Towards material and process agnostic features for the classification of pore types in metal additive manufacturing

Vandecasteele, Mathieu; Heylen, Rob; Iuso, Domenico; Thanki, Aditi; Philips, Wilfried; Witvrouw, Ann; Verhees, Dries; Booth, Brian G.

doi:10.1016/j.matdes.2023.111757

Cited by 4 publications

(3 citation statements)

References 68 publications

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“…The details about the induced error layers including layer number and laser power and speed can be seen in Table 1. A more detailed description of the specimen is available in Vandecasteele et al 16…”

Section: Specimen Descriptionmentioning

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: Specimen Descriptionmentioning

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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“…Shrestha and Chou compared the characteristics of KH and LOF pores by looking at pore formation in cuboid parts printed with various energy densities. KH pores were found to have a near-spherical shape, while LOF pores were found to be elongated and to vary in size more [26,31].…”

Section: Introductionmentioning

confidence: 96%

“…Three important classes are lack of fusion (LOF), keyhole (KH), and gas porosity; each has different characteristics that allow them to be identified and result predominately from different material or process-related factors [2,[22][23][24][25][26][27][28][29][30]. Example 3D renderings of KH and LOF pores from [31] are shown in Figure 1. Shrestha and Chou compared the characteristics of KH and LOF pores by looking at pore formation in cuboid parts printed with various energy densities.…”

Section: Introductionmentioning

confidence: 99%

Using Voxelisation-Based Data Analysis Techniques for Porosity Prediction in Metal Additive Manufacturing

George,

Trevisan Mota,

Maguire

et al. 2024

Applied Sciences

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Additive manufacturing workflows generate large amounts of data in each phase, which can be very useful for monitoring process performance and predicting the quality of the finished part if used correctly. In this paper, a framework is presented that utilises machine learning methods to predict porosity defects in printed parts. Data from process settings, in-process sensor readings, and post-process computed tomography scans are first aligned and discretised using a voxelisation approach to create a training dataset. A multi-step classification system is then proposed to classify the presence and type of porosity in a voxel, which can then be utilised to find the distribution of porosity within the build volume. Titanium parts were printed using a laser powder bed fusion system. Two discretisation techniques based on voxelisation were utilised: a defect-centric and a uniform discretisation method. Different machine learning models, feature sets, and other parameters were also tested. Promising results were achieved in identifying porous voxels; however, the accuracy of the classification requires improvement before being applied industrially. The potential of the voxelisation-based framework for this application and its ability to incorporate data from different stages of the additive manufacturing workflow as well as different machine learning models was clearly demonstrated.

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Sustainability of additive manufacturing: a comprehensive review

Singh,

Mehta,

Vasudev

2024

Prog Addit Manuf

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Towards material and process agnostic features for the classification of pore types in metal additive manufacturing

Cited by 4 publications

References 68 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

Using Voxelisation-Based Data Analysis Techniques for Porosity Prediction in Metal Additive Manufacturing

Sustainability of additive manufacturing: a comprehensive review

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