Flaw Identification in Additively Manufactured Parts Using X-ray Computed Tomography and Destructive Serial Sectioning

Author(s):  
Veeraraghavan Sundar ◽  
Zackary Snow ◽  
Jayme Keist ◽  
Griffin Jones ◽  
Rachel Reed ◽  
...  
2017 ◽  
Vol 52 (4) ◽  
pp. 487-501 ◽  
Author(s):  
Nisrin Abdelal ◽  
Steven L Donaldson

Voids are a concern in composite materials, as they may have a negative effect on the mechanical properties of the laminates. Voids may develop especially in low cost or off-optimum process conditions. In this study, samples of glass reinforced epoxy laminates with void volume fractions in the 0.5–7% range were successfully obtained by varying the vacuum in the hand layup vacuum bagging manufacturing process. Void content was experimentally characterized using four different methods: ultrasonic scanning, epoxy burn off, serial sectioning, and X-ray computed tomography. The goal of this paper was to determine how the methods compared with respect to each other at quantifying void content. The specimens were taken from nearby locations in the same panels, so a true comparison of the methods could be obtained. The results showed, for the specific material and manufacturing conditions used, that the four different techniques can quantify voids content but with a large variation in the accuracy. X-ray computed tomography was the most successful technique to characterize voids, followed by serial sectioning. Ultrasonic scanning and epoxy burn off were not recommended techniques to characterize voids for laminates manufactured with these materials and process conditions. However, epoxy burn off was a successful technique to calculate fiber and resin weight fraction.


1999 ◽  
Vol 11 (1) ◽  
pp. 199-211
Author(s):  
J. M. Winter ◽  
R. E. Green ◽  
A. M. Waters ◽  
W. H. Green

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster

Extended abstract of a paper presented at Microscopy and Microanalysis 2013 in Indianapolis, Indiana, USA, August 4 – August 8, 2013.


Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.


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