scholarly journals Characterisation of diamond abrasive grains of grinding tools using industrial X-ray computed tomography

2020 ◽  
Vol 112 (1-2) ◽  
pp. 25-40
Author(s):  
Luca Pagani ◽  
Qunfen Qi ◽  
Jing Lu ◽  
Hui Huang ◽  
Guoqin Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Threshold Value ◽  
Volume Data ◽  
X Ray ◽  
Anisotropic Diffusion Filter ◽  
Abrasive Grains ◽  
Reconstructed Volume ◽  
X Ray Computed ◽  
Grinding Tool ◽  
Diamond Abrasive

AbstractIn this paper, a characterisation of diamond abrasive grains of grinding tools using industrial X-ray computed tomography (XCT) is carried out. One of the most challenge tasks in the characterisation is extracting the diamond abrasive grains from the XCT volume data. Methods that are able to extract the grains are then developed and introduced in this paper. The first step is to create a triangular mesh surface from the reconstructed volume file using a gradient anisotropic diffusion filter. The second step is to convert the measured greyscale volume into a signed distance field using a global threshold value and then a localised method for grain segmentation. To validate the proposed method, three different types of grinding tool specimens are measured and analysed. Each abrasive grain is segmented and the distributions of grains (with both random and designed patterns) are then calculated, plotted and analysed. The quantitative analysis clearly shows the deviations between the measured distribution and the designed pattern of the grinding tool, which indicates that the proposed method can provide an accurate and comprehensive characterisation of the grinding tools.

Mesh generation of porous metals from X-ray computed tomography volume data

2014 ◽  
Vol 28 (7) ◽  
pp. 2445-2451 ◽  
Author(s):  
Zhenyu Niu ◽  
Hiromasa Suzuki ◽  
Yutaka Ohtake ◽  
Takashi Michikawa
Keyword(s):  
Computed Tomography ◽  
Mesh Generation ◽  
Volume Data ◽  
Porous Metals ◽  
X Ray ◽  
X Ray Computed

Measurement of the Porosity of Additive-Manufactured Al-Cu Alloy Using X-Ray Computed Tomography

2016 ◽  
Vol 258 ◽  
pp. 448-451 ◽  
Author(s):  
Aneta Zatočilová ◽  
Tomáš Zikmund ◽  
Jozef Kaiser ◽  
David Paloušek ◽  
Daniel Koutný
Keyword(s):  
Computed Tomography ◽  
Additive Manufacturing ◽  
Selective Laser Melting ◽  
Dimensional Accuracy ◽  
Threshold Value ◽  
Processing Parameters ◽  
Laser Melting ◽  
X Ray ◽  
X Ray Computed ◽  
Metallic Parts

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.

X-Ray Computed Tomography of Ultralightweight Metals

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

scholarly journals Nano- scale X-ray Computed Tomography: Morphological Analysis of Fuel Cell Electrodes

2013 ◽  
Vol 19 (S2) ◽  
pp. 630-631
Author(s):  
P. Mandal ◽  
W.K. Epting ◽  
S. Litster
Keyword(s):  
Computed Tomography ◽  
Fuel Cell ◽  
Morphological Analysis ◽  
Fuel Cell Electrodes ◽  
X Ray ◽  
Nano Scale ◽  
X Ray Computed

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

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

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.

Sign in / Sign up

Export Citation Format

Share Document