Enhancing 3D Printing Infill Quality through Advanced Machine Learning
Author:
Affiliation:
1. Maclay School,Tallahassee,FL,32312
2. Florida State University,College of Engineering,Tallahassee,FL,32307
3. Florida A&M University,Dept. of Comp. & Info Sciences,Tallahassee,FL,32307
Funder
National Science Foundation
Northrop Grumman
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10606968/10606970/10607535.pdf?arnumber=10607535
Reference11 articles.
1. Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique
2. Additive manufacturing: expanding 3D printing horizon in industry 4.0
3. Improved Modeling of Kinematics-Induced Geometric Variations in Extrusion-Based Additive Manufacturing Through Between-Printer Transfer Learning
4. Review of transfer learning in modeling additive manufacturing processes
5. Co-learning of extrusion deposition quality for supporting interconnected additive manufacturing systems
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