Robot ML Control for Weld Parameter and Trajectory Adjustment
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Solution Overview
Problem
Conventional robots face challenges due to unique operational characteristics, such as calibration errors and mechanical/electrical variations, leading to inconsistent manufacturing outputs and the need for time-consuming parameter adjustments, which complicates robot installation and operation.
Innovation Solution
Implementing machine learning logic, such as an artificial neural network, to model and adapt to operational characteristics, allowing for training and updating based on manufacturing data to improve the quality and efficiency of manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional robots are used with fixed manufacturing parameters, then the robot structure and control system remain simple, but the manufacturing precision deteriorates due to calibration errors and operational variations
Solution Approach 1:
The system implements feedback by capturing sensor data from actual welds and comparing it against target weld profiles. The machine learning model uses this feedback to automatically adjust manufacturing parameters, eliminating the need for manual parameter tuning while maintaining consistent weld quality across different robots and operating conditions.
Solution Approach 2:
The machine learning model enables the robot system to self-adjust manufacturing parameters autonomously. By continuously learning from captured weld data and operational characteristics, the system automatically optimizes its own performance without requiring external intervention or complex manual calibration procedures.
2Manufacturing precision
If machine learning logic is implemented to adapt to operational characteristics, then the manufacturing precision improves, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The machine learning model serves multiple functions: it characterizes robot operational variations, predicts optimal manufacturing parameters, and adjusts trajectories in real-time. This multi-functionality allows a single computational system to handle multiple aspects of precision control without requiring separate dedicated systems for each function.
Solution Approach 2:
The machine learning model acts as an intermediary layer between the robot controller and the manufacturing process. It processes sensor data and operational characteristics to generate adjusted manufacturing parameters, mediating between raw sensor inputs and final execution commands to achieve precise weld profiles.
3Manufacturing precision
If robot-specific parameter adjustments are made to account for operational characteristics, then the manufacturing precision improves, but the ease of operation deteriorates due to time-consuming calibration processes
Solution Approach 1:
The system performs preliminary characterization of robot operational characteristics by capturing sensor data during initial operation. The machine learning model learns and stores optimal parameter adjustments in advance, so that when actual manufacturing begins, the robot is already optimized for its specific operational characteristics without requiring time-consuming manual calibration.
Solution Approach 2:
The machine learning model automatically performs the calibration and optimization process that would otherwise require manual intervention. By autonomously capturing operational data, analyzing variations, and adjusting parameters, the system eliminates the need for operators to perform complex calibration procedures during installation and setup.
4Manufacturing precision
If manufacturing parameters are adjusted on a robot-by-robot basis, then the manufacturing precision improves, but the productivity deteriorates due to increased setup time for each robot
Solution Approach 1:
Each robot system autonomously characterizes its own operational characteristics and optimizes its parameters through the machine learning model. This self-service capability eliminates the need for centralized calibration procedures or manual intervention for each robot, allowing multiple robots to be deployed and optimized simultaneously without sequential setup time.
Solution Approach 2:
The system performs parameter optimization in the background during initial operation rather than requiring dedicated setup time. By continuously learning and adjusting parameters as the robot begins manufacturing, the system achieves robot-specific optimization without delaying production deployment or requiring sequential calibration of each robot before use.
Data Source
AI summary
This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, that provide for training, implementing, or updated machine learning logic, such as an artificial neural network, to model a manufacturing process performed in a manufacturing robot environment. For example, the machine learning logic may be trained and implemented to learn from or make adjustments based on one or more operational characteristics associated with the manufacturing robot environment. As another example, the machine learning logic, such as a trained neural network, may be implemented in a semi-autonomous or autonomous manufacturing robot environment to model a manufacturing process and to generate a manufacturing result. As another example, the machine learning logic, such as the trained neural network, may be updated based on data that is captured and associated with a manufacturing result. Other aspects and features are also claimed and described.