Multi-jet hydrodynamic surface finishing and X-ray computed tomography (X-CT) inspection of laser powder bed fused Inconel 625 fuel injection/spray nozzles

2021 ◽  
Vol 291 ◽  
pp. 117018
Author(s):  
Arun Prasanth Nagalingam ◽  
Jian-Yuan Lee ◽  
S.H. Yeo
2021 ◽  
Vol 11 (9) ◽  
pp. 4304
Author(s):  
Christopher G. Klingaa ◽  
Filippo Zanini ◽  
Sankhya Mohanty ◽  
Simone Carmignato ◽  
Jesper H. Hattel

Channels manufactured by laser powder bed fusion have an inherent process-induced dross formation and surface texture that require proper characterization for design and process optimization. This work undertakes surface texture characterization of AlSi10Mg channels of nominal diameter sizes ranging from 1 mm to 9 mm using X-ray computed tomography. Profile parameters, including Pa, Pz, and Pq, were found to be interchangeable for qualitative characterization of surface texture variation. Psk, Pvv, and the fractal dimension could identify the presence of extreme dross and sintered particles on the measured profiles. A method for predicting the equivalent diameter of the unobstructed cross-sectional area (Deq) was presented and its reduction was found to follow a logarithmic trend, as a function of channel length. An empirical model Pa (β, D), as a function of local angular position (β) and channel diameter (D), was demonstrated on a perfect channel geometry, resulting in well-predicted roughness and internal geometry.


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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