View source for Nano Dimension Technologies Ltd (20240257333). INSPECTING A PRODUCT MADE BY ADDITIVE MANUFACTURING simplified abstract

=INSPECTING A PRODUCT MADE BY ADDITIVE MANUFACTURING=


==Organization Name==

[[:Category:Nano Dimension Technologies Ltd|Nano Dimension Technologies Ltd]]


[[Category:Nano Dimension Technologies Ltd]]

==Inventor(s)==

[[:Category:Eri Rubin of Kibbutz Ma'ale Ha'hamisha (IL)|Eri Rubin of Kibbutz Ma'ale Ha'hamisha (IL)]][[Category:Eri Rubin of Kibbutz Ma'ale Ha'hamisha (IL)]]

[[:Category:Yotam Raz of Tel Avis (IL)|Yotam Raz of Tel Avis (IL)]][[Category:Yotam Raz of Tel Avis (IL)]]

[[:Category:Itay Mosafi of Tel Aviv (IL)|Itay Mosafi of Tel Aviv (IL)]][[Category:Itay Mosafi of Tel Aviv (IL)]]

[[:Category:Marina Izmailov of Rehovot (IL)|Marina Izmailov of Rehovot (IL)]][[Category:Marina Izmailov of Rehovot (IL)]]

[[:Category:Katia Huri of Givatayim (IL)|Katia Huri of Givatayim (IL)]][[Category:Katia Huri of Givatayim (IL)]]

[[:Category:Eli David of Tel Aviv (IL)|Eli David of Tel Aviv (IL)]][[Category:Eli David of Tel Aviv (IL)]]

==INSPECTING A PRODUCT MADE BY ADDITIVE MANUFACTURING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240257333 titled 'INSPECTING A PRODUCT MADE BY ADDITIVE MANUFACTURING

The patent application discusses methods for inspecting products made through additive manufacturing (AM) of multiple layers. It involves using an augmented file derived from a design file, which includes layer data for each design layer and weighted layer data for design layers beneath it. A machine learning algorithm is then applied to optical inspection images of the product during the AM process to detect production errors.

* Augmented file derived from design file for additive manufacturing (AM) products
* Includes layer data for each design layer and weighted layer data for layers beneath it
* Machine learning algorithm used to detect production errors in optical inspection images during the AM process

Potential Applications:
- Quality control in additive manufacturing processes
- Improving the efficiency and accuracy of product inspections in AM systems

Problems Solved:
- Detecting production errors in real-time during the additive manufacturing process
- Enhancing the quality and reliability of products made through AM

Benefits:
- Minimizing defects and ensuring product quality in additive manufacturing
- Streamlining the inspection process and reducing the need for manual intervention

Commercial Applications:
Title: "Advanced Quality Control System for Additive Manufacturing Processes"
This technology can be utilized in industries such as aerospace, automotive, and healthcare for ensuring the quality and reliability of products manufactured through additive manufacturing.

Prior Art:
Readers can explore prior research on machine learning algorithms for quality control in additive manufacturing processes to gain a deeper understanding of the existing technology landscape.

Frequently Updated Research:
Stay updated on the latest advancements in machine learning algorithms for quality control in additive manufacturing processes to leverage cutting-edge technologies for product inspection and quality assurance.

Questions about Additive Manufacturing Quality Control:
1. How does the use of machine learning algorithms improve the inspection process in additive manufacturing?
Machine learning algorithms enhance the detection of production errors in real-time, leading to improved product quality and reliability.
2. What are the potential challenges in implementing augmented files for product inspection in additive manufacturing?
Implementing augmented files may require specialized software and training to ensure accurate data representation and analysis.


==Original Abstract Submitted==

methods of inspecting a product made by additive manufacturing (am) of multiple layers, computer program products and inspection modules for am systems are provided. an augmented file is derived from a design file including layer data used to produce the product by am. for each design layer, the augmented file includes the layer data for the design layer and weighted layer data for design layers beneath the design layer. a machine learning (ml) algorithm (trained on previous images and augmented files) is applied with respect to the derived augmented file onto received optical inspection images of the product during the am process to detect production errors.



[[Category:G06T7/00]]
[[Category:CPC_G06T7/0006]]