scholarly journals Repeatability and Reproducibility Assessment of a PolyJet Technology Using X-ray Computed Tomography

2020 ◽  
Vol 10 (20) ◽  
pp. 7040
Author(s):  
Ana Pilipović ◽  
Gorana Baršić ◽  
Marko Katić ◽  
Maja Rujnić Havstad

From the very start of their use until today, processes in Additive Manufacturing (AM) have found a way to grow from prototype production to individual and small-series production. Improvements in machinery, materials and other challenges in AM development have improved product quality, its mechanical properties and dimensional accuracy. Research in the field of dimensional accuracy must be focused on achieving better tolerances. From the beginning of AM, there has been a big issue in assuring dimensional repeatability and reproducibility of a part being printed over the course of several days. In order to examine that, a test plate was designed and built repeatedly with PolyJet technology over the course of several weeks. Measurements of dimensional accuracy and shape deviations of several typical features were carried out using X-ray Computed Tomography. Measurement results were analysed and presented in order to indicate the repeatability and reproducibility of PolyJet AM technology. Results show that PolyJet technology consistently produces parts within ±100 μm, at a 95% confidence interval, under reproducibility conditions of over a 1-month period. Accuracy for measurands (distance) in the x and y axis was significantly better than it was for the z axis which was from 56 to 197 µm, i.e., in the x and y axis, it was from −8 to 76 µm. Shape errors (i.e., cylindricity) were larger than positional or dimensional errors; this can be attributed to relatively large surface roughness and small feature sizes on the test plate that was used.

2016 ◽  
Vol 258 ◽  
pp. 448-451 ◽  
Author(s):  
Aneta Zatočilová ◽  
Tomáš Zikmund ◽  
Jozef Kaiser ◽  
David Paloušek ◽  
Daniel Koutný

The additive manufacturing of metallic parts by means of selective laser melting is an emerging technology, the development of which is currently of great interest. The quality of the parts produced is evaluated mainly in terms of their mechanical properties, dimensional accuracy, and the homogeneity of the material. Because it is virtually impossible to produce parts without any internal porosity using powder-based additive manufacturing processes, measuring the porosity is critically important to optimizing the processing parameters. X-ray computed tomography is currently the only way used to measure the distribution of pores non-destructively and it can also measure the density and dimensional accuracy. Many studies have presented results of porosity measurements made using CT, but no standard methodology for the making of measurements and processing of data currently exists. The choice of parameters used for measurement and processing can have a significant impact on the results. This study focuses on the effect of voxel resolution on the resulting porosity number and discusses the possibilities for determining the threshold value for detecting pores. All the results presented in this study were obtained by analyzing the sample produced by selective laser melting technology from AlCu2Mg1.5Ni alloy.


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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