Robot Manufacturing Control Using Neural Feedback Adjustment
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Solution Overview
Problem
Conventional robots face challenges in manufacturing tasks due to unique operational characteristics, such as calibration errors and mechanical variations, leading to inconsistent manufacturing outputs and increased complexity in parameter determination, which complicates installation and operation.
Innovation Solution
Implementing machine learning logic, specifically an artificial neural network, to model and adjust manufacturing processes in robots, allowing them to learn from operational characteristics and adapt to errors, thereby improving the quality and efficiency of manufacturing outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional robots are used to perform manufacturing operations, then the robot can accomplish basic manufacturing tasks, but the robot produces inconsistent manufacturing outputs due to calibration errors and mechanical variations
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from actual manufacturing outputs is continuously fed back to the machine learning model. The model compares predicted outputs with actual measurements and adjusts its parameters accordingly, enabling the robot to compensate for calibration errors and mechanical variations, thereby improving manufacturing precision and operational reliability
Solution Approach 2:
The patent dynamically changes operational parameters by using a machine learning model that predicts optimal welding parameters (such as current, voltage, travel speed) based on real-time sensor data and historical performance. This adaptive parameter adjustment compensates for robot-specific variations and calibration errors, improving output consistency without requiring manual recalibration
2Manufacturing precision
If manual parameter determination is performed for each robot, then the robot can be customized to its operational characteristics, but the installation and operation complexity increases significantly
Solution Approach 1:
The patent implements self-service by enabling the robot system to automatically determine and optimize its own operational parameters through machine learning. The system autonomously analyzes sensor data, identifies performance patterns, and adjusts parameters without requiring manual intervention or expert calibration, thereby maintaining parameter accuracy while dramatically reducing installation and operational complexity
Solution Approach 2:
The patent replaces manual mechanical calibration processes with an automated machine learning system. Instead of requiring technicians to physically adjust robot components and parameters based on trial-and-error or expert knowledge, the system uses computational algorithms to automatically optimize parameters, substituting complex manual procedures with intelligent automation
3Manufacturing precision
If machine learning logic is implemented to model manufacturing processes, then the quality of manufacturing outputs is enhanced, but the computational requirements and system complexity increase
Solution Approach 1:
The patent segments the manufacturing system into distinct functional modules: a machine learning model for prediction, sensor systems for data collection, and control systems for parameter adjustment. This modular segmentation allows the complex machine learning functionality to be integrated incrementally and maintained independently, reducing overall system complexity while preserving output quality enhancement
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.