Additive manufacturing letters最新文献

This work aims to fully characterise the Nb-Ti-Si based alloy (Nb-26Ti-16Si-2.2Al-2Cr), processed by the electron-beam powder-bed-fusion in both as-built and heat-treated conditions, to elucidate the microstructure-property relationships. The as-built condition has [001]-oriented columnar grains of the Nb₃Si phase with the Nbss phase dispersed throughout the microstructure. The microhardness is 645.2 ± 6.7 HV_0.5, and the indentation fracture toughness shows distinct directionality: 7.7 MPa·m^1/2 in the horizontal direction compared to 5.3 MPa·m^1/2 in the vertical direction. Both properties are comparable to the cast version. The directionality is attributed to the underlying mechanisms such as crack bridging, arrest, and micro-crack formation. By contrast, in the heat-treated condition, the alloy exhibits a dual-phase microstructure (Nbss and Nb₅Si₃ phases) with near-equiaxed grain shape due to the Nb₃Si phase decomposition. The fracture toughness increases to 12.1 MPa·m^1/2, at the expense of a reduced microhardness of 564.4 ± 15.0 HV_0.5.

This study proposes a novel deep learning technique for efficient powder morphology characterization, crucial for successful additive manufacturing. The method segments powder particles in microscopy images using Pix2Pix image translation model, enabling precise quantification of size distribution and extraction of critical morphology parameters like circularity and aspect ratio. The proposed approach achieves high accuracy (Structural Similarity Index of 0.8) and closely matches established methods like laser diffraction in measuring particle size distribution (within a deviation of ∼7 %) and allows determination of additional particle attributes of aspect ratio and circualarity in a reliable, repeated, and automated way. These findings highlight the potential of deep learning for automated powder characterization, offering significant benefits for optimizing additive manufacturing processes.

Electron beam melting (EBM), also known as electron beam powder bed fusion (EB-PBF), is a metal additive manufacturing (AM) technology that can make metal parts that are difficult, inefficient, or unachievable through conventional manufacturing routes and other AM technologies. However, a comprehensive understanding of the dynamics of electron beam-matter interactions in EBM remains elusive, which is a barrier for the development and adoption of EBM technology. Here, we report the dynamics and mechanisms of pore formation, pore elimination, and crack elimination in EBM. Three mechanisms of pore formation are observed: (1) pore formation from feedstock powders, (2) pore formation from pre-existing defects, and (3) pore captured by solidification front. One pore elimination mechanism is discovered: pore elimination due to metal vapor condensation, which is unique to EBM. One crack elimination mechanism is uncovered: crack elimination through remelting. These results will enhance the understanding of defect formation and evolution mechanisms in EBM and may inspire the invention of effective approaches to mitigate and control defects (porosity and cracks) in EBM.

Developing high-performance β-Ti alloys is a persistent and long-term demand for the advancement of next-generation biomaterials. In this study, a strategy of leveraging the unique characteristics of laser powder bed fusion (L-PBF) technique and nanocarbon materials was proposed to design a novel carbon-supersaturated β-Ti alloy. Ultrathin graphene oxide (GO) sheets were closely covering onto spherical Ti-15Mo-5Zr-3Al (Ti1553) powders, enhancing laser absorptivity while maintaining good flowability. Consequently, the GO-added Ti1553 builds tended to be denser than the initial ones, indicating an improved additive manufacturability. During L-PBF, GO sheets were completely dissolved into the Ti1553 matrix, generating fully carbon-supersaturated β-Ti structures with a reduced grain size. Thanks to the exceptional strengthening effects of high-concentration solid-solution carbon (∼0.05 wt%), the GO/Ti1553 builds achieved a high ultimate tensile strength of 1166 MPa. Moreover, as revealed by the immunofluorescence staining experiments, the GO/Ti1553 builds demonstrated a retained cytocompatibility. This study provides new insight into composition and processing design of high-performance Ti components for biomedical applications.

Process parameters optimization in additive manufacturing (AM) is usually required to unlock superior properties, and this is often facilitated by modeling. In electron beam powder bed fusion (E-PBF), the preheat temperature is an important parameter to be optimized as it significantly influences the microstructure and properties. Here we compare the effect of two preheat temperatures (1000 and 950°C, above and below δ-phase solvus temperature) on the microstructural evolution of E-PBF IN718 Ni-based superalloy. Using thermal and thermo-kinetic modeling, we predict microstructural changes and compare them with experimental findings. A decrease of only 50°C in the preheat temperature has a low impact on the solidification microstructure with a slight reduction in columnar grain width. In the solid-state, higher preheating causes intergranular δ-phase precipitation, contributing to a higher γ" precipitation potential, formation of co-precipitates, and higher hardness. The lower preheat temperature induces intergranular and intragranular δ-phase precipitation, reducing the γ" precipitation potential and hardness. The chemical composition of γ' and γ" is largely unaffected by the preheat temperature variation. These insights underscore the importance of preheat temperature optimization in microstructure design and property control during E-PBF.

The present work systematically investigates the effects of BN nanopowder functionalization on the processability, microstructure and tensile response of the custom Powder Bed Fusion Laser Beam (PBF-LB) Al alloy ‘AMALLOY3D’. The results show that a minor addition of BN (0.3 % by weight) not only produces near fully dense parts (99.91 %), but is also paired with improved flowability, enhancing the overall processability. Electron backscatter diffraction (EBSD) analysis revealed the transformation to a fully equiaxed grain structure in the BN-functionalized material, resulting in a 40 % increase in yield strength. Energy dispersive spectroscopy using a scanning transmission electron microscope (STEM-EDS) was employed to reveal the intricate secondary phases’ arrangements. These observations coupled with the help of the CALPHAD approach led to the reconstruction of the solidification history of AMALLOY3D and the BN-functionalized material. The present study unravels the complex dynamics leading to the columnar-to-equiaxed transition in BN-reinforced AMCs, proving that such unique microstructures and exceptional tensile properties can be achieved without compromising PBF-LB processability.

This study reports an anomalous temperature-dependent tensile behavior of laser-beam powder bed fusion (PBF-LB) processed Cu–Cr–Zr alloy. The yield strength of the alloy initially decreases as the temperature increases to 200±5 MPa and then increases to 350±11 MPa at 500°C before reducing to 234±6 MPa at 600°C. The microstructure consists of elongated Cu grains with a high concentration of Cr solute (∼1 mass%), resulting from rapid solidification during the PBF-LB process. Transmission electron microscopy for the specimens deformed at 500°C revealed the presence of numerous nanoscale Cr-rich particles embedded inside the supersaturated solid solution of the Cu matrix. Nanoscale particles can act as barriers to dislocation motion, leading to an increase in internal stress during plastic deformation at elevated temperatures. This work provides the high potential of post heat treatments for achieving superior mechanical performance using high solute supersaturation formed by the PBF-LB process.

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. We introduce “AMGPT”, a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating a curated corpus of literature. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from $\sim$50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. A query retrieval function has also been added, enabling the system to fetch relevant literature from Elsevier journals based on the context of the query. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text.

Single element detection, spatial frequency modulation imaging (SPIFI) is deployed in a laser processing environment. SPIFI images are used to monitor a laser melting process, like that encountered during additive manufacturing, with an exposure time of $120\mu s$, which enables real-time, in-situ monitoring of melt track formation coaxial with the processing laser. SPIFI images from a single photodiode are shown to be comparable to or better than white light camera images taken at the same numerical aperture. SPIFI deployed in this manner represents a disruptive new sensor metrology system that, when coupled with developing process models, can ultimately be used to validate statistically significant process parameter-signature-quality relationships with quantified uncertainty.