View source for General Electric Company (20240342831). SYSTEMS AND METHODS FOR COMPRESSION, MANAGEMENT, AND ANALYSIS OF DOWNBEAM CAMERA DATA FOR AN ADDITIVE MACHINE simplified abstract

=SYSTEMS AND METHODS FOR COMPRESSION, MANAGEMENT, AND ANALYSIS OF DOWNBEAM CAMERA DATA FOR AN ADDITIVE MACHINE=


==Organization Name==

[[:Category:General Electric Company|General Electric Company]]


[[Category:General Electric Company]]

==Inventor(s)==

[[:Category:Naresh S. Iyer of Niskayuna NY (US)|Naresh S. Iyer of Niskayuna NY (US)]][[Category:Naresh S. Iyer of Niskayuna NY (US)]]

[[:Category:Subhrajit Roychowdhury of Niskayuna NY (US)|Subhrajit Roychowdhury of Niskayuna NY (US)]][[Category:Subhrajit Roychowdhury of Niskayuna NY (US)]]

[[:Category:Christopher D. Immer of Niskayuna NY (US)|Christopher D. Immer of Niskayuna NY (US)]][[Category:Christopher D. Immer of Niskayuna NY (US)]]

[[:Category:Xiaohu Ping of Niskayuna NY (US)|Xiaohu Ping of Niskayuna NY (US)]][[Category:Xiaohu Ping of Niskayuna NY (US)]]

[[:Category:Rogier S. Blom of Clifton Park NY (US)|Rogier S. Blom of Clifton Park NY (US)]][[Category:Rogier S. Blom of Clifton Park NY (US)]]

[[:Category:Jing Yu of Pasadena CA (US)|Jing Yu of Pasadena CA (US)]][[Category:Jing Yu of Pasadena CA (US)]]

==SYSTEMS AND METHODS FOR COMPRESSION, MANAGEMENT, AND ANALYSIS OF DOWNBEAM CAMERA DATA FOR AN ADDITIVE MACHINE - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240342831 titled 'SYSTEMS AND METHODS FOR COMPRESSION, MANAGEMENT, AND ANALYSIS OF DOWNBEAM CAMERA DATA FOR AN ADDITIVE MACHINE

The abstract describes an additive manufacturing apparatus that utilizes an energy source to melt material and form components. It includes a camera to capture image data of the melted material during the process and a controller to adjust the energy source based on the image data using an artificial intelligence model.

* Energy source melts material to form components in additive manufacturing process
* Camera captures image data of melted material during process
* Controller adjusts energy source based on image data using artificial intelligence model
* Correction made by predicting errors in process and compensating for them
* Correction adjusts configuration of energy source during additive manufacturing process

Potential Applications:
- Additive manufacturing processes in various industries such as aerospace, automotive, and healthcare
- Quality control in 3D printing to ensure accurate and precise components
- Research and development for improving additive manufacturing techniques

Problems Solved:
- Ensures accuracy and precision in additive manufacturing processes
- Reduces errors and defects in components
- Enhances efficiency and productivity in manufacturing operations

Benefits:
- Improved quality control and consistency in additive manufacturing
- Increased productivity and reduced waste
- Enhanced capabilities for complex component manufacturing

Commercial Applications:
Title: "Advanced Additive Manufacturing Apparatus for Precision Component Production"
This technology can be used in industries such as aerospace, automotive, and healthcare for producing high-quality components with precision and efficiency. It can also be utilized in research and development for advancing additive manufacturing techniques.

Questions about the technology:
1. How does the use of artificial intelligence improve the additive manufacturing process?
- The use of artificial intelligence allows for real-time adjustments based on image data, leading to more accurate and precise component production.
2. What are the potential cost savings associated with implementing this additive manufacturing apparatus?
- The technology can reduce waste and errors, leading to cost savings in material usage and production processes.


==Original Abstract Submitted==

an example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. the controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.



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