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19 results for “Laser Powder Bed Fusion”
[Data] Qualify-As-You-Go: Sensor Fusion of Optical and Acoustic Signatures with Contrastive Deep Learning for Multi-Material Composition Monitoring in Laser Powder Bed Fusion Process
<p><br>Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes with multiple materials, addressing challenges in process control and ensuring accurate material composition. Experimental data from processing five powder compositions were collected using a custombuilt monitoring system in a commercial LPBF machine. The research categorised signals from LPBF processing various compositions, enhancing prediction accuracy by combining optical with acoustic data and training convolutional neural networks using contrastive learning. Latent spaces of trained models using two contrastive loss functions, clustered acoustic and optical<br>emissions based on similarities, aligning with five compositions. Contrastive learning and sensor fusion were found to be essential for monitoring LPBF processes involving multiple materials. This research advances the understanding of multi-material LPBF, highlighting sensor fusion strategies’ potential for improving quality control in additive manufacturing. Data set for this work is hosted here</p>
[Data] Acoustic emission signature of martensitic transformation in Laser Powder Bed Fusion of Ti6Al4V-Fe, supported by operando X-ray diffraction
<p>The dataset for this study focuses on investigating Acoustic Emission (AE) monitoring in the Laser Powder Bed Fusion (LPBF) process, using premixed Ti6Al4V-(x wt%) Fe, where x = 0, 3, and 6. By employing a structure-borne AE sensor, we analyze AE data statistically, uncovering notable discrepancies within the 50-750 kHz frequency range. Leveraging Machine Learning (ML) methodologies, we accurately predict composition for particular processing conditions. These fluctuations in AE signals primarily arise from unique microstructural alterations linked to martensitic phase transformation, corroborated by operando synchrotron X-ray diffraction and post-mortem SEM and EBSD analysis. Moreover, cracks are evident at the periphery of the printed parts, stemming from local inadequate heat input during the blending of Ti6Al4V with added Fe powder. These cracks are discerned via AE signals subsequent to the cessation of the laser beam, correlating with the presence of brittle intermetallics at their junction. This study highlights for the first time the potential of AE monitoring in reliably detecting footprints of martensitic transformations during the LPBF process. Additionally, AE is shown to prove valuable for assessing crack formations, particularly in scenarios involving premixed powders and necessitating precise selection of processing parameters, notably at part edges.</p>
Process parameters and properties of laser powder bed fusion alloys
<p>List of process paramaters and resulting properties of printed samples after laser powder bed fusion.</p> <p>Process parameters include: manufacturer of LPBF equipment, equipment model and laser type of the printer, laser power (P), scan speed (v), nominal powder layer thickness (t), hatch spacing (h), laser beam diameter (spot size (d), focus offset distance of the laser beam, scanning strategy, rotation angle of scanning strategy between layers and the build plate temperature.</p> <p>Information about the post-processing of the alloys was collected as to whether the alloy was heat-treated, heat treatment type, temperature and duration of each heat treatment step.</p> <p><br> Properties include consolidation, hardness, yield stress, elongation to failure, tensile strength</p> <p><br> Dataset was collected from peer-reviewed publicaions. Sources of the original data are provided within the datasheet.</p>
Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading"
<p>Title of dataset: Data for "Using physics-informed neural networks to predict the lifetime of laser powder bed fusion processed 316L stainless steel under multiaxial low-cycle fatigue loading".</p> <p>Name/institution/contact information: Dr. Michal Bartošák, Czech Technical University in Prague - Faculty of Mechanical Engineering, email: michal.bartosak@fs.cvut.cz.</p> <p>Date of data collection: The data were collected between 2021 and 2024.</p> <p>File name structure: The data consists of two files: "316L_fatigue_and_defects.xls," which contains fatigue lifetime data and defect characteristics, and an associated description file, "read_me.txt."</p> <p>See "https://doi.org/10.1016/j.ijfatigue.2024.108608" for the associated article and a detailed description of the methods.</p>
Data for the conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion"
<p>The dataset contains raw data for conference poster "TiAl6V4 bistable mechanism produced by Laser Powder Bed Fusion" presented at the 9th Metal Additive Manufacturing Conference.</p>
Dataset for the research paper "Computational and experimental investigation of thermally auxetic multi-metal lattice structures produced by Laser Powder Bed Fusion"
<p>The aim of this study is to investigate the potential of tailoring the structural thermal expansion properties of a multi-metal re-entrant lattice structure made of 316L stainless steel and CuCr1Zr copper alloy. Several geometric configurations with different layout of parent materials were designed and tested for their ability to thermally expand at elevated temperature. The study showed that one of the geometric configurations with the chosen material layouts allows to exceed the expansion range that can be achieved by both parent materials. The prediction of the finite element analysis was thus confirmed by experimental measurements. In addition, the influence of manufacturing imperfections in the form of geometric deviations and non-optimal material deposition was also investigated, and the results showed that this has a significant influence on the overall expansion. In conclusion, it was found that it is possible to tailor multi-metal lattice structures to a specific expansion, but the disadvantages associated with manufacturing must first be eliminated.</p>
Dataset for "Influence on micro-geometry and surface characteristics of laser powder bed fusion built 17-4 PH miniature spur gears in laser shock peening"
<p>The dataset represents the experimental data for publication "Influence on micro-geometry and surface characteristics of laser powder bed fusion built 17-4 PH miniature spur gears in laser shock peening".</p>
Influence of feature size and shape on corrosion of 316L lattice structures fabricated by laser powder bed fusion
<p><strong>An open dataset for the paper with the same title: "<em>Influence of feature size and shape on corrosion of 316L lattice structures fabricated by laser powder bed fusion</em>". </strong></p> <p><strong>The dataset contains, for example, 3D models, original and analyzed microCT data, video visualizations, tensile testing .csv files, microscopy images, and code resources. Selected works are presented as part of the paper.</strong></p> <p><strong>Abstract:</strong></p> <p><em>Laser powder bed fusion (LPBF) has become an established method for manufacturing end-use metal components. Exploiting the geometric freedom of additive manufacturing (AM) offers broad possibilities for part optimization and enables performance enhancements across industry sectors. However, part shape and feature size have been found to locally affect residual stresses, melt pool cooling rates, microstructure, and thus the mechanical properties of </em><em>components. Even though the mesoscale structure can locally induce microstructural changes, there are no prior studies on how it influences corrosion. </em><em>Using AM-produced, optimized parts in critical applications necessitates a better understanding of their long-term performance. In this study, lattice structures were used to probe the influence of feature size and shape on corrosion susceptibility and its spatial localization.</em></p> <p><em>The susceptibility of submillimeter LPBF-fabricated 316L stainless steel </em><em>lattice structures to corrosion was investigated by conducting a 21-day immersion corrosion test in an aqueous 3.5wt% NaCl solution. Schoen gyroid and Schwarz </em><em>diamond triply periodic minimal surface lattices were manufactured with three unit cell sizes and wall thicknesses (0.867, 0.515, and 0.323 mm). The nominal surface and cross-sectional areas were the same for the two geometries. X-ray microcomputed tomography (microCT) scans before and after the corrosion test were compared for volumetric losses. <em>In addition, the </em>mechanical properties and microstructure of the samples were evaluated.</em></p> <p><em>As part of the study, a workflow to register, index, and analyze volumetric changes of consecutive microCT image stacks was developed. The method is fully reported and applicable to time-lapse studies with microCT. Three out of five of the 0.323 mm wall thickness lattices displayed visually aggressive pitting. Based on the microcomputed tomography data, the mass losses were localized either in the entrapped powder particles or partially melted surface globules. Corrosion did not occur in the dense base material. The total mass losses ranged from 8 to 19 mg. Despite visual indications to support a higher corrosion susceptibility for the smallest lattice sizes, the mass loss values did not confirm this conclusion. The tensile test results did not provide any clear indications of latent corrosion effects on mechanical properties.</em></p> <p> </p> <p><em>Version 1.1: 'Microstructure.zip' was revised. Metallographic preparation and Beraha II etching was redone for selected samples. New images and grain size (and grain distribution) measurements were added.</em></p> <p><em>Version 1.2: 'CT_Data_Heatmap_example.zip' was added. </em></p>
Manufacturing of high strength and high conductivity copper with laser powder bed fusion
<p>Additive manufacturing (AM), known as 3D printing, enables rapid fabrication of geometrically complex copper (Cu) components for electrical conduction and heat management applications. However, pure Cu or Cu alloys produced by 3D printing often suffer from either low strength or low conductivity at room and elevated temperatures. Here, we demonstrate a design strategy for 3D printing of high strength, high conductivity Cu by uniformly dispersing a minor portion of lanthanum hexaboride (LaB<sub>6</sub>) nanoparticles in pure Cu through laser powder bed fusion (L-PBF). We show that trace additions of LaB<sub>6</sub> to pure Cu result in an improved L-PBF processability, an enhanced strength, and improved thermal stability, all whilst maintaining a high conductivity. The presented strategy could expand the applicability of 3D-printed Cu components to more demanding conditions where high strength, high conductivity, and thermal stability are required.</p>
Material Dependent Influence of Ring/Spot Beam Profiles in Laser Powder Bed Fusion
<p>Raw data associated with a paper submission.<br>"Material Dependent Influence of Ring/Spot Beam Profiles in Laser Powder Bed Fusion"</p> <p>Contained are all the Matlab scripts used for the simulations, as well as csv's of any data plotted in graphs.</p>
Supplementary materials for "Operando phase mapping in multi-material laser powder bed fusion"
<p>This dataset includes the experimental data for the paper: "Operando phase mapping in multi-material laser powder bed fusion" by S. Sumarli, F. Malamud, S. Van Petegem, S. Gaudez, A. Baganis, M. Busi, E. Polatidis, C. Leinenbach, R. Logé, and M. Strobl. For more information, see the "Open data structure.pdf" file.</p>
Manufacturing of high strength and high conductivity copper with laser powder bed fusion
Open the record for dataset details and reuse information.
[Data] Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
<div> <div> <div> <p>Different Laser Powder Bed Fusion (LPBF) process spaces were deliberately introduced by employing two distinct 316L stainless steel powder distributions (with particle sizes >45 μm and < 45 μm) and processing them with two sets of laser parameters, resulting in the creation of four datasets [D1, D2, D3, and D4]. These datasets encompass LoF pores, conduction mode, and keyhole formations, each associated with three LPBF regimes denoted as D1, D2, D3, and D4. The experiments utilized a Sisma MYSINT 100 commercial LPBF printer and an airborne AE sensor system with a flat frequency response ranging from 0 to 150 kHz. Validation of the ground truths for the three laser regimes across the four datasets, representing distinct process spaces, was accomplished through the confirmation of cross-sectional images. In the course of fabricating a cube using a powder bed and laser, data acquisition from an AE sensor was triggered when the optical intensity reached a threshold of 0.5 V for each scan length. The photodiode trigger gain was adjusted to saturate at 5 V, and the ensuing continuous-time window, where the optical signal remained at 5 V for 12.5 ms, was calculated and segmented to generate the dataset. Irrespective of the specific regime (Lack of Fusion, Conduction, and Keyhole) or the cube being fabricated (with two powder distributions), the signals obtained during this process were then segmented into a 12.5 ms window comprising 5000 data points. To eliminate any noise, an offline application of a low-pass Butterworth filter with a 150 kHz cut-off frequency was employed, aligned with the frequency response specification of the AE sensor. Each dataset has two files against it [raw/groundtruth label].</p> </div> </div> </div>
Data for "Nanoparticle reinforced medium entropy CoCrFeNi produced by laser powder bed fusion: Microstructure evolution"
<p>Nanoparticle-reinforced metallic composites produced via laser powder bed fusion (LPBF) offer an economically feasible approach for obtaining high-strength near-net shaped critical components in automotive and aviation industries. This study investigates the equiatomic medium entropy alloy (MEA) CoCrFeNi manufactured by LPBF, incorporating two types of reinforcing particles, titanium nitride (TiN) and titanium oxide (TiO2), with varying sizes and volume concentrations. In this paper, we focus on analyzing the microstructure and texture evolution of all alloys, alongside examining the dissolution, precipitation and phase transitioning of the particles. TiN nanoparticles dissolve in the melt pool and uniformly precipitate as TiO2, forming novel core-shell nanoparticles resistant to coarsening. </p> <p>Here we share the STEM raw data files that were used in the analysis. We also share a general image analysis routine that used python based modules to measure the particle sizes and their volume fraction.</p> <p>In brief, we used the following steps for several images of each sample:<br>(a) Threshold the equalized grayscale image to create a binary image.<br>(b) Calculate the area fraction of the cleaned binary image.<br>(c) Detect contours in the binary image.<br>(d) Extract properties of circles from the contours, such as scaled diameter, and area.<br>(e) Draw circles on the original image using the detected contours.</p> <p><br>For TiN/5/800 samples containing multiple square-shaped particles, we assess the area of these squares and subsequently determine the diameter of a circle possessing an equivalent area.</p> <p>We also share the the raw file and the jupyter notebook for the 4DSTEM experiment conducted on the core-shell nanoparticle.</p>
Optical Emission and Reflection Data for Melting Regime Classification in Laser Powder Bed Fusion of 316L Stainless Steel and Ti-6Al-4V
<p>This dataset and accompanying code repository contain the experimental data and analysis scripts used in the study of real-time melting regime classification in Laser Powder Bed Fusion (LPBF) processes. The data includes optical sensor measurements (emission and reflection) collected during LPBF printing of 316L stainless steel and Ti-6Al-4V specimens, along with corresponding process parameters.</p>
Raw data from Fast generation of a metamodel for conduction mode melt pool dimensions in Laser Powder Bed Fusion
<p>This is the raw data repository for the submission of the Manuscript <span>"Fast generation of a metamodel for conduction mode melt pool dimensions in Laser Powder Bed Fusion"</span></p>
Tailored deformation behavior of 304L stainless steel through control of the crystallographic texture with Laser-Powder Bed Fusion
<p>Laser-powder bed fusion (L-PBF) has gained significant research interest, not only for its profound advantage of producing near-net shape complex geometries of metallic parts, but also for the possibility of producing tailored microstructures. Recent observations have shown that by adjusting the process parameters it is possible to manipulate the crystallographic texture, through the control of the geometrical features of the melt pool. It is also known that the deformation behavior, namely the transformation induced plasticity or twinning induced plasticity effects, of austenitic stainless steels are dependent on the crystallographic texture. Based on the aforementioned observations, the deformation behavior of austenitic stainless steels processed by L-PBF can be tailored. By adjusting the laser power and the laser scanning speed, tailored crystallographic textures were obtained, along the uniaxial loading direction in 304L stainless steel samples produced by L-PBF. The possibility to engineer the crystallographic textures and thus the deformation behavior, in metastable stainless steels, is demonstrated by performing in situ neutron diffraction and uniaxial tension and compression tests. The influence of the initial and the evolving crystallographic texture on the deformation behavior is demonstrated and elaborated accordingly. The observed asymmetry in the deformation behavior between tension and compression is also discussed in detail.</p>
Experimental data used in the publication :" Investigation of the Effects of Various Severe Plastic Deformation Techniques on the Microstructure of Laser Powder Bed Fusion AlSi10Mg Alloy"
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Identification of Multiple Swelling Mechanisms in Laser Powder Bed Fusion
<p>Raw data associated with a paper submission.<br> "Identification of Multiple Swelling Mechanisms in Laser Powder Bed Fusion" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.<br> Matlab scripts for the models mentioned are also available.<br> Pyrometry recorded in-situ when printing 27 cubes of various processing parameters<br> Alicona surface profiles of all 27 cubes and of staircases mentioned.</p>
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