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Author: Hufenbach, Julia Kristin × Subject: Additive manufacturing ×

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Now showing 1 - 3 of 3
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    In situ detection of cracks during laser powder bed fusion using acoustic emission monitoring
    (Amsterdam : Elsevier, 2022) Seleznev, Mikhail; Gustmann, Tobias; Friebel, Judith Miriam; Peuker, Urs Alexander; Kühn, Uta; Hufenbach, Julia Kristin; Biermann, Horst; Weidner, Anja
    Despite rapid development of laser powder bed fusion (L-PBF) and its monitoring techniques, there is still a lack of in situ crack detection methods, among which acoustic emission (AE) is one of the most sensitive. To elaborate on this topic, in situ AE monitoring was applied to L-PBF manufacturing of a high-strength Al92Mn6Ce2 (at. %) alloy and combined with subsequent X-ray computed tomography. By using a structure borne high-frequency sensor, even a simple threshold-based monitoring was able to detect AE activity associated with cracking, which occurred not only during L-PBF itself, but also after the build job was completed, i.e. in the cooling phase. AE data analysis revealed that crack-related signals can easily be separated from the background noise (e.g. inert gas circulation pump) through their specific shape of a waveform, as well as their energy, skewness and kurtosis. Thus, AE was verified to be a promising method for L-PBF monitoring, enabling to detect formation of cracks regardless of their spatial and temporal occurrence.
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    Additively manufactured AlSi10Mg lattices – Potential and limits of modelling as-designed structures
    (Amsterdam [u.a.] : Elsevier Science, 2022) Gebhardt, Ulrike; Gustmann, Tobias; Giebeler, Lars; Hirsch, Franz; Hufenbach, Julia Kristin; Kästner, Markus
    Additive manufacturing overcomes the restrictions of classical manufacturing methods and enables the production of near-net-shaped, complex geometries. In that context, lattice structures are of high interest due to their superior weight reduction potential. AlSi10Mg is a well-known alloy for additive manufacturing and well suited for such applications due to its high strength to material density ratio. It has been selected in this study for producing bulk material and complex geometries of a strut-based lattice type (rhombic dodecahedron). A detailed characterisation of as-built and heat-treated specimens has been conducted including microstructural analyses, identification of imperfections and rigorous mechanical testing under different load conditions. An isotropic elastic–plastic material model is deduced on the basis of tension test results of bulk material test specimens. Performed experiments under compression, shear, torsion and tension load are compared to their virtual equivalents. With the help of numerical modelling, the overall structural behaviour was simulated using the detailed lattice geometry and was successfully predicted by the presented numerical models. The discussion of the limits of this approach aims to evaluate the potential of the numerical assessment in the modelling of the properties for novel lightweight structures.
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    Designing materials by laser powder bed fusion with machine learning-driven bi-objective optimization
    (Rio de Janeiro : Elsevier, 2024) Kononenko, Denys Y.; Chernyavsky, Dmitry; King, Wayne E.; Hufenbach, Julia Kristin; van den Brink, Jeroen; Kosiba, Konrad
    To exploit the full industrial potential of additive manufacturing (AM) beyond prototyping, the resource-consuming identification of the optimal processing conditions needs to be minimized. This task becomes more challenging when multiple properties of the part shall be simultaneously optimized. We utilize machine learning (ML) methods in a case study on laser powder bed fusion (LPBF) of a Zr-based glass-forming alloy. Our experiments show that processing parameters affect density and amorphicity opposingly, demonstrating the efficacy of our ML-based approach. We employ multi-objective optimization using Gaussian Process Regression to model and predict target properties and their uncertainties of parts fabricated by LPBF – a widely used metal AM technology. With density and amorphicity as target parameters, we optimize models using the Pareto front facilitated by the Non-Dominated Sorting Genetic Algorithm II. Despite deviations in the amorphicity data, we demonstrate this method to identify the high-performance region of the process parameters and its ability to be iteratively enhanced with additional experimental data. This bi-objective optimization approach provides a robust toolset for navigating LPBF processing. It can be easily extended to a larger set of target properties and transferred to further AM technologies.