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Brueckner, F., Sousa, J., Riede, M., Hemschik, R., Seifert, M., Kühn, S., . . . Leyens, C. (2025). AI-assisted process monitoring and control approaches for AM – state-of-the-art and challenges in industrial application. In: Bo Gu; Hongqiang Chen; Henry Helvajian (Ed.), Laser 3D Manufacturing XII: . Paper presented at Laser 3D Manufacturing XII, San Francisco, United States, January 28–30, 2025. SPIE - The International Society for Optics and Photonics, Article ID 1335406.
Open this publication in new window or tab >>AI-assisted process monitoring and control approaches for AM – state-of-the-art and challenges in industrial application
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2025 (English)In: Laser 3D Manufacturing XII / [ed] Bo Gu; Hongqiang Chen; Henry Helvajian, SPIE - The International Society for Optics and Photonics, 2025, article id 1335406Conference paper, Published paper (Refereed)
Abstract [en]

Laser-based Directed Energy Deposition (DED) is an industrially established AM process that is used for a variety of different contours, surfaces, repair and redesign purposes as well as the construction of complete components. The challenge here is that components become more and more complex, larger and therefore more difficult to build. This means that sometimes lengthy processes have to be carried out in a very robust, reproducible and cost-effective manner. In contrast to conventional production technology, numerous dynamic process influences and weld pool phenomena have a decisive influence on the resulting welding result in DED. Precise attention must therefore be paid to exact temperature-time curves, suitable path planning and solidification conditions in order to achieve a tightly toleranced contour accuracy of the resulting component and to obtain defect-free results. During this lecture, different monitoring and control approaches will be presented in order to come closer to the aforementioned goal. In addition to a variety of sensors and customized process tools, this can also be supported by the use of AI-based methods.

Place, publisher, year, edition, pages
SPIE - The International Society for Optics and Photonics, 2025
Series
Proceedings of SPIE, ISSN 0277-786X ; 13354
Keywords
Directed Energy Deposition DED, process control, process monitoring, artificial intelligence AI, additive manufacturing, laser cladding, in-situ sensors, large parts
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-112527 (URN)10.1117/12.3046201 (DOI)2-s2.0-105002587897 (Scopus ID)
Conference
Laser 3D Manufacturing XII, San Francisco, United States, January 28–30, 2025
Note

ISBN for host publication: 9781510684560 (print), 9781510684577 (electronic);

Available from: 2025-04-25 Created: 2025-04-25 Last updated: 2025-05-15Bibliographically approved
Sousa, J., Brandau, B., Darabi, R., Sousa, A., Brueckner, F., Reis, A. & Reis, L. P. (2025). Artificial Intelligence for Control in Laser-Based Additive Manufacturing: A Systematic Review. IEEE Access, 13, 30845-30860
Open this publication in new window or tab >>Artificial Intelligence for Control in Laser-Based Additive Manufacturing: A Systematic Review
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2025 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 13, p. 30845-30860Article, review/survey (Refereed) Published
Abstract [en]

Laser-based additive manufacturing (LAM) offers the ability to produce near-net-shape metal parts with unparalleled energy efficiency and flexibility in both geometry and material selection. Despite advantages, these processes are inherently, as they are characterized by multiphysics interactions, multiscale phenomena, and highly dynamic behaviors, making their modeling and optimization particularly challenging. Artificial intelligence (AI) has emerged as a promising tool for enhancing the monitoring and control of additive manufacturing. This paper presents a systematic review of AI applications for real-time control of laser-based manufacturing processes, analyzing 16 relevant articles sourced from Scopus, IEEE Xplore, and Web of Science databases. The primary objective of this work is to contribute to the advancement of autonomous manufacturing systems capable of self-monitoring and self-correction, ensuring optimal part quality, enhanced efficiency, and reduced human intervention. Our findings indicate that 62.5 % of the 16 analyzed studies have deployed AI-driven controllers in real-world scenarios, with over 56 % using AI for control strategies, such as Reinforcement Learning. Furthermore, 62.5 % of the studies employed AI for process modeling or monitoring, which was integral to the development or data pipelines of the controllers. By defining a groundwork for future developments, this review not only highlights current advancements but also hints future innovations that will likely include AI-based controllers.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2025
Keywords
Additive manufacturing, artificial intelligence, close-loop control, machine learning, reinforcement learning
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-111720 (URN)10.1109/ACCESS.2025.3537859 (DOI)001425531400034 ()2-s2.0-85217544216 (Scopus ID)
Note

Validerad;2025;Nivå 2;2025-02-24 (u2);

Full text: CC BY license;

Funder: Project Hi-rEV—Recuperação do Setor de Componentes Automóveis co-financed by the Plano de Recuperação e Resiliência (PRR), Portuguese, through NextGeneration European Union (EU) under Grant C644864375-00000002;

Available from: 2025-02-24 Created: 2025-02-24 Last updated: 2025-06-24Bibliographically approved
Sheydaeian, E., Gerdt, L., Stepien, L., Lopez, E., Brückner, F. & Leyens, C. (2025). Effect of pre-heat temperature on enhancing the processability of pure zinc by laser-based powder bed fusion. Progress in Additive Manufacturing, 10, 2443-2453
Open this publication in new window or tab >>Effect of pre-heat temperature on enhancing the processability of pure zinc by laser-based powder bed fusion
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2025 (English)In: Progress in Additive Manufacturing, ISSN 2363-9512, Vol. 10, p. 2443-2453Article in journal (Refereed) Published
Abstract [en]

Zinc-based biomaterials are promising for bioresorbable applications; however, their low melting points pose challenges in laser-based additive manufacturing (AM). This study addresses this challenge by focusing on pre-heat temperature in laser-based powder bed fusion (PBF-LB) AM, a critical factor that significantly impacts final part properties. Unlike previous studies, this work systematically explores the pre-heat temperature’s role in shaping the process map, alongside laser power and scanning speed, for high-density zinc fabrication. The primary goal is to analytically generate parameter sets to avoid the vaporization temperature of zinc during the PBF-LB process and enhance the process’s stability. The proposed approach demonstrates a significant influence of the variation in pre-heat temperature on other input parameters range, such as power and scanning speed, thus enhancing the material’s processability both theoretically and next experimentally. For model validation, 20 specimens divided between three builds each with unique pre-heat temperatures were printed, revealing a direct correlation between increased pre-heat temperature and part density. Remarkably, high density was achieved even with low laser power and high scanning speed, reaching up to 99.96%. This emphasizes the role of pre-heat temperature in enhancing production speed without compromising part integrity. Mechanical properties, assessed by Vickers microhardness (31.4 ± 3.5–39.7 ± 3.3 HV). Control over pre-heat temperature shows promise in influencing part microstructure and grain morphology, critical for future studies.

Place, publisher, year, edition, pages
Springer Nature, 2025
Keywords
Zinc, Laser-based powder bed fusion, Pre-heat temperature, Theoretical model, Experimentation, Density
National Category
Manufacturing, Surface and Joining Technology Atom and Molecular Physics and Optics
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-109653 (URN)10.1007/s40964-024-00760-4 (DOI)001297792500001 ()2-s2.0-85202068464 (Scopus ID)
Note

Validerad;2025;Nivå 1;2025-03-28 (u4);

Funder: Alexander von Humboldt (AvH) Stiftung; Fraunhofer-Gesellschaft

Available from: 2024-09-04 Created: 2024-09-04 Last updated: 2025-05-15Bibliographically approved
Brandau, B., Hemschik, R., Sousa, J. P., Brueckner, F. & Kaplan, A. F. .. (2025). Enhancing laser cladding stability: Defects and schlieren-based analytics during L-DED. Additive Manufacturing, 103, Article ID 104758.
Open this publication in new window or tab >>Enhancing laser cladding stability: Defects and schlieren-based analytics during L-DED
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2025 (English)In: Additive Manufacturing, ISSN 2214-8604, E-ISSN 2214-7810, Vol. 103, article id 104758Article in journal (Refereed) Published
Abstract [en]

A schlieren system, adapted for Laser Directed Energy Deposition, was used to monitor and analyze the process zone under various conditions, including deliberate contamination and parameter limits. This approach enabled the identification and correlation of process-induced defects with schlieren phenomena. Events and zones were characterized and qualitative categorized to validate schlieren monitoring as a diagnostic tool. Notably, a highly active and spatially confined schlieren formation was consistently observed above the melt pool. Using a tailored schlieren optical setup and simulations, schlieren patterns were linked to refractive index changes in process gases, enabling quantitative analysis. The refractive index within the hot gas dome over the molten pool was observed to range from 1.00000712 to 1.00875126, with fluctuation speeds reaching up to 210 m/s. As a result, a model was developed to describe the impact of refractive index dynamics on the performance of coaxial monitoring systems in laser processes. A case study using an exemplary imaging monitoring system demonstrated that schlieren phenomena can cause wavelength-dependent lateral geometric shifts of up to 228 µm, significantly affecting the accuracy of object-based monitoring outcomes. The findings offer critical insights into the complex interplay between refractive index variations and monitoring results, paving the way for refined monitoring strategies that enhance reliability and precision in laser cladding applications.

Place, publisher, year, edition, pages
Elsevier B.V., 2025
Keywords
Laser cladding, Laser material processing, Monitoring systems, Defect detection, Schlieren imaging, Process modeling
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-112360 (URN)10.1016/j.addma.2025.104758 (DOI)001458489300001 ()2-s2.0-105001386527 (Scopus ID)
Note

Validerad;2025;Nivå 2;2025-04-14 (u5);

Full text license: CC BY 4.0;

Available from: 2025-04-14 Created: 2025-04-14 Last updated: 2025-06-24Bibliographically approved
Ferreira, J., Darabi, R., Sousa, A., Brueckner, F., Reis, L. P., Reis, A., . . . Sousa, J. (2025). Gen-JEMA: enhanced explainability using generative joint embedding multimodal alignment for monitoring directed energy deposition. Journal of Intelligent Manufacturing
Open this publication in new window or tab >>Gen-JEMA: enhanced explainability using generative joint embedding multimodal alignment for monitoring directed energy deposition
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2025 (English)In: Journal of Intelligent Manufacturing, ISSN 0956-5515, E-ISSN 1572-8145Article in journal (Refereed) Epub ahead of print
Abstract [en]

This work introduces Gen-JEMA, a generative approach based on joint embedding with multimodal alignment (JEMA), to enhance feature extraction in the embedding space and improve the explainability of its predictions. Gen-JEMA addresses these challenges by leveraging multimodal data, including multi-view images and metadata such as process parameters, to learn transferable semantic representations. Gen-JEMA enables more explainable and enriched predictions by learning a decoder from the embedding. This novel co-learning framework, tailored for directed energy deposition (DED), integrates multiple data sources to learn a unified data representation and predict melt pool images from the primary sensor. The proposed approach enables real-time process monitoring using only the primary modality, simplifying hardware requirements and reducing computational overhead. The effectiveness of Gen-JEMA for DED process monitoring was evaluated, focusing on its generalization to downstream tasks such as melt pool geometry prediction and the generation of external melt pool representations using off-axis sensor data. To generate these external representations, autoencoder (AE) and variational autoencoder (VAE) architectures were optimized using Bayesian optimization. The AE outperformed other approaches achieving a 38% improvement in melt pool geometry prediction compared to the baseline and 88% in data generation compared with the VAE. The proposed framework establishes the foundation for integrating multisensor data with metadata through a generative approach, enabling various downstream tasks within the DED domain and achieving a small embedding, allowing efficient process control based on model predictions and embeddings. 

Place, publisher, year, edition, pages
Springer Nature, 2025
Keywords
Artificial intelligence, Transference, Embedding representation, Contrastive learning, Metal additive manufacturing
National Category
Artificial Intelligence Robotics and automation
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-112664 (URN)10.1007/s10845-025-02614-4 (DOI)001476169900001 ()2-s2.0-105003559561 (Scopus ID)
Note

Full text license: CC BY

Available from: 2025-05-15 Created: 2025-05-15 Last updated: 2025-05-16
Sousa, J., Sousa, A., Brueckner, F., Reis, L. P. & Reis, A. (2025). Human-in-the-loop Multi-objective Bayesian Optimization for Directed Energy Deposition with in-situ monitoring. Robotics and Computer-Integrated Manufacturing, 92, Article ID 102892.
Open this publication in new window or tab >>Human-in-the-loop Multi-objective Bayesian Optimization for Directed Energy Deposition with in-situ monitoring
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2025 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 92, article id 102892Article in journal (Refereed) Published
Abstract [en]

Directed Energy Deposition (DED) is a free-form metal additive manufacturing process characterized as toolless, flexible, and energy-efficient compared to traditional processes. However, it is a complex system with a highly dynamic nature that presents challenges for modeling and optimization due to its multiphysics and multiscale characteristics. Additionally, multiple factors such as different machine setups and materials require extensive testing through single-track depositions, which can be time and resource-intensive. Single-track experiments are the foundation for establishing optimal initial parameters and comprehensively characterizing bead geometry, ensuring the accuracy and efficiency of computer-aided design and process quality validation. We digitized a DED setup using the Robot Operating System (ROS 2) and employed a thermal camera for real-time monitoring and evaluation to streamline the experimentation process. With the laser power and velocity as inputs, we optimized the dimensions and stability of the melt pool and evaluated different objective functions and approaches using a Response Surface Model (RSM). The three-objective approach achieved better rewards in all iterations and, when implemented in a real setup, allowed to reduce the number of experiments and shorten setup time. Our approach can minimize waste, increase the quality and reliability of DED, and enhance and simplify human-process interaction by leveraging the collaboration between human knowledge and model predictions.

Place, publisher, year, edition, pages
Elsevier, 2025
Keywords
Additive manufacturing, Digital twin, Robot operating system, Surrogate modeling, Process optimization
National Category
Robotics and automation Control Engineering
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-110704 (URN)10.1016/j.rcim.2024.102892 (DOI)001354834400001 ()2-s2.0-85208184166 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-12-04 (sarsun);

Full text license: CC BY;

Funder: Fundação para a Ciência e a Tecnologia (FCT), Portugal (2022.09967.BD); Plano de Recuperação e Resiliência (PRR), República Portuguesa, Portugal (C644864375-00000002); NextGeneration EU (C644864375-00000002); Portuguese funding agency, FCT;

Available from: 2024-11-12 Created: 2024-11-12 Last updated: 2025-05-15Bibliographically approved
Brandau, B., Brueckner, F. & Kaplan, A. F. H. (2024). Absorbance determination of a powder bed by high resolution coaxial multispectral imaging in laser powder bed fusion. Optics and Laser Technology, 168, Article ID 109780.
Open this publication in new window or tab >>Absorbance determination of a powder bed by high resolution coaxial multispectral imaging in laser powder bed fusion
2024 (English)In: Optics and Laser Technology, ISSN 0030-3992, E-ISSN 1879-2545, Vol. 168, article id 109780Article in journal (Refereed) Published
Abstract [en]

This study presents an approach for in-situ monitoring of laser powder bed fusion. Using standard laser optics, coaxial high-resolution multispectral images of powder beds are acquired in a pre-objective scanning configuration. A continuous overview image of the entire 114 × 114 mm powder bed can be generated, detecting objects down to 20 µm in diameter with a maximum offset of 22–49 µm. Multispectral information is obtained by capturing images at 6 different wavelengths from 405 nm to 850 nm. This allows in-line determination of the absorbance of the powder bed with a maximum deviation of 2.5% compared to the absorbance spectra of established methods. For the qualification of this method, ray tracing simulations on powder surfaces and tests with 20 different powders have been carried out. These included different particle sizes, aged and oxidized powders.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Spectroscopic Measurement, Correction Method, Laser Powder Bed Fusion, Scanning Method, Metal Powder, Multispectral Imaging, Laser Material Processing
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-99249 (URN)10.1016/j.optlastec.2023.109780 (DOI)001051885900001 ()2-s2.0-85166468238 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-08-07 (hanlid)

Available from: 2023-08-02 Created: 2023-08-02 Last updated: 2025-05-15Bibliographically approved
Riede, M., Heidowitzsch, M., Werner, T., Samuel, C., Lopez, E., Brueckner, F., . . . Norman, A. (2024). Additive Manufacturing of ATHENA's large Optical Bench by Direct Energy Deposition. In: Ramón Navarro; Ralf Jedamzik (Ed.), Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI: . Paper presented at SPIE Astronomical Telescopes + Instrumentation 2024, Yokohama, Japan, June 16-21, 2024. SPIE, Article ID 131001I.
Open this publication in new window or tab >>Additive Manufacturing of ATHENA's large Optical Bench by Direct Energy Deposition
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2024 (English)In: Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI / [ed] Ramón Navarro; Ralf Jedamzik, SPIE , 2024, article id 131001IConference paper, Published paper (Other academic)
Abstract [en]

The large class science mission NewATHENA, rescoped by the European Space Agency (ESA) in November 2023, will explore the hot and energetic universe using advanced X-ray technology. The key components of the telescope will be hundreds of Silicon Porous Optics (SPO) modules arranged in an optical bench with a diameter of around 2.7 metres. Considering the overall size, the delicate cell structure and the high aspect ratio in combination with the material-related challenges of Ti6Al4V, additive manufacturing using Direct Energy Deposition (DED) is a promising alternative to conventional processing. In addition to discussing fundamental challenges (e.g. shielding), the development of a highperformance hybrid DED process and associated equipment for robust long-term production will be presented. The developed end-to-end manufacturing approach will be verified by manufacturing and analysing test specimens, geometric demonstrators and representative large breadboards [1], [2].

Place, publisher, year, edition, pages
SPIE, 2024
Series
Proceedings of SPIE, ISSN 0277-786X, E-ISSN 1996-756X ; 13100
Keywords
Advanced manufacturing, Additive manufacturing, Laser metal deposition, Ti-6Al-4V, Large-scale part
National Category
Manufacturing, Surface and Joining Technology
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-110523 (URN)10.1117/12.3020113 (DOI)001327611800040 ()2-s2.0-85205959529 (Scopus ID)
Conference
SPIE Astronomical Telescopes + Instrumentation 2024, Yokohama, Japan, June 16-21, 2024
Funder
The European Space Agency (ESA)
Note

ISBN for host publication: 9781510675230, 9781510675247

Available from: 2024-10-23 Created: 2024-10-23 Last updated: 2025-05-15Bibliographically approved
Kaplan, A. F. H., Hellström, M. & Brueckner, F. (2024). Exploration of laser-induced drop transfer modes from wire for AM. In: M. Schmidt; C.B. Arnold; K. Wudy (Ed.), 13th CIRP Conference on Photonic Technologies (LANE 2024): . Paper presented at 13th CIRP Conference on Photonic Technologies (LANE 2024), Fürth, Germany, September 15-19, 2024 (pp. 194-199). Elsevier
Open this publication in new window or tab >>Exploration of laser-induced drop transfer modes from wire for AM
2024 (English)In: 13th CIRP Conference on Photonic Technologies (LANE 2024) / [ed] M. Schmidt; C.B. Arnold; K. Wudy, Elsevier, 2024, p. 194-199Conference paper, Published paper (Refereed)
Abstract [en]

Laser-induced ablation of drops from a metal wire enables the sequential deposition of voxels, for additive manufacturing. Striving for the goal of controlled, reproducible drop transfer, for this new technique, further trends and phenomena have been studied. Different modes of drop growth along with necking have been observed. Rather reproducible was a growing pending drop underneath the wire tip until separation for a certain size. In contrast, initial transients from the laser-induced recoil pressure can lead to a quick separation of a smaller drop. Initiation of a swinging cycle can also cause drop ablation after one cycle. For too fast wire feeding, the wire underpins the melt for a while in a spoon-like manner. Apart from the drop size, the different modes affect the scatter of the flight trajectory and landing position, as important optimization criteria, for controlled 3D-printing.

Place, publisher, year, edition, pages
Elsevier, 2024
Series
Procedia CIRP, E-ISSN 2212-8271 ; 124
Keywords
wire, drop transfer, boiling front, AM, high speed imaging
National Category
Physical Sciences Materials Engineering
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-110165 (URN)10.1016/j.procir.2024.08.098 (DOI)2-s2.0-85204339171 (Scopus ID)
Conference
13th CIRP Conference on Photonic Technologies (LANE 2024), Fürth, Germany, September 15-19, 2024
Note

Full text license: CC BY-NC-ND 4.0;

Funder:  EU program ERDF Interreg Aurora, co financed by Region Norrbotten (20358021);

Available from: 2024-10-08 Created: 2024-10-08 Last updated: 2025-05-15Bibliographically approved
Müller, M., Gerdt, L., Schrüfer, S., Riede, M., López, E., Brueckner, F. & Leyens, C. (2024). Laser-based directed energy deposition and characterisation of cBN-reinforced NiAl-based coatings. The International Journal of Advanced Manufacturing Technology, 134(1-2), 657-675
Open this publication in new window or tab >>Laser-based directed energy deposition and characterisation of cBN-reinforced NiAl-based coatings
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2024 (English)In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 134, no 1-2, p. 657-675Article in journal (Refereed) Published
Abstract [en]

Within this study, the alloy NiAl–2.5Ta–7.5Cr is investigated as a new matrix material for cBN-reinforced abrasive turbine blade tip coatings as currently used NiCoCrAlY matrix alloys suffer from insufficient strength at the high operating temperatures. Laser-based directed energy deposition with blown powder was applied to produce cBN reinforced NiAl-based coatings on monocrystalline CMSX-4 substrates. For this, powdery titanium-coated cBN and NiAl–2.5Ta–7.5Cr material were co-injected into the process zone to achieve an in situ formation of a NiAl–2.5Ta–7.5Cr/cBN composite. In order to overcome challenges such as cracking susceptibility, inductive preheating of the substrate up to 800 °C was used. Optical and scanning electron microscopy, energy dispersive X-ray spectroscopy, as well as electron backscatter diffraction were applied to analyse the fabricated samples’ microstructure. Additionally, the mechanical properties were evaluated by means of microhardness mappings. This work demonstrates the feasibility of in situ forming a metal matrix composite with a homogeneous distribution of cBN particles. The results show the beneficial effect of high-temperature preheating on the crack formation. However, the study also reveals challenges such as cracking induced by the injected cBN particles as well as severe intermixing of substrate and coating, which yields spatially resolved deviations in the chemical composition and resulting variations in microstructure and hardness.

Place, publisher, year, edition, pages
Springer Nature, 2024
Keywords
Additive manufacturing, Advanced materials, Coatings, Directed energy deposition, Intermetallics, Nickel aluminide
National Category
Manufacturing, Surface and Joining Technology Metallurgy and Metallic Materials
Research subject
Manufacturing Systems Engineering
Identifiers
urn:nbn:se:ltu:diva-108497 (URN)10.1007/s00170-024-14032-6 (DOI)001277218900003 ()2-s2.0-85199503500 (Scopus ID)
Note

Validerad;2024;Nivå 2;2024-08-14 (hanlid);

Funder: Federal Ministry for Economic Affairs and Climate Action (20T1701);

Full text license: CC BY

Available from: 2024-08-09 Created: 2024-08-09 Last updated: 2025-05-15Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0003-4373-3848

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