3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

Heylen, Rob; Thanki, Aditi; Verhees, Dries; Iuso, Domenico; Beenhouwer, Jan De; Sijbers, Jan; Witvrouw, Ann; Haitjema, Han; Bey-Temsamani, Abdellatif

doi:10.1088/1361-6501/ac459a

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…After printing, detachment from the printer bed, and grinding and polishing to remove the supports, the 3D printed cylindrical object was CT scanned resulting in a CT reconstruction with isotropic 10 µm voxel resolution. The CT scan was denoised with a total variation denoising method 24 and thresholded to create a pore segmentation. Registration of the CT reconstruction with the experiment's coordinate system was performed by minimizing the deviation of several features from their theoretical values with respect to translation and rotation degrees of freedom.…”

Section: Resultsmentioning

confidence: 99%

Combining high-throughput imaging in visible and SWIR wavelengths for in-situ porosity prediction in laser powder bed fusion

Ahar,

Vandecasteele,

Booth

et al. 2024

Laser 3D Manufacturing XI

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Laser powder bed fusion is at the forefront of manufacturing metallic objects, particularly those with complex geometries or those produced in limited quantities. However, this 3D printing method is susceptible to several printing defects due to the complexities of using a high-power laser with ultra-fast actuation. Accurate online print defect detection is therefore in high demand, and this defect detection must maintain a low computational profile to enable low-latency process intervention. In this work, we propose a low-latency LPBF defect detection algorithm based on fusion of images from high-speed cameras in the visible and short-wave infrared (SWIR) spectrum ranges. First, we design an experiment to print an object while both imposing porosity defects on the print, and recording the laser's melt pool with the high-speed cameras. We then train variational autoencoders on images from both cameras to extract and fuse two sets of corresponding features. The melt pool recordings are then annotated with pore densities extracted from the printed object's CT scan. These annotations are then used to train and evaluate the ability of a fast neural network model to predict the occurrence of porosity from the fused features. We compare the prediction performance of our sensor fused model with models trained on image features from each camera separately. We observe that the SWIR imaging is sensitive to keyhole porosity while the visible-range optical camera is sensitive to lack-of-fusion porosity. By fusing features from both cameras, we are able to accurately predict both pore types, thus outperforming both single camera systems.

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Section: Resultsmentioning

confidence: 99%

Combining high-throughput imaging in visible and SWIR wavelengths for in-situ porosity prediction in laser powder bed fusion

Ahar,

Vandecasteele,

Booth

et al. 2024

Laser 3D Manufacturing XI

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“…To extract pore densities from the CT image, the following procedure was followed ( Figure 4 ). First, the CT image was denoised with a 3D total variation method to minimise the effect of imaging artefacts (e.g., scatter) and to improve the subsequent pore segmentation accuracy [ 41 ]. A binary segmentation mask was then obtained by comparing the CT image with a global threshold obtained using Otsu’s method.…”

Section: Materials and Methodsmentioning

confidence: 99%

Encoding Stability into Laser Powder Bed Fusion Monitoring Using Temporal Features and Pore Density Modelling

Booth

Heylen

Nourazar

et al. 2022

Sensors

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In laser powder bed fusion (LPBF), melt pool instability can lead to the development of pores in printed parts, reducing the part’s structural strength. While camera-based monitoring systems have been introduced to improve melt pool stability, these systems only measure melt pool stability in limited, indirect ways. We propose that melt pool stability can be improved by explicitly encoding stability into LPBF monitoring systems through the use of temporal features and pore density modelling. We introduce the temporal features, in the form of temporal variances of common LPBF monitoring features (e.g., melt pool area, intensity), to explicitly quantify printing stability. Furthermore, we introduce a neural network model trained to link these video features directly to pore densities estimated from the CT scans of previously printed parts. This model aims to reduce the number of online printer interventions to only those that are required to avoid porosity. These contributions are then implemented in a full LPBF monitoring system and tested on prints using 316L stainless steel. Results showed that our explicit stability quantification improved the correlation between our predicted pore densities and true pore densities by up to 42%.

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“…Konstantinos [15] studied a non-local form of a TV-based faithful image denoising model, where the regular term is replaced by fractional-order TV, and discussed the regularity and uniqueness of the solution when the fidelity parameter is high or low. Rob Heylen et al [16] explored the performance of a TV denoising-based 3D image denoising method in improving the pore segmentation of selective laser melting additive manufacturing parts. Donghao Lv et al [17] proposed a new upper bound function to improve the accuracy of traditional TV denoising methods.…”

Section: Introductionmentioning

confidence: 99%

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

Yin,

Li,

Wang

et al. 2023

JMSE

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Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

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3D total variation denoising in X-CT imaging applied to pore extraction in additively manufactured parts

Cited by 5 publications

References 23 publications

Combining high-throughput imaging in visible and SWIR wavelengths for in-situ porosity prediction in laser powder bed fusion

Combining high-throughput imaging in visible and SWIR wavelengths for in-situ porosity prediction in laser powder bed fusion

Encoding Stability into Laser Powder Bed Fusion Monitoring Using Temporal Features and Pore Density Modelling

A Robust Denoised Algorithm Based on Hessian–Sparse Deconvolution for Passive Underwater Acoustic Detection

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