Manufacturing Process Control Using Vision and ML Quality Feedback
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Solution Overview
Problem
Current manufacturing processes often rely on human operators for assembly tasks due to challenges in automating low-quantity production runs, which can result in variability and inefficiency, as robotic systems are costly and difficult to deploy for such tasks.
Innovation Solution
A manufacturing system that includes monitoring platforms and control modules to dynamically adjust processing parameters using image data and machine learning models to predict and achieve desired quality metrics, allowing for real-time correction of manufacturing processes without destructive testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If human operators are used for assembly tasks, then flexibility and adaptability are maintained, but manufacturing precision and productivity are reduced
Solution Approach 1:
The patent replaces human operators with an automated system comprising robotic manipulators, computer vision systems, and machine learning models. The vision system captures images of components, the ML model predicts quality metrics, and robotic systems perform assembly tasks with high precision, eliminating human variability while maintaining adaptability through software-based control
Solution Approach 2:
The system implements continuous feedback loops where the computer vision system monitors component positions and qualities in real-time, the ML model predicts quality metrics based on captured images, and the system dynamically adjusts processing parameters to maintain quality consistency. This closed-loop control enables automated systems to adapt to variations and maintain high precision
2Manufacturing precision
If robotic systems are deployed for low-quantity production runs, then manufacturing precision and productivity are improved, but device complexity and cost increase
Solution Approach 1:
The patent designs a universal automated system that can handle multiple component types and assembly tasks through reconfigurable robotic manipulators and adaptable vision systems. The machine learning model is trained on diverse data to recognize various components, enabling the same system to perform different assembly operations without requiring dedicated equipment for each task
Solution Approach 2:
The system achieves adaptability for different production runs by changing software parameters, machine learning models, and processing instructions rather than physical reconfiguration. The vision system can be retuned via software, and the ML model can be retrained on new data, allowing the same hardware infrastructure to handle varying production requirements with minimal complexity increase
3Manufacturing precision
If real-time monitoring and adjustment systems are implemented, then manufacturing precision and quality control are improved, but device complexity and processing time increase
Solution Approach 1:
The system performs preliminary quality assessment using the machine learning model that predicts final quality metrics before the assembly process is complete. By analyzing component images and predicting quality outcomes in advance, the system can proactively adjust processing parameters to prevent quality deviations, reducing the need for complex real-time intervention systems
Data Source
AI summary
A manufacturing system is disclosed herein. The manufacturing system includes one or more stations, a monitoring platform, and a control module. Each station of the one or more stations is configured to perform at least one step in a multi-step manufacturing process for a component. The monitoring platform is configured to monitor progression of the component throughout the multi-step manufacturing process. The control module is configured to dynamically adjust processing parameters of each step of the multi-step manufacturing process to achieve a desired final quality metric for the component.