| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Automated Fiber Length Measurement for 3D Printed Polymer Composites, Including Identification and Measurement of Non-trivially Placed Fibers |
| Author(s) |
Chris O'Brien, Chad Duty |
| On-Site Speaker (Planned) |
Chris O'Brien |
| Abstract Scope |
The mechanical stiffness and strength of a composite material is significantly influenced by the fiber length. Processing parameters for large-scale printing systems (e.g., extrusion screw speed) can directly impact the resulting fiber length distribution. There are limited standardized methods for reproducible and generalizable quantification of fiber length. Current measurement techniques are often expensive, laborious, and error prone due to dependence upon human involvement. Deep learning offers the potential to automate the laborious tasks such as segmentation, identification, and measurement of imaged fibers. The aim of the presented work is a first step in establishing a deep learning-based fiber measurement pipeline that may be generalized across various fiber imaging set-ups. This presentation focuses on the description of the measurement of both stand-alone fibers as well fibers those that are imaged in non-trivial placements (e.g., overlapping). The end goal is an open-source method for reliable, automated quantification of residual fiber length. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |