Robert bosch gmbh (20240211722). Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing simplified abstract
Contents
Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing
Organization Name
Inventor(s)
Alexander Buhmann of Reutlingen (DE)
Monika Heringhaus of Stuttgart (DE)
Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240211722 titled 'Device and Method for Parameter Estimation in Micro-Electro-Mechanical System Testing
The abstract describes a computer-implemented method for training a graph neural network to predict second measurement results of produced products based on received first measurement results.
- The method involves receiving first and second measurement results for multiple products, constructing graphs of the first measurements, and generating a training data set by assigning the corresponding second measurements to the graphs.
- The graph neural network is then trained on the data set to predict the second measurements based on the graphs.
Potential Applications:
- Quality control in manufacturing processes
- Predictive maintenance in industrial settings
- Product optimization based on measurement data
Problems Solved:
- Improving accuracy in predicting product quality
- Enhancing efficiency in analyzing measurement results
- Streamlining decision-making processes in production
Benefits:
- Increased productivity and cost savings
- Enhanced product quality and consistency
- Real-time monitoring and decision support
Commercial Applications:
- Quality assurance software for manufacturing companies
- Industrial IoT solutions for predictive analytics
- Data-driven optimization tools for production processes
Questions about the technology: 1. How does this method compare to traditional predictive modeling techniques?
- The method leverages graph neural networks to capture complex relationships in the data, potentially leading to more accurate predictions.
2. What are the scalability considerations for implementing this technology in large-scale production environments?
- Scalability may depend on factors such as data volume, computational resources, and network architecture.
Original Abstract Submitted
a computer-implemented method of training a graph neural network for predicting second measurement results of produced products based on received first measurement results is disclosed. the method includes (i) receiving first measurement and second measurement results for a plurality of produced products, (ii) constructing graphs of the first measurements and generating a training data set by assigning the corresponding second measurement of the first measurement to the corresponding graphs, respectively, and (iii) training the graph neural network on the training data set to predict the second measurements based on the graphs.
