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18 results for “316L”
DHM Tensile Loading of 316L Stainless Steel
<p>Video <em><strong>3D.avi</strong></em> shows the in-situ observation and height measurement of the tensile deformation of 316L stainless steel by DHM</p> <p>Video <em><strong>Plot of aligned stack.avi</strong></em> shows the measured height profile of the segment indicated in image <em><strong>ROI.jpg</strong></em></p> <p>Inoue Laboratory - Materials Infomatics in Physical Metallurgy Lab, Institute of Industrial Science, The University of Tokyo</p>
316L L-PBF fatigue dataset
<p>This file contains 316L Laser Powder Bed Fusion fatigue tests dataset.</p> <p>Experiments were carried on a MTS Landmark 100 kN servohydraulic fatigue test machine.</p> <p>This experimental campaign took place in the context of a PhD grant from the French region Pays de la Loire (see https://pastel.archives-ouvertes.fr/tel-03688021 for the thesis manuscript).</p> <p>Fatigue tests were carried :</p> <p>- in air or in salt-spray</p> <p>- on different batches (polished, pre-corroded, with artificial defects,...)</p> <p>- at R=-1 and R=0.1</p>
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>
Dataset of Hyperspectral Melt Pool Signatures and Thermal Anomalies in DED of 316L steel
<p><strong>Description of the dataset</strong><br>The dataset includes in-situ melt pool signatures (hyperspectral NIR images) during the Directed Energy Deposition of 316L steel for several classes of thermal anomalies. Thermal anomalies were created during the process by varying the scanning speed.</p> <p>Samples were printed on the MiCLAD machine at the Vrije Universiteit Brussel (Belgium).</p> <p>Process and acquisition parameters:</p> <ul> <li>Hardware: <ul> <li>Machine: MiCLAD (Vrije Universiteit Brussel)</li> <li>Laser: High-YAG BIMO 1064nm, 2.55mm fibre, flat-top</li> <li>Nozzle: Harald-Dickler HighNo 4.0</li> </ul> </li> <li>Process parameters: <ul> <li>Laser power: 600 W</li> <li>Scanning speed: 500/700/900/1100/1300 mm/min</li> <li>Powder: 316L 45-105 um</li> <li>Powder flow rate: 3.5 g/m</li> <li>Layer thickness: 0.2 mm</li> </ul> </li> <li>Image characteristics: <ul> <li>Camera: 3D-One Avior AX-M25NIR</li> <li>Hyperspectral filter layout: 5x5 (25 wavelengths per image)</li> </ul> </li> </ul> <p><strong>Description of the files</strong></p> <ul> <li>CSV dataset (hyperspectral_nir_meltpool_dataset.csv): List of filename, sample, label, time (ms), X and Z position (mm) and local scanning speed (mm/min) for all melt pool signatures. Thermal anomalies are labelled accordingly: <ul> <li>0 : baseline</li> <li>1 : edge</li> <li>2 : underheat</li> <li>3 : strong underheat</li> <li>4 : overheat</li> <li>5 : strong overheat</li> </ul> </li> <li>Melt pool signatures (hyperspectral_nir_meltpool_images_*.zip): Raw .tif thermal images of the melt pool taken in-situ. The raw images must debayered to retrieve the spectral information, see the Python function and example script. </li> <li>Python debayer function (debayer.py): Debayering function to retrieve the spectral information from the raw images. </li> </ul>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of austenitic stainless steel AISI 316L
<p><span>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF</span><span>‑</span><span>LB/M) and from a conventional process route (hot rolled sheet). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</span></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>
Residual stress in 316L stainless steel benchmark additively manufactured arches determined by neutron diffraction and snychtron X-ray diffraction
<p>Residual stress data recorded as part of the EASI-STRESS project. </p> <p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon and ID15A at ESRF). The data is for residual stress in a benchmark 316L stainless steel arch manufactured by laser powder bed fusion using a MetalFAB1 additive manufacturing machine. The arch is square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for two lines is presented: line 1 runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction). Line 2 runs along to the x axis at a depth of 1 mm into the arch. </p> <p>The stress-free reference used was a reference comb cut from an identically made arch.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 ´ 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</p>
As built and post heat treatment residual stresses in 316L stainless steel additively manufactured benchmark arches
<p>The data presented is the residual stress in three orthogonal directions determined by neutron diffraction (SALSA at ILL) and synchrotron X-ray diffraction (P07 and P61A at Desy operated by Hereon). The data is for residual stress in a benchmark 316L stainless steel arches manufactured by laser powder bed fusion using MetalFAB1 and EOS M290 additive manufacturing machines. These arches are square topped with dimensions of nominally 20 mm in both the x and y direction (in plane). The overhang which creates the arch shape runs parallel to the y direction. The height of the arch is nominally 10 mm with the ligament above the over hang being of 2 mm nominal thickness. </p> <p>Data for part in both an as built and after a 700 °C 2 hour heat treatment are presented. The measurement line runs from the centre of the top surface (defined at the origin) down into the arch (defined as positive z direction).</p> <p>The stress-free reference used was a reference comb cut from an identically made arches. For heat treated samples, heat treated combs were used.</p> <p>Different gauge volume sizes and shapes were defined at each institution, all measurement locations indicate the centre of the gauge volume during each measurement. gauge volumes were: P07 200 x 200 µm2, ~1.9 mm, P61A 150 × 150 µm2 ~2.9 mm, ID15A 200 x 50 µm2 ~1.7 mm, SALSA 0.6 x 0.6 x2 mm3.</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>
Datasets describing optimization the cutting regime in the turning of AISI 316L steel based on the NSAG-II and NSAG-III multicriteria algorithms.
<p><em>This work shows the multi-criteria data analysis of the dry and MQL turning process of AISI 316L steel using the evolutionary algorithms of non-dominant class II and III (NSAG-II and NSAG-III). The wear of the cutting tool (VB), the energy consumption (E) and the machining time (t) are used as analysis variables, with the aim of minimizing the wear of the cutting tool based on the optimal selection of parameters. When comparing the results obtained from both methods, we found that NSAG-III was the best alternative for selecting parameters in the turning of specimens, with fewer tool wear and more efficient use of energy consumption.</em> <em>Interpretation of this data</em></p>
L-DED Printing of 316L Steel Cylinders
<p>Two videos showing building of 316L Steel Cylinders using L-DED process (BeAM Magic 2.0)</p>
Compilation of HCF data of selective laser melted 316L and AlSi10Mg
<p>The file (.xlsx) contains a compilation of high-cycle fatigue data of selective laser melted 316L stainless steel and AlSi10Mg aluminium alloy. The study was carried out for uniaxial, stress-controlled loading. The experimental testing program determines the analysis of the effect of input data (material, build direction, specimen size) with assumed constants, on output data (cycles to failure). The data are used to probabilistic modelling of the size effect of mini-specimens fabricated by additive manufacturing.</p> <p>The studies were financially supported from the project 2021/05/X/ST5/00076 funded by the National Science Centre, Poland.</p>
Data from: Subgrain-controlled grain growth in laser melted 316L promoting strength at high temperatures
Stainless steel 316L prepared by laser melting consisted of hierarchical austenitic microstructure with micron sized (10-25 µm) grains containing fine 1µm subgrains with a cellular structure. At high temperature thermal treatments (≥ 1100 °C), merging and growth of the 1 µm subgrains into bigger subgrains restricted the rapid grain growth and microstructure coarsening. Partial phase transformation of austenite to ferrite at temperatures ≥ 1100 °C in combination with gradual and steady growth of subgrains inside the micron sized grains and nucleation of sigma phase have promoted the tensile strength of stainless steel 316L to 300 MPa at 1100 °C compared to conventionally made 316L counterparts with tensile strength of approximately 40 MPa. The grain growth mechanism of laser melted microstructure can change the application criteria for 316L and expand the application fields for 316L.
Potentiodynamic polarisation curves and pitting descriptors of 316L stainless steel in NaCl electrolyte
<div>This deposit contains all data and code supporting the analysis in:</div> <div> </div> <div>Identifying stable pitting pathways in 316L stainless steel via fractal-inspired PCA-based clustering</div> <div>Coelho L.B., Amand T., Torres D., Olivier M., Ustarroz J. (accepted 21 April 2025, npj Materials Degradation)</div> <div> </div> <div>Included files</div> <div> </div> <div>calculated_epit_data.csv – the ML-estimated critical pitting potentials (Epit)</div> <div> </div> <div>calculated_logipit_data.csv – the ML-estimated log(jₚᵢₜ) values corresponding to Epit</div> <div> </div> <div>calculated_epass_data.csv – the ML-estimated passive potentials (Epass) and log(jₚₐₛₛ)</div> <div> </div> <div>calculated_epit_df_meta_data.csv – manually curated Epit values just before stable pit growth</div> <div> </div> <div>calculated_logipit_df_meta_data.csv – manually curated log(jₚᵢₜ) values just before stable pit growth</div> <div> </div> <div>Critical-descriptor definitions</div> <div> </div> <div>The critical pitting potentials (Epit) and passive potentials (Epass) were estimated using the machine learning (ML) model described previously [1].</div> <div>The files calculated_epit_df_meta_data.csv and calculated_logipit_df_meta_data.csv contain the last metastable-pitting descriptors—manually identified—immediately prior to the onset of stable pit growth.</div> <div> </div> <div>Experimental methods</div> <div>The macro-scale potentiodynamic polarization (PP) tests were performed at varying NaCl concentrations (0.005 M, 0.01 M and 0.05 M). Using an SP-300 (Bio-Logic) potentiostat inside a Faraday cage, the cell comprised:</div> <div> </div> <div>WE: 316L SS specimen (~1 cm² exposed area)</div> <div> </div> <div>RE: Ag/AgCl/KCl_sat inside a Luggin capillary</div> <div> </div> <div>CE: platinum foil</div> <div> </div> <div>After 60 min immersion to stabilize the open-circuit potential (OCP), polarization scans ran from –30 mV to +900 mV vs. OCP at 0.5 mV/s, matching our previous macro PP study on the same sample [2]. Each NaCl concentration was tested 28 times (84 curves total).</div> <div> </div> <div>References</div> <div> </div> <div>[1] L.B. Coelho, D. Torres, V. Vangrunderbeek, M. Bernal, G.M. Paldino, G. Bontempi, J. Ustarroz, Estimating pitting descriptors of 316 L stainless steel by machine learning and statistical analysis, Npj Mater Degrad 7 (2023) 82. https://doi.org/10.1038/s41529-023-00403-z.</div> <div> </div> <div>[2] L. B. Coelho, S. Kossman, A. Mejias, X. Noirfalise, A. Montagne, A. Van Gorp, M. Poorteman, M. G. Olivier, “Mechanical and corrosion characterization of industrially treated 316L stainless steel surfaces,” Surf. Coat. Technol. 382 (2020) 125175. https://doi.org/10.1016/j.surfcoat.2019.125175</div> <div> </div> <div> </div>
Influence of ion irradiation on the nanomechanical properties of thin alumina coatings deposited on 316L SS by PLD
<p>Set consists three folders named after the methods used to obtained the data i.e. Nanoindentation, XRD and SEM.</p> <p><strong>→ SEM </strong></p> <p>In SEM folder there are 4 original image files that make up Figure 2 in the paper. Below please find the description:</p> <p><em>Fig. 2. SEM images of: (a) etched 316L SS surface; as-deposited PLD-grown Al2O3 coating on 316L SS: (b) cross-section, (c) surface; (d) surface of PLD-grown Al2O3 coating after ion irradiation (50 dpa).</em></p> <p><strong>→ XRD</strong></p> <p>In XRD folder there are two .txt files that makes up the Fig. 3 in the paper. In each of them there are two columns, where 1<sup>st</sup> is 2θ (°) and 2<sup>nd</sup> is Intensity (counts per second). Based on the filenames, they are clearly identifiable.</p> <p><strong>→ Nanoindentation</strong></p> <p>In Nanoindentation folder there are two folders dedicated to two different materials (coating and substrate steel). In each of these folders there are subfolders indicating data for the particular material state (level of dpa). To each unique combination of the material, state and nanoindentation force there are two .txt files assigned. First is named ‘LD [material] [dpa][force].txt’ and contains Load-displacement data. In such a file there are two columns, where 1<sup>st</sup> is force (mN) and 2<sup>nd</sup> is displacement (nm). Single curves are arranged one under another (separated with two empty rows). Second file is named ‘AR [material] [dpa][force].txt’ and contains aggregate data. For reproducing the calculations given in the paper, first seven columns from the file are needed:</p> <p>1<sup>st</sup> – measurement No</p> <p>3<sup>rd</sup> - Maximum indentation depth h(max) (nm)</p> <p>4<sup>th</sup> - Indenter contact depth at F(max) h(c) (nm)</p> <p>5<sup>th</sup> – Maximum force (mN)</p> <p>6<sup>th</sup> - Indentation hardness H(IT) (GPa)</p> <p>7<sup>th</sup> – Reduced modulus E (GPa)</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>
Data from: Subgrain-controlled grain growth in laser melted 316L promoting strength at high temperatures
Open the record for dataset details and reuse information.
Data for the strength-ductility enhancement of 316L stainless steel meta-crystal lattice of architected materials by harnessing the non-equilibrium solidification in metal additive manufacturing
<p>Raw (tabular) CALPHAD data for SLM produced 316L stainless steel.</p>
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