability and manufacturing precision through data-driven adjustments
Solution Approach 2:
The system creates a digital replica or model of the welding process by capturing and storing weld data. This digital copy allows the system to analyze, simulate, and optimize welding parameters before executing actual welds, thereby improving manufacturing precision while maintaining the flexibility to handle complex welding tasks through virtual testing and optimization
3Productivity
If automated welding is implemented, then productivity and precision are improved, but device complexity increases due to the need for imaging devices, controllers, and machine learning algorithms
Solution Approach 1:
The controller serves multiple functions: it captures weld data through imaging devices, processes data through machine learning algorithms, generates control instructions, and manages the welding tool. By consolidating these diverse functions into a single multi-functional controller, the system achieves high productivity and precision while minimizing the number of separate components, thereby reducing overall system complexity
Solution Approach 2:
The patent merges the imaging system, data processing algorithms, control instruction generation, and welding execution into an integrated automated system. The controller combines multiple subsystems (imaging, analysis, control) into a unified architecture that operates seamlessly, reducing the complexity that would arise from managing separate independent systems while maintaining high productivity and precision
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
This system enables precise, efficient repair of airfoils by automating the welding process, reducing waste and the need for frequent re-training, and ensuring accurate exclusion of standard features from the welding area.
Implementation Method 1
heating the object to a workable temperature
Data Source
AI summary
An automated welding system includes a mounting platform, a welding tool, an imaging device configured to acquire data associated with an object, and a controller. The controller is configured to receive the acquired data, determine an area to be welded in the acquired data, retrieve stored master model data associated with the object, and compare the acquired data to the stored master model data to identify a master model area in the acquired data. The controller is also configured to mask the master model area in the acquired data, such that the master model area is excluded from the area to be welded, and generate control instructions for controlling at least one of the mounting platform and the welding tool to weld the area to be welded.