18426024. SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL simplified abstract (GENERAL ELECTRIC COMPANY)
Contents
- 1 SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL - 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 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Additive Manufacturing Diagnostics
- 1.13 Original Abstract Submitted
SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL
Organization Name
Inventor(s)
V S S Srivatsa Ponnada of Bengaluru (IN)
Venkata Dharma Surya Narayana Sastry Rachakonda of Bengaluru (IN)
Megha Navalgund of Bengaluru (IN)
Pär Christoffer Arumskog of Molnlycke (SE)
Mattias Fager of Molnlycke (SE)
SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18426024 titled 'SYSTEMS, AND METHODS FOR DIAGNOSING AN ADDITIVE MANUFACTURING DEVICE USING A PHYSICS ASSISTED MACHINE LEARNING MODEL
Simplified Explanation
The patent application describes a system for diagnosing issues in an additive manufacturing device by analyzing data from the device and generating physics features to determine the health of its components.
- The system obtains parameters for a digital twin of a component based on raw data and generates physics features using transfer functions.
- Classifiers are used to classify the component as either in a good condition or a bad condition.
- The health of the component is determined based on the physics features and classifiers.
Key Features and Innovation
- Utilizes raw data from the additive manufacturing device to create a digital twin of its components.
- Generates physics features to assess the health of the components.
- Employs classifiers to classify the components into different conditions.
- Provides a comprehensive system for diagnosing issues in additive manufacturing devices.
Potential Applications
The technology can be applied in various industries utilizing additive manufacturing, such as aerospace, automotive, and healthcare, to ensure the reliability and efficiency of the devices.
Problems Solved
- Enables early detection of issues in additive manufacturing devices.
- Improves maintenance processes by accurately assessing the health of components.
- Enhances the overall performance and longevity of additive manufacturing devices.
Benefits
- Reduces downtime by proactively identifying and addressing potential issues.
- Increases productivity and efficiency in additive manufacturing operations.
- Enhances the quality and consistency of manufactured products.
Commercial Applications
Title: "Advanced Diagnostics System for Additive Manufacturing Devices" This technology can be utilized by additive manufacturing companies to streamline maintenance processes, improve product quality, and reduce operational costs. It can also be integrated into quality control systems to ensure the reliability and accuracy of manufactured parts.
Prior Art
Readers interested in prior art related to this technology may explore research papers, patents, and industry publications on additive manufacturing diagnostics, digital twins, and predictive maintenance systems.
Frequently Updated Research
Researchers in the field of additive manufacturing are constantly developing new methods and technologies for diagnosing and improving the performance of additive manufacturing devices. Stay updated on the latest advancements in digital twin technology and predictive maintenance systems for additive manufacturing.
Questions about Additive Manufacturing Diagnostics
How does this system improve the maintenance process of additive manufacturing devices?
This system enhances maintenance processes by providing accurate assessments of component health, enabling proactive maintenance to prevent downtime and costly repairs.
What are the potential applications of this technology beyond additive manufacturing?
This technology can also be applied in other industries that rely on complex machinery, such as aerospace, automotive, and healthcare, to improve equipment reliability and performance.
Original Abstract Submitted
A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.
