INTERNATIONAL BUSINESS MACHINES CORPORATION (20240375353). DEFECT MITIGATION IN ADDITIVE MANUFACTURING simplified abstract
Contents
DEFECT MITIGATION IN ADDITIVE MANUFACTURING
Organization Name
INTERNATIONAL BUSINESS MACHINES CORPORATION
Inventor(s)
Jeremy R. Fox of Georgetown TX (US)
Jill S. Dhillon of Jupiter FL (US)
Tushar Agrawal of West Fargo ND (US)
Sarbajit K. Rakshit of Kolkata (IN)
DEFECT MITIGATION IN ADDITIVE MANUFACTURING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240375353 titled 'DEFECT MITIGATION IN ADDITIVE MANUFACTURING
The abstract describes techniques for defect mitigation in additive manufacturing, involving a system with computer-readable storage media storing a sliced model file and a machine learning model predicting errors and generating corrective printing parameters for a 3D printer.
- System includes computer-readable storage media and machine learning model
- Machine learning model predicts errors in sliced model file
- Corrective printing parameters generated for 3D printer
- Fused Filament Fabrication (FFF) 3D printer prints object based on sliced model file and corrective printing parameters
Potential Applications: - Improving the quality and accuracy of 3D printed objects - Reducing defects and errors in additive manufacturing processes
Problems Solved: - Mitigating defects and errors in additive manufacturing - Enhancing the efficiency and reliability of 3D printing processes
Benefits: - Higher quality and accuracy in 3D printed objects - Increased efficiency and reliability in additive manufacturing
Commercial Applications: - Industrial 3D printing for manufacturing components with high precision and quality - Customized production of complex parts in various industries
Questions about the Technology: 1. How does the machine learning model predict errors in the sliced model file? 2. What are the potential implications of using corrective printing parameters in additive manufacturing processes?
Frequently Updated Research: - Stay updated on advancements in machine learning algorithms for error prediction in additive manufacturing.
Original Abstract Submitted
described are techniques for defect mitigation in additive manufacturing. the techniques including a system having one or more computer-readable storage media storing a sliced model file of an object to be manufactured and a machine learning model configured to predict an error in the sliced model file and generate corrective printing parameters. the system further includes a fused filament fabrication (fff) three-dimensional (3d) printer communicatively coupled to the one or more computer-readable storage media. the fff 3d printer is configured to print the object according to the sliced model file and the corrective printing parameters.
