18377918. PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN simplified abstract (Applied Materials, Inc.)
Contents
- 1 PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN
Organization Name
Inventor(s)
Tao Zhang of San Ramon CA (US)
Upendra Ummethala of Cupertino CA (US)
PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN - A simplified explanation of the abstract
This abstract first appeared for US patent application 18377918 titled 'PERFORMING RADIO FREQUENCY MATCHING CONTROL USING A MODEL-BASED DIGITAL TWIN
Simplified Explanation
The method described in the abstract involves updating a digital replica of manufacturing equipment based on sensor data, obtaining predictive data from the digital replica, and causing corrective actions based on the predictive data.
- The method involves receiving sensor data from manufacturing equipment.
- The digital replica reflects a virtual representation of physical elements and dynamics of the equipment.
- Predictive data is obtained from the digital replica.
- Corrective actions are performed based on the predictive data.
Potential Applications
This technology could be applied in various industries such as manufacturing, automotive, aerospace, and more to improve equipment performance and maintenance.
Problems Solved
1. Improved equipment performance: By using predictive data from the digital replica, potential issues can be identified and corrected before they cause downtime. 2. Enhanced maintenance efficiency: Proactive maintenance based on predictive data can help reduce unexpected breakdowns and increase overall equipment reliability.
Benefits
1. Increased equipment uptime: By predicting and preventing potential issues, equipment downtime can be minimized. 2. Cost savings: Proactive maintenance can help reduce repair costs and increase the lifespan of equipment. 3. Improved productivity: With fewer unexpected breakdowns, production processes can run more smoothly.
Potential Commercial Applications
Predictive maintenance software solutions, equipment monitoring systems, industrial IoT platforms.
Possible Prior Art
One possible prior art could be predictive maintenance systems that use sensor data to predict equipment failures and schedule maintenance tasks in advance. Another could be digital twins that create virtual replicas of physical assets for monitoring and analysis purposes.
Unanswered Questions
How does the method handle different types of manufacturing equipment with varying complexities?
The abstract does not specify how the method adapts to different types of manufacturing equipment and their unique operational characteristics.
What is the scalability of this technology for large-scale manufacturing facilities?
The abstract does not address how this method can be scaled up to monitor and manage multiple pieces of equipment in a large manufacturing facility simultaneously.
Original Abstract Submitted
A method includes receiving, from one or more sensors, sensor data associated with manufacturing equipment and updating one or more values of a digital replica associated with the manufacturing equipment based on the sensor data. The digital replica comprises a model reflecting a virtual representation of physical elements and dynamics of how the manufacturing equipment operates. One or more outputs indicative of predictive data is obtained from the digital replica and, based on the predictive data, performance of one or more corrective actions associated with the manufacturing equipment is caused.
