View source for University of Southern California (20240272614). EXTENDED FABRICATION-AWARE CONVOLUTION LEARNING FRAMEWORK FOR PREDICTING 3D SHAPE DEFORMATION IN ADDITIVE MANUFACTURING simplified abstract

=EXTENDED FABRICATION-AWARE CONVOLUTION LEARNING FRAMEWORK FOR PREDICTING 3D SHAPE DEFORMATION IN ADDITIVE MANUFACTURING=


==Organization Name==

[[:Category:University of Southern California|University of Southern California]]


[[Category:University of Southern California]]

==Inventor(s)==

[[:Category:Qiang Huang of Los Angeles CA (US)|Qiang Huang of Los Angeles CA (US)]][[Category:Qiang Huang of Los Angeles CA (US)]]

[[:Category:Yuanxiang Wang of Los Angeles CA (US)|Yuanxiang Wang of Los Angeles CA (US)]][[Category:Yuanxiang Wang of Los Angeles CA (US)]]

[[:Category:Cesar Ruiz Torres of Los Angeles CA (US)|Cesar Ruiz Torres of Los Angeles CA (US)]][[Category:Cesar Ruiz Torres of Los Angeles CA (US)]]

==EXTENDED FABRICATION-AWARE CONVOLUTION LEARNING FRAMEWORK FOR PREDICTING 3D SHAPE DEFORMATION IN ADDITIVE MANUFACTURING - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240272614 titled 'EXTENDED FABRICATION-AWARE CONVOLUTION LEARNING FRAMEWORK FOR PREDICTING 3D SHAPE DEFORMATION IN ADDITIVE MANUFACTURING

==Simplified Explanation==
The patent application describes a method for predicting 3D shape deformation in additive manufacturing using a convolution learning framework.

==Key Features and Innovation==
* Joint learning for a wide class of 3D shapes, including spherical and polyhedral shapes.
* 3D cookie-cutter function to capture shape deformation for polyhedral shapes.
* Extended convolution learning framework for modeling and predicting the quality of 3D freeform shapes.
* Incorporation of spatial correlations among different shapes by changing kernel functions and distance measures.

==Potential Applications==
This technology can be applied in additive manufacturing processes to predict and offset shape deformation, improving the quality of manufactured products.

==Problems Solved==
This technology addresses the challenge of predicting and correcting shape deformation in 3D printing processes, ensuring accurate and high-quality output.

==Benefits==
* Improved accuracy in predicting shape deformation.
* Enhanced quality of 3D printed products.
* Increased efficiency in additive manufacturing processes.

==Commercial Applications==
* Quality control in additive manufacturing.
* Customized 3D printing services for various industries.
* Research and development in the field of 3D printing technology.

==Prior Art==
Readers can explore prior research on convolution learning frameworks in additive manufacturing and shape deformation prediction to understand the evolution of this technology.

==Frequently Updated Research==
Stay updated on the latest advancements in convolution learning frameworks and additive manufacturing technologies to enhance the application of this innovation.

==Questions about 3D Shape Deformation Prediction==
===How does the convolution learning framework improve shape deformation prediction in additive manufacturing?===
The convolution learning framework allows for joint learning of various 3D shapes, enabling accurate modeling and prediction of shape deformation.

===What are the potential commercial implications of using this technology in additive manufacturing processes?===
This technology can revolutionize quality control in additive manufacturing, leading to improved product quality and efficiency in various industries.


==Original Abstract Submitted==

an apparatus, system, and method is provided for predicting 3d shape deformation in additive manufacturing. a convolution learning framework for shape deviation modeling provides joint learning for a wide class of 3d shapes including both spherical and polyhedral shapes. a 3d cookie-cutter function can effectively capture the unique pattern of the shape deformation for polyhedral shapes. since 3d freeform shapes can be approximated as a combination of spherical and polyhedral patches, the extended convolution learning framework builds a foundation for modeling and predicting the quality of 3d freeform shapes. by changing the kernel function and considering new distance measures for points from different shapes, the spatial correlations among different shapes can be correctly incorporated. the predicted deformation may be used to offset the machine instructions to an additive manufacturing machine to ameliorate deformation.



[[Category:G05B19/4099]]
[[Category:B33Y50/02]]
[[Category:G06T17/10]]
[[Category:CPC_G05B19/4099]]