Abstract
Fiber-reinforced composite parts used in drones, automobiles, and sports equipment are now being manufactured by additive manufacturing (AM), where the material parameters such as fiber direction can be changed within a layer or from one layer to the other. Non-destructive evaluation methods are required to assess the quality of such customized printed parts. In this work, a micro-computed tomography (lCT) dataset is analyzed to determine the fiber content in a 3D printed composite material part using a digital binary image processing method. The existing literature on binary image analysis methods to measure the fiber volume fraction is limited to continuous fiber reinforced composites. Discontinuous fiber reinforced 3D printing filaments are popular in manufacturing parts with increased strength. The methods developed in this work expands the binary image process to scans that show fibers embedded length-wise in different directions in the 3D printed layers. An optimized thresholding method is trained on the filament sample and then applied to 3D printed samples. The results show fiber volume fraction measurements with standard deviations below 0.15%. The results in this work will be useful for product quality validation.
| Original language | English (US) |
|---|---|
| Journal | IEEE Embedded Systems Letters |
| DOIs | |
| State | Published - Sep 1 2022 |
Keywords
- Additive manufacturing
- Computed tomography
- Imaging
- Manuals
- Optical fiber devices
- Optical fiber testing
- Resins
- Three-dimensional displays
- composite material.
- cyber-physical system
- non-destructive evaluation
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science(all)