18277533. MONITORING APPARATUS FOR QUALITY MONITORING simplified abstract (Siemens Aktiengesellschaft)
Contents
- 1 MONITORING APPARATUS FOR QUALITY MONITORING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MONITORING APPARATUS FOR QUALITY MONITORING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Questions about Quality Monitoring Apparatus and Method for Industrial Manufacturing Processes
- 1.11 Original Abstract Submitted
MONITORING APPARATUS FOR QUALITY MONITORING
Organization Name
Inventor(s)
Sebastian Gruber of Landshut (DE)
[[:Category:Denis Krompa� of Vaterstetten (DE)|Denis Krompa� of Vaterstetten (DE)]][[Category:Denis Krompa� of Vaterstetten (DE)]]
[[:Category:Hans-Georg K�pken of Erlangen (DE)|Hans-Georg K�pken of Erlangen (DE)]][[Category:Hans-Georg K�pken of Erlangen (DE)]]
MONITORING APPARATUS FOR QUALITY MONITORING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18277533 titled 'MONITORING APPARATUS FOR QUALITY MONITORING
Simplified Explanation
The patent application describes a monitoring apparatus and method for quality monitoring of a supplemented manufacturing process in comparison to predefined industrial manufacturing processes. This involves obtaining teacher models, creating data samples where teacher models and specific student models disagree, adapting student learning models based on trained teacher-specific student models, customizing the adapted student model to the supplemented manufacturing process, and monitoring the process using the customized student model.
Key Features and Innovation
- Obtaining teacher models for predefined manufacturing processes
- Training generator learning models and teacher-specific student models based on teacher models
- Creating data samples where teacher models and specific student models disagree
- Adapting student learning models based on trained teacher-specific student models
- Customizing the adapted student model to the supplemented manufacturing process
- Monitoring the process using the customized student model
Potential Applications
The technology can be applied in various industrial manufacturing settings where quality monitoring of manufacturing processes is crucial. It can also be used in educational settings for personalized learning models.
Problems Solved
This technology addresses the need for accurate quality monitoring in manufacturing processes, as well as the need for personalized learning models based on teacher-specific data.
Benefits
- Improved quality monitoring in manufacturing processes
- Personalized learning models for more effective training
- Enhanced efficiency and accuracy in industrial manufacturing
Commercial Applications
"Quality Monitoring Apparatus and Method for Industrial Manufacturing Processes"
This technology has significant commercial potential in industries where manufacturing processes require precise monitoring and optimization. It can be utilized by manufacturing companies to improve quality control and efficiency in their operations.
Questions about Quality Monitoring Apparatus and Method for Industrial Manufacturing Processes
How does this technology improve quality monitoring in manufacturing processes?
This technology improves quality monitoring by customizing student learning models to specific manufacturing processes, allowing for more accurate predictions and analysis.
What are the potential applications of this technology beyond industrial manufacturing?
The technology can also be applied in educational settings for personalized learning models based on teacher-specific data.
Original Abstract Submitted
A monitoring apparatus and method for quality monitoring of a supplemented manufacturing process to a set of predefined manufacturing processes of industrial manufacturing includes: obtaining teacher models, providing an initial version of a student learning model and an initial version of a generator learning model, for each teacher model, training the generator learning model and a teacher specific student model to create data samples where the teacher model and teacher specific student learning models do not agree in their predictions, and adapting the current version of the student learning model based all trained teacher specific student models, customizing the adapted student model to the supplemented manufacturing process by training the adapted student model with annotated data of the supplemented manufacturing process, and monitoring the supplemented manufacturing process by processing the customized student model using data samples collected during the supplemented manufacturing process as input data.
