CAD-Based Scatter Compensation For Polychromatic Reconstruction Of Additive Manufactured Parts

Iuso, Domenico; Nazemi, Ehsan; Six, Nathanaël; Samber, Björn De; Beenhouwer, Jan De; Sijbers, Jan

doi:10.1109/icip42928.2021.9506536

Cited by 3 publications

(1 citation statement)

References 23 publications

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“…Ziabari, et al [12] developed AI-CT which reduces BH and noise in the XCT reconstruction of metal AM parts by training a DNN on simulated data sets and applying it to experimental data. CAD models were used in [13] to correct for scattering in XCT measurements for metal parts. In [14], through registration of the CAD models and the actual CT of the part, followed by forward projection and a similar parameter estimation scheme as in [12], authors have reduced BH and scattering artifacts enabling improved reconstruction quality.…”

Section: Introductionmentioning

confidence: 99%

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

Ziabari

Venkatakrishnan

Dubey

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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High-resolution X-ray computed tomography (XCT) is an important technique for the inspection of additively manufactured (AM) parts. While XCT is typically used off-line to inspect a subset of manufactured parts, significantly accelerating measurement speed while retaining accuracy would enable use of XCT for in-line inspection to rapidly identify defects in each part as it is manufactured. Here, we propose a deep learning (DL) based approach that uses computer aided design (CAD) models of the AM parts and physicsbased information to rapidly produce high-quality reconstructions from sparse XCT measurements without high quality ground truth data. Our approach uses a generative adversarial neural network (GAN) to produced realistic training data from the CAD-based simulations and a deep neural network that is trained using data from the first stage to produce accurate 3D reconstructions. Using experimental XCT data of metal parts, we demonstrate enhanced defect detection capabilities while dramatically reducing the scan time.

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Section: Introductionmentioning

confidence: 99%

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

Ziabari

Venkatakrishnan

Dubey

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Practical Multi-Mesh Registration for Few-View Poly-Chromatic X-Ray Inspection

Iuso,

Paramonov,

De Beenhouwer

et al. 2024

J Nondestruct Eval

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Accurate 3D mesh registration is essential in many industrial applications of X-ray imaging, as it allows quality assessment and inspection of manufactured objects. Conventional methods rely mainly on time-consuming and expensive X-ray computed tomography (X-CT) or ancillary camera systems. Instead, we propose a novel approach for efficient 3D multi-mesh registration in few-view industrial X-ray imaging scenarios. Our approach harnesses the capabilities of CAD-ASTRA, an X-ray mesh projector, compatible with the ASTRA toolbox and popular GPU libraries such as CuPy and PyTorch, for the simulation of X-ray projec tions from a known object surface mesh. As a differentiable program, CAD-ASTRA allows iterative improvement of the objects’ position in space by back-propagation of a differentiable measure of the projection error. The potential of this approach is demonstrated through tests on simultaneous multiple object registration in a poly-chromatic imaging, even in cases where the spectral characteristics of the imaging system are unknown. Results from a diverse set of real experiments highlight the efficacy of mesh registration, achieving successful registrations even when only two projections at a 10$$^\circ $$ ∘ angle relative to the scanning system center are available. The mesh projector facilitates resource-efficient registration in industrial applications with few viewpoints, thereby reducing the demand for resources and eliminating the need for X-CT reconstruction.

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Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing

Iuso

Chatterjee

Heylen

et al. 2022

Developments in X-Ray Tomography XIV

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Additive manufacturing (AM) is increasingly gaining interest as a low-waste production technique, capable of producing objects using a computer-aided design file. It is particularly interesting for rapid prototyping of parts and manufacturing objects that have complex shapes. However, as in the case of AM through selective laser melting (SLM), manufactured objects may contain defects that can seriously alter their properties. These defects may appear as pores, which can be detected by X-ray computed tomography (X-CT) in a non-destructive manner. CT images can simply be segmented by thresholding or through more advanced techniques such as discrete X-CT reconstruction or machine learning techniques. Nevertheless, these techniques are vulnerable to image reconstruction artefacts. In this work, we evaluate the performance of state-of-the-art, deeply supervised 3D deep learning networks (UNet++, UNet 3+ and UNet-MSS) in terms of segmentation performance of pores from X-ray CT images. The networks have been trained on a real CT dataset, with (noisy) labels produced from both conventional thresholding of the CT images as well as more advanced discrete polychromatic reconstructions. Furthermore, the performance of the networks was evaluated on a test dataset with severe CT artefacts. Pore segmentation from real CT images, which include noise and reconstruction artefacts, revealed that the best performing network was UNet++ with an average Sørensen-Dice score of 0.869 ± 0.006.

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CAD-Based Scatter Compensation For Polychromatic Reconstruction Of Additive Manufactured Parts

Cited by 3 publications

References 23 publications

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

Simurgh: A Framework for Cad-Driven Deep Learning Based X-Ray CT Reconstruction

Practical Multi-Mesh Registration for Few-View Poly-Chromatic X-Ray Inspection

Evaluation of deeply supervised neural networks for 3D pore segmentation in additive manufacturing

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