Filtern
Dokumenttyp
Sprache
- Englisch (14)
Referierte Publikation
- ja (14) (entfernen)
Schlagworte
- Additive manufacturing (7)
- Heat accumulation (4)
- Laser powder bed fusion (4)
- AISI 316L (3)
- Additive Manufacturing (3)
- In situ monitoring (3)
- AGIL (2)
- Additive manufacturing (AM) (2)
- Infrared thermography (2)
- Laser Powder Bed Fusion (2)
- Representative specimens (2)
- Selective laser melting (SLM) (2)
- Thermal history (2)
- Thermography (2)
- 316L (1)
- Cellular substructure (1)
- Computed Tomography (1)
- Computed tomography (1)
- Convolutional neural networks (CNN) (1)
- Creep (1)
- Creep behavior (1)
- Defect detection (1)
- Diffraction (1)
- Elastic modulus (1)
- Electron backscatter diffraction (1)
- Fatigue damage (1)
- Finite element method (1)
- Flaw detection (1)
- Heat treatment (1)
- IN 718 (1)
- Image registration (1)
- In-situ monitoring (1)
- In-situ process monitoring (1)
- Inconel 718 (1)
- Inter layer time (1)
- Inter-layer time (1)
- Laser Powder Bed Fusion (LPBF) (1)
- Laser Powder Bed Fusion (PBF-LB/M, L-PBF) (1)
- Laser beam melting (LBM) (1)
- Laser powder bed fusion (L-PBF) (1)
- Low-cycle fatigue (1)
- Machine learning (1)
- Microstructure (1)
- Online monitoring (1)
- Optical Tomography (1)
- PBF-LB/M/316L (1)
- Process monitoring (1)
- Process simulation (1)
- Reference data (1)
- Residual Stress (1)
- SWIR camera (1)
- SWIR thermography (1)
- Selective Laser Melting (SLM) (1)
- Shear modulus (1)
- Stainless Steel (1)
- Temperature dependence (1)
- Tensile strength (1)
- Tensile testing (1)
- Ti-6Al-4V (1)
- X-ray and Neutron Diffraction (1)
- X-ray computed tomography (XCT) (1)
- Young's modulus (1)
- infrared Thermography (1)
Organisationseinheit der BAM
- 9.6 Additive Fertigung metallischer Komponenten (14) (entfernen)
This article reports temperature-dependent elastic properties (Young’s modulus, shear modulus) of three alloys measured by the dynamic resonance method. The alloys Ti-6Al-4V, Inconel IN718, and AISI 316 L were each investigated in a variant produced by an additive manufacturing processing route and by a conventional manufacturing processing route. The datasets include information on processing routes and parameters, heat treatments, grain size, specimen dimensions, and weight, as well as Young’s and shear modulus along with their measurement uncertainty. The process routes and methods are described in detail. The datasets were generated in an accredited testing lab, audited as BAM reference data, and are hosted in the open data repository Zenodo. Possible data usages include the verification of the correctness of the test setup via Young’s modulus comparison in low-cycle fatigue (LCF) or thermo-mechanical fatigue (TMF) testing campaigns, the design auf VHCF specimens and the use as input data for simulation purposes.
Creep and creep damage behavior of stainless steel 316L manufactured by laser powder bed fusion
(2022)
This study presents a thorough characterization of the creep properties of austenitic stainless steel 316L produced by laser powder bed fusion (LPBF 316L) contributing to the sparse available data to date. Experimental results (mechanical tests, microscopy, X-ray computed tomography) concerning the creep deformation and damage mechanisms are presented and discussed. The tested LPBF material exhibits a low defect population, which allows for the isolation and improved understanding of the effect of other typical aspects of an LPBF microstructure on the creep behavior. As a benchmark to assess the material properties of the LPBF 316L, a conventionally manufactured variant of 316L was also tested. To characterize the creep properties, hot tensile tests and constant force creep tests at 600 °C and 650 °C are performed. The creep stress exponents of the LPBF material are smaller than that of the conventional variant. The primary and secondary creep stages and the times to rupture of the LPBF material are shorter than the hot rolled 316L. Overall the creep damage is more extensive in the LPBF material. The creep damage of the LPBF material is overall mainly intergranular. It is presumably caused and accelerated by both the appearance of precipitates at the grain boundaries and the unfavorable orientation of the grain boundaries. Neither the melt pool boundaries nor entrapped gas pores show a significant influence on the creep damage mechanism.
Additive manufacturing (AM) of metals and in particular laser powder bed fusion (LPBF) enables a degree of freedom in design unparalleled by conventional subtractive methods. To ensure that the designed precision is matched by the produced LPBF parts, a full understanding of the interaction between the laser and the feedstock powder is needed. It has been shown that the laser also melts subjacent layers of material underneath. This effect plays a key role when designing small cavities or overhanging structures, because, in these cases, the material underneath is feed-stock powder. In this study, we quantify the extension of the melt pool during laser illumination of powder layers and the defect spatial distribution in a cylindrical specimen. During the LPBF process, several layers were intentionally not exposed to the laser beam at various locations, while the build process was monitored by thermography and optical tomography. The cylinder was finally scanned by X-ray computed tomography (XCT). To correlate the positions of the unmolten layers in the part, a staircase was manufactured around the cylinder for easier registration. The results show that healing among layers occurs if a scan strategy is applied, where the orientation of the hatches is changed for each subsequent layer. They also show that small pores and surface roughness of solidified material below a thick layer of unmolten material (>200 µm) serve as seeding points for larger voids. The orientation of the first two layers fully exposed after a thick layer of unmolten powder shapes the orientation of these voids, created by a lack of fusion.
The prediction of porosity is a crucial task for metal based additive manufacturing techniques such as laser powder bed fusion. Short wave infrared thermography as an in-situ monitoring tool enables the measurement of the surface radiosity during the laser exposure. Based on the thermogram data, the thermal history of the component can be reconstructed which is closely related to the resulting mechanical properties and to the formation of porosity in the part. In this study, we present a novel framework for the local prediction of porosity based on extracted features from thermogram data. The framework consists of a data pre-processing workflow and a supervised deep learning classifier architecture. The data pre-processing
workflow generates samples from thermogram feature data by including feature information from multiple subsequent layers.
Thereby, the prediction of the occurrence of complex process phenomena such as keyhole pores is enabled. A custom convolutional neural network model is used for classification. Themodel is trained and tested on a dataset from thermographic in-situ monitoring of the manufacturing of an AISI 316L stainless steel test component. The impact of the pre-processing parameters and the local void distribution on the classification performance is studied in detail. The presented model achieves an accuracy of 0.96 and an f1-Score of 0.86 for predicting keyhole porosity in small sub-volumes with a dimension of (700 × 700 × 50) μm3. Furthermore, we show that pre-processing parameters such as the porosity threshold for sample
labeling and the number of included subsequent layers are influential for the model performance. Moreover, the model prediction is shown to be sensitive to local porosity changes although it is trained on binary labeled data that disregards the actual sample porosity.