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8 results for “Directed Energy Deposition”
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" 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.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
Inconel 718 two ways: Powder Bed Fusion vs. Directed Energy Deposition
<p>Raw data associated with a paper submission.<br> "Inconel 718 two ways: Powder Bed Fusion vs. Directed Energy Deposition" submitted to Additive Manufacturing Letters.</p> <p>Contained are all the raw data used in the analysis, as well as csv's of any data plotted in graphs.</p> <p>Raw coaxial images captured during printing of various processing parameters<br> Optical Micrographs from which melt pool size and PDAS were measured<br> EBSD scans (.crc and .oip) of all disucssed samples<br> Raw Hardness Data<br> Position/Power logs from the build</p>
NEMARCO project: Dataset for the publication "Development of a new manufacturing route for NiCrSiFeB alloys by Direct Energy Deposition Laser Beam process (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based NiCrSiFeB alloy (Ni-Cr-Si-B self-fluxing family) are excellent candidates for replacing Cobalt-based alloys in aeronautical components such as sealing rings, valve seats, sliding bearing seats, etc. In this type of components, commonly manufactured by centrifugal casting and conventional processes, high temperature wear and stiffness under complex thermo-mechanical stresses cause lack of sealing and an increase in the wear rate. Metal additive manufacturing by direct laser metal deposition with powder (p-LMD) is presented as a potential manufacturing route for the complex processing of this type of alloys. This research work deals with the development of a new manufacturing route using p-LMD that ranges from the proper selection of the chemical composition for the starting powders, the development of the LMD process parameters to tackle the challenges associated to the wide solidification range and crack susceptibility of Ni-Cr-Si-B alloys, its monitoring and control, as well as the post- processing required to achieve the manufacture of aeronautical components.</p>
NEMARCO project: Dataset for the publication "Solidification Path, Strengthening Mechanisms and Hardness of Ni-Cr-Si-Fe-B Self-Fluxing Alloys Obtained by Laser-Directed Energy Deposition (LMD)"
<p><strong>LMD dataset</strong></p> <p>This dataset gathers data from different parts of the Laser Metal Deposition metal Additive Manufacturing process (DED-LB). The dataset covers not only the process development data for samples manufacturing and monitored data of the melt pool size during the process, but also the metrics associated to the powder feedstock consumption, energy consumption and process efficiency.</p> <p><strong>Motivation</strong></p> <p>Nickel-based Ni-Cr-Si-B self-fluxing alloys are excellent candidates to replace Cobalt-based alloys in aeronautical components. In this work, metal additive manufacturing by directed energy deposition using a laser beam (DED-LB, also known as LMD) and gas-atomized powders as a material feedstock is presented as a potential manufacturing route for the complex processing of these alloys. This research deals with the advanced material characterization of these alloys obtained by LMD and the study and understanding of their solidification paths and strengthening mechanisms.</p>
[Data] Real-time monitoring and quality assurance for laser-based directed energy deposition: integrating co-axial imaging and self-supervised deep learning framework
<p>The experimental setup utilized a co-axial color Charged Couple Device (CCD) camera, integrated into the laser deposition head. This camera operates at a frame rate of 30 frames per second and captures the morphology of the process area. The captured images consist of three RGB channels with a 640 × 480 pixels resolution. To enable the camera to capture the radiation from the process zone, a beam splitter is installed on Precitec's laser applicator head. An optical notch filter within the 650–675 nm range also blocks the laser wavelengths.</p> <p>The dataset consists of four categories that covers the process map of DED process [.rar file].<br>The dataset consist of around 48,000 images that are labelled into 4 categories [P1-P2-P3-P4]. The images correspond to DED process zone captured co-axially<br>The categories are function of linear laser energy deposited. The folder is already split into Train and Test.</p>
Acoustic Monitoring Dataset for Robotic Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz.</p><p><strong>Laser Directed Energy Deposition:</strong></p><p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p><p> </p><p><strong>Folder Structure:</strong></p><ul><li><strong>/sample-1</strong>: The main folder for the experiment sample.<ul><li><strong>/audio_files</strong>: Contains 4624 <strong>.wav</strong> audio files, each representing a 40 ms chunk of the LDED process sound.</li><li><strong>/annotations_1.csv</strong>: A CSV file providing annotations for the audio files, labeling each as "Defect-free", "Defective", or "Laser-off".</li></ul></li><li>audio_features.h5: extracted acoustic features in time-domain, frequency-domain, and time-frequency representations (MFCC features). Feature extraction was conducted using Python Essentia Library.</li></ul><p> </p><p><strong>File Naming Convention:</strong></p><ul><li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li></ul><p><strong>Annotation Details:</strong></p><ul><li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li></ul><p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p><ul><li>Geometry: Single bead wall structure</li><li>Dimensions: 90 mm * 42.5 mm</li><li>Number of layers: 50</li><li>Laser beam diameter: 2 mm</li><li>Layer thickness: 0.85 mm</li><li>Stand-off distance: 12 mm</li><li>Laser profile: Gaussian</li><li>Laser wavelength: 1064 nm</li></ul><p><strong>Process Parameters:</strong></p><ul><li>Laser power: 2.3 kW</li><li>Speed: 25 mm/s</li><li>Dwell time: 0 s</li><li>Powder flow rate: 12 g/min</li></ul><p>This dataset aims to facilitate the development and testing of acoustic-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Audio Visual in-situ Monitoring Dataset for Laser Directed Energy Deposition (LDED) of Maraging Steel C300
<p>This dataset presents a set of acoustic signals and coaxial CCD images captured during a single-bead wall experiment in robotic Laser Directed Energy Deposition (LDED) using Maraging Steel C300. The acoustic data was recorded using a high-fidelity Prepolarized microphone sensor (Xiris WeldMIC), capturing the intricate sound profiles associated with the LDED process at a sampling rate of 44,100 Hz. The coaxial CCD melt pool images are captured at 30 Hz.</p> <p><strong>Laser Directed Energy Deposition:</strong></p> <p>This dataset was generated with a robotic LDED process that consists of a six-axis industrial robot (KUKA KR90) coupled with a two-axis positioner, a laser head, and a coaxial powder-feeding nozzle.</p> <p> </p> <p><strong>File Naming Convention:</strong></p> <ul> <li>Audio files within the <strong>audio_files</strong> folder are named following the pattern <strong>sample_ExperimentID_SampleID.wav</strong>. Given that there's only one experiment and one sample provided in this demo dataset, the naming will be consistent, for example, <strong>sample_1_1.wav</strong> for the first file.</li> <li>Coaxial melt pool image files within the <strong>images </strong>folder are named following the pattern <strong>sample_ExperimentID_SampleID.jpg</strong>. </li> </ul> <p><strong>Annotation Details:</strong></p> <ul> <li>The <strong>annotations_1.csv</strong> file contains detailed labels for each audio file and image file, correlating to the conditions observed during the experiment, aiding in quick identification and analysis.</li> </ul> <p><strong>Handcrafted features for ML modelling:</strong></p> <ul> <li>The <strong>audio_features.h5</strong> file contains various physics-informed acousitc feature extracted through Python, which can be used for baseline ML modelling purpose.</li> </ul> <p><strong>Experimental Parameters:</strong> The dataset reflects a controlled experiment setup with the following specifications:</p> <ul> <li>Geometry: Single bead wall structure</li> <li>Dimensions: 90 mm * 42.5 mm</li> <li>Number of layers: 50</li> <li>Laser beam diameter: 2 mm</li> <li>Layer thickness: 0.85 mm</li> <li>Stand-off distance: 12 mm</li> <li>Laser profile: Gaussian</li> <li>Laser wavelength: 1064 nm</li> </ul> <p><strong>Process Parameters:</strong></p> <ul> <li>Laser power: 2.3 kW</li> <li>Speed: 25 mm/s</li> <li>Dwell time: 0 s</li> <li>Powder flow rate: 12 g/min</li> </ul> <p>This dataset aims to facilitate the development and testing of acoustic-based, or multi-sensor fusion-based defect detection models for real-time quality monitoring in LDED processes. It can also serve as a reference point for further research on sensor fusion, machine learning, and real-time monitoring of manufacturing processes.</p>
Surface Porosity Prediction in Directed-Energy-Deposition (DED) Printed SS316L Parts
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