View source for Siemens Energy Global GmbH & Co. KG (20240239049). COMPUTER-IMPLEMENTED CORRELATION BETWEEN MONITORING DATA AND ACCORDING INSPECTION DATA IN POWDER-BED ADDITIVE MANUFACTURING simplified abstract

=COMPUTER-IMPLEMENTED CORRELATION BETWEEN MONITORING DATA AND ACCORDING INSPECTION DATA IN POWDER-BED ADDITIVE MANUFACTURING=


==Organization Name==

[[:Category:Siemens Energy Global GmbH & Co. KG|Siemens Energy Global GmbH & Co. KG]]


[[Category:Siemens Energy Global GmbH & Co. KG]]

==Inventor(s)==

[[:Category:Hamid Jahangir of Aachen (DE)|Hamid Jahangir of Aachen (DE)]][[Category:Hamid Jahangir of Aachen (DE)]]

[[:Category:Vivian Schiller of Stuttgart (DE)|Vivian Schiller of Stuttgart (DE)]][[Category:Vivian Schiller of Stuttgart (DE)]]

==COMPUTER-IMPLEMENTED CORRELATION BETWEEN MONITORING DATA AND ACCORDING INSPECTION DATA IN POWDER-BED ADDITIVE MANUFACTURING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240239049 titled 'COMPUTER-IMPLEMENTED CORRELATION BETWEEN MONITORING DATA AND ACCORDING INSPECTION DATA IN POWDER-BED ADDITIVE MANUFACTURING

'''Simplified Explanation''': 

This patent application describes a method for correlating monitoring data and post-manufacturing inspection data in powder-bed-based additive manufacturing of workpieces. The method involves extracting region-of-interest information, transforming defect information from volumetric to layerwise domain, or vice versa, and computing spatial overlap between the monitoring and inspection data.

* Key Features and Innovation:
    - Correlating monitoring and inspection data in additive manufacturing
    - Anomaly detection in layerwise domain
    - Defect information in volumetric domain
    - Region-of-interest extraction
    - Spatial overlap computation

'''Potential Applications''':
This technology can be applied in various industries utilizing additive manufacturing processes, such as aerospace, automotive, and medical device manufacturing. It can improve quality control and process optimization in the production of complex workpieces.

'''Problems Solved''':
- Lack of correlation between monitoring and inspection data
- Difficulty in identifying defects in additive manufacturing processes
- Inefficient quality control methods

'''Benefits''':
- Enhanced quality control in additive manufacturing
- Improved defect detection and analysis
- Increased efficiency in post-manufacturing inspection processes

'''Commercial Applications''':
Title: Advanced Quality Control Method for Additive Manufacturing
This technology can be commercially used by additive manufacturing companies to streamline their quality control processes, reduce production costs, and ensure the production of high-quality workpieces. It can also be integrated into existing additive manufacturing systems to enhance overall process efficiency.

'''Questions about Additive Manufacturing Technology''':
1. How does this method improve the quality control process in additive manufacturing?
    - This method enhances quality control by correlating monitoring and inspection data, allowing for more accurate defect detection and analysis.
2. What industries can benefit the most from this technology?
    - Industries such as aerospace, automotive, and medical device manufacturing can benefit greatly from this technology due to its ability to improve quality control and process optimization.


==Original Abstract Submitted==

a computer-implemented method of correlating monitoring data and post-manufacturing inspection data in powder-bed-based additive manufacturing of workpieces includes (a) providing monitoring data including anomaly detection information of a workpiece's geometry in a layerwise domain, (b) providing inspection data including defect information of the workpiece's geometry in a volumetric domain, (c) extracting a region-of-interest information from the monitoring data, and, either, (d1) transforming the defect information of the inspection data from the volumetric domain into the layerwise domain, or, (d2) transforming the anomaly detection information of the monitoring data from the layerwise domain into the volumetric domain, as well as, (e) computing a spatial overlap of the monitoring data of interest and the transformed inspection data.



[[Category:B29C64/393]]
[[Category:B29C64/153]]
[[Category:B29C64/268]]
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[[Category:CPC_B29C64/393]]