Additive manufacturing最新文献

{"title":"Laser powder bed fusion vs. single track laser melting of martensitic Ti-Nb: Phase and microstructure formation","authors":"Florian Senftleben ,&nbsp;Mariana Calin ,&nbsp;Jürgen Eckert ,&nbsp;Matthias Bönisch","doi":"10.1016/j.addma.2025.104884","DOIUrl":"10.1016/j.addma.2025.104884","url":null,"abstract":"<div><div>The aim of this work is to explore the fabrication of α″ Ti-Nb via laser powder bed fusion (LPBF) using pre-alloyed ball-milled feedstock powders. Ti-29Nb alloy powder was prepared by mechanical alloying of elemental Ti and Nb powders, using NaCl as milling agent. Pre-alloyed powders were consolidated into bulk cuboids via LPBF and the effect of different build settings on resulting phases, microstructure and porosity was studied. Phases and microstructures of the LPBF parts were compared with those of single tracks on α″ martensitic substrates. Depending on the Nb content, LPBF leads to either planar or cellular-dendritic solidification. α′ and α″ martensites, β and α phase form in the as-built parts. Single track experiments show that planar growth is conducive for the formation of α″ martensite post-solidification. While in-situ alloying is possible for specific LPBF settings, the use of pre-alloyed powders is recommended to enlarge the build parameter space for reproducible as-built microstructures.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104884"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncertainty-driven trustworthy identification paradigm for unstable melt pool state based on acoustic emission in LPBF","authors":"Jiafeng Tang ,&nbsp;Kunpeng Tan ,&nbsp;Junlong Tang ,&nbsp;Zhibin Zhao ,&nbsp;Xingwu Zhang ,&nbsp;Xuefeng Chen","doi":"10.1016/j.addma.2025.104887","DOIUrl":"10.1016/j.addma.2025.104887","url":null,"abstract":"<div><div>Thanks to the high precision and flexibility, laser powder bed fusion (LPBF) has hugged in producing key components for the fields of aerospace and biomedicine. However, ensuring the consistent of quality during the manufacturing process remains a headache challenge. Online monitoring the state of melt pool and implementing related closed-loop feedback control is a promising solution to improve quality stability. Especially, the combination of online monitoring and deep learning (DL)-based methods is gaining significant traction. Unfortunately, the ‘black-box’ nature of DL models reduces their reliability of prediction. Additionally, the complex multiphysics-coupled nature of the melt pool often causes transient fluctuations that manifest the inter-layer and intra-layer heterogeneity in monitoring data, which deepens the credibility crisis of DL methods and closed-loop control. In this work, we propose a reliable paradigm for identifying the unstable state of melt pool over inter-layer and intra-layer in LPBF, MSRIM (<strong>M</strong>elt pool <strong>S</strong>tate <strong>R</strong>eliable <strong>I</strong>dentification <strong>M</strong>odel). It outputs both predictions and their uncertainties, enabling control systems to dynamically adjust strategies based on confidence levels. Concretely, we analyze and investigate the heterogeneity of processing data caused by fluctuations of melt pool under different scenarios, along with the uncertainties introduced by such heterogeneity. Then, we quantify and decompose the uncertainties from different sources, and provides a reliable foundation for online control of quality. Furthermore, we develop a custom LPBF melt pool full-processing acoustic emission (AE) monitoring system and created an AE-based dataset including 36 groups of parameters with three melt pool states for verifying our work. Extensive experiments demonstrate that our paradigm achieves the satisfactory and reliable melt pool state identification.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104887"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144614615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing rheological properties of 3D printed cementitious materials via ensemble machine learning","authors":"Muhammad Saeed Zafar ,&nbsp;Farid Javadnejad ,&nbsp;Maryam Hojati","doi":"10.1016/j.addma.2025.104889","DOIUrl":"10.1016/j.addma.2025.104889","url":null,"abstract":"<div><div>The complex interaction between rheology-modifying admixtures and fresh cementitious mix printability limits 3D printing applications in construction. To optimize the properties of 3D printable concrete, this study presents a machine learning (ML)-based, knowledge-guided framework that integrates data-driven modeling with expert validation. A structured workflow uses a small dataset to predict and refine optimal mix designs. A total of 77 lab samples were prepared with varying amounts of nano-clay (NC), silica fume (SF), bentonite volclay (BC), and methylcellulose (MC). Their rheological properties, including plastic viscosity (VIS), dynamic yield stress (DYS), and static yield stress (SYS), were measured using a rheometer. Ensemble ML models were developed through automated preprocessing, cross-validated hyperparameter tuning, and RMSE-based selection. The top five models per rheological responses were combined using a voting regressor, improving predictive accuracy while mitigating overfitting. Predictions were visualized using contour maps from gridded synthetic data, revealing nonlinear interactions among input features. A key innovation is applying expert ratings to contour maps to guide the selection of high-performing mixes. This step allows domain knowledge to define acceptable printability ranges and helps address ML uncertainty from limited training data. Optimized mixes were selected based on rating maps and re-evaluated through additional rheology and 3D printing tests. The results demonstrated that the mixes met satisfactory extrudability and buildability requirements, confirming the validity of the defined expert rating criteria and the practical utility of the framework in optimizing 3D printable concrete mixes containing the defined additives. The proposed approach ensures both predictive robustness and practical applicability. It enables iterative refinement of models as new data becomes available and offers a systematic approach to navigating complex mix interactions. Overall, combining ensemble modeling, contour visualization, and knowledge-driven evaluation provides a powerful tool for advancing 3D concrete printing mix design.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104889"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasonic rolling-enhanced additive manufacturing of IN718 superalloy: Microstructural refinement and mechanical property improvement through variable power modulation","authors":"Hang Lin ,&nbsp;Zhizhuo Li ,&nbsp;Mingwang Fu ,&nbsp;Hao Yi ,&nbsp;Haiou Zhang ,&nbsp;Runsheng Li","doi":"10.1016/j.addma.2025.104891","DOIUrl":"10.1016/j.addma.2025.104891","url":null,"abstract":"<div><div>Conventional wire and arc direct energy deposition (WADED) of nickel-based superalloys faces critical challenges, such as, coarse columnar grains, pronounced elemental segregation, and suboptimal mechanical performance, hindering their applications in high-value aerospace industries. Herein, we developed an ultrasonic rolling-assisted WADED (UR-WADED) strategy that synergistically couples dynamic plastic deformation with in-situ ultrasonic vibration. Through systematic modulation of ultrasonic power (0–90 %), its effects on dendritic evolution, phase transformation, and dislocation dynamics were decoupled. Multiscale characterization revealed that ultrasonic mechanical excitation induced three key effects: (1) grain refinement was achieved through the combined effects of acoustic cavitation and rolling. Under high-power UR, a mixed grain structure was formed, and the average grain size in the fine-grained region was reduced by 80.5 % (from 178.68 μm to 34.87 μm); (2) The joint action of acoustic streaming and rolling transformed the morphology of the Laves phase from a continuous chain-like distribution into a more dispersed island-like form; (3) Texture randomization occurred, with the maximum intensity of the (001) pole figure reduced by 62 %, accompanied by the generation of a high density of intragranular dislocations. The optimized 90UR-WADED specimen exhibited significant property enhancement: Vickers hardness increased by 42.5 % (376.2 vs 264.1 HV_0.5), while yield and ultimate tensile strengths surged to 768.2 (+55.5 %) and 1072.9 MPa (+38.9 %), respectively, outperforming conventional WADED counterparts. Quantitative strengthening analysis identified grain boundary strengthening (∼59 %) and dislocation hardening (∼23 %) as dominant mechanisms. After heat treatment, the 90UR-WADED sample exhibited a fully equiaxed grain structure, and its mechanical properties surpassed those of wrought IN718. This work established a notable hybrid manufacturing approach that overcomes the intrinsic limitations of arc-based additive manufacturing and provides a scalable pathway for fabricating high-performance superalloy components.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104891"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent monitoring of porosity in laser melting deposition based on deep transfer learning","authors":"Fenglei Zheng,&nbsp;Ke Peng,&nbsp;Yangyang Zhu,&nbsp;Qingsong Bai,&nbsp;Zongping Wang,&nbsp;Luofeng Xie,&nbsp;Ming Yin","doi":"10.1016/j.addma.2025.104905","DOIUrl":"10.1016/j.addma.2025.104905","url":null,"abstract":"<div><div>Porosity defects represent one of the greatest challenges to the stability and reliability of parts produced by Laser Melting Deposition (LMD). Integrating sensor data with machine learning algorithms for porosity monitoring has garnered considerable attention. However, current machine learning approaches heavily rely on abundant labeled data and the assumption of independent and identically distributed (i.i.d.) data. In LMD processes, variations in process conditions could potentially affect the distribution and correlation of process data. Collecting sufficient labeled data for new processes from scratch is time-consuming and costly, and conducting experiments that cover all possible conditions to build a comprehensive dataset is highly challenging. Inspired by transfer learning, we propose a deep subdomain adaptation method based on ShuffleNetV2 (DSAS) to monitor porosity for the LMD process. First, based on the dynamic evolution of the melt pool and the physical mechanism of pore formation, local defect samples with corresponding structures were constructed, along with a source-domain porosity monitoring network. Second, for the more complex and variable conditions in the target domain, porosity monitoring was achieved using the proposed DSAS. Specifically, samples collected under the source and target domain were projected into different subdomains based on porosity levels. Domain alignment was achieved by calculating the Local Maximum Mean Discrepancy (LMMD) distance between domains. DSAS not only learns cross-domain features to adapt the model to diverse process conditions during transfer, but also alleviates the challenge of similar boundary samples in porosity monitoring by aligning features within subdomains. Experimental results demonstrate that the proposed method effectively leverages historical knowledge from relevant experimental data to monitor porosity under complex and changeable working conditions. This significantly improves model development efficiency and reduces costs in data-scarce scenarios, offering substantial value for the implementation and promotion of quality monitoring in LMD processes.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104905"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolution of eutectic microstructure and strength in an Al-Ce-Ni-Mn-Sc-Zr alloy fabricated by laser powder-bed fusion","authors":"Clement N. Ekaputra ,&nbsp;Jon-Erik Mogonye ,&nbsp;David C. Dunand","doi":"10.1016/j.addma.2025.104903","DOIUrl":"10.1016/j.addma.2025.104903","url":null,"abstract":"<div><div>We characterize the evolution of microstructure and mechanical properties during thermal exposure of a strong and creep-resistant Al-11.5Ce-3.4Ni-0.6Mn-0.11Sc-0.34Zr (wt%) alloy fabricated by laser power-bed fusion (L-PBF). The alloy composition is based on a cast, near-eutectic alloy (Al-10.4Ce-3.5Ni-0.80Mn-0.25Sc-0.12Zr, wt%) with extreme creep- and coarsening-resistance for high-temperature applications. The as-fabricated L-PBF alloy exhibits a continuous network of fine, eutectic Al₁₁Ce₃ and Al₂₇Ce₃Ni₆ phases. The compositions of these phases are non-stoichiometric in the peak-aged alloy, but shift to the stoichiometric compositions during long-term thermal exposure. Upon aging at 300–400°C, L1₂-Al₃(Sc,Zr) nanoprecipitates form in the α-Al matrix and at the matrix/eutectic interface; Mn solutes are present in the Al matrix, but to a lesser extent than in the cast alloy. The refined eutectic phases are the dominant strengthening mechanism in the L-PBF alloy, and their evolution controls the loss of strength and creep resistance at elevated temperatures. During long-term thermal exposure at 300–400°C, the continuous eutectic network fragments into discontinuous elongated particles, which then spheroidize and coarsen. The initial eutectic fragmentation is associated with a significant decrease in room-temperature hardness and work-hardening capacity; the subsequent particle coarsening is slower and results in a more gradual decline in room-temperature strength and hardness. At 300°C, the alloy demonstrates excellent creep resistance, with dislocation creep threshold stresses of 109–149 MPa, depending on the aging condition and eutectic microstructure. Lastly, we demonstrate via analytical and numerical (finite-element) modelling that inhibition of dislocation motion, rather than load transfer, is the dominant strengthening mechanism imparted by the eutectic precipitates.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104903"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144720955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of deposition conditions and in-nozzle geometry on alignment of fiber additives in fused filament fabrication","authors":"Hoang Minh Khoa Nguyen ,&nbsp;Ankur Jain ,&nbsp;Dong-Wook Oh","doi":"10.1016/j.addma.2025.104894","DOIUrl":"10.1016/j.addma.2025.104894","url":null,"abstract":"<div><div>Incorporating low concentrations of fiber-type additives into thermoplastic feedstocks for polymer additive manufacturing processes such as fused filament fabrication (FFF) can enhance mechanical properties of printed parts, thereby enabling superior performance. However, controlling the alignment of such fibers remains very challenging due to the complex interactions between internal nozzle flow and external conditions after filament extrusion. This work investigates a strategy for actively promoting perpendicular fiber alignment by using embedded orifice structures. Flow visualization experiments were carried out using optically transparent filaments in order to investigate this strategy. A suspension of ball-milled carbon fibers in uncured polydimethylsiloxane was extruded onto a heated bed, replicating realistic FFF processing conditions. Real-time fiber orientation angles were measured during extrusion and within the solidified filament. It was shown that the extent of squeeze flow can be controlled by altering the gap between the nozzle tip and the printing bed. In addition, two nozzle configurations were evaluated—a straight channel nozzle and an orifice embedded nozzle. Experimental results were compared with computational fluid dynamics simulations to characterize fiber rotation dynamics. A key finding of practical interest is that the alignment of fibers in the printed part can be controlled by adjusting the in-nozzle channel geometry and the gap distance. The findings provide valuable guidance into optimizing FFF parameters for producing high-performance polymer composites with fiber additives.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104894"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144694568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Underlying role of micro-area energy input mode on formability and performance of high-strength aluminum alloy processed by micro laser powder bed fusion (μ-LPBF)","authors":"He Liu,&nbsp;Dongdong Gu,&nbsp;Guangjing Huang,&nbsp;Linxuan Li,&nbsp;Bin Pei,&nbsp;Youyou Zhou","doi":"10.1016/j.addma.2025.104892","DOIUrl":"10.1016/j.addma.2025.104892","url":null,"abstract":"<div><div>Micro laser powder bed fusion (μ-LPBF) additive manufacturing technology has significant advantages in forming extremely fine metallic structures, but controlling the laser scanning mode and attendant heat input in small areas has become much more difficult. In the present work, the regulation mechanisms of thermal flow characteristics on flaw control and performance evolution exhibited a pivotal role in fabricating high-strength Al-Mg-Sc-Zr alloys via μ-LPBF. Based on the simulation-guided laser printing path design, the effects of energy input mode on μ-LPBF formability, grain distribution and mechanical anisotropy were systematically studied in this work. Experimental results demonstrated that periodic intensive long-range variation scanning effectively enhanced melt uniformity and the dimensional accuracy of the fine lattice structure, achieving the smallest average flaw volume of 2.62 × 10⁻⁶ mm³ and near-full density (99.94 %). Due to the double exposure and intrinsic heat treatment, the long range regular remelted samples possess the least residual stress of 53.7 ± 1.8 MPa and the decreased precipitate density (1.78 ×10²⁴ mm⁻³) of the secondary Al₃(Sc, Zr) precipitates after aging treatment. The transient thermal accumulation induced unstable flow patterns that intensify pore cluster at the overlap regions, acting as three-dimensional interconnected networks of potential crack propagation pathways and it led to a remarkable deterioration of elongation. In contrast, rotational thermal flow mode enabled periodic reconstruction of thermal gradients, endowing horizontally aged specimens with superior strength-ductility synergy (UTS of 579.21 ± 3.35 MPa and elongation of 11.94 ± 2.32 %) and ignorable mechanical anisotropy induced by building direction. Fractography-coupled EBSD results of short-range dispersion mode samples reveals the combination of crack propagation along &lt; 001 &gt; texture orientations and the strain localization that induced by pores clusters in island boundary is responsible for the mechanical properties anisotropy. Conclusions in this work will advance the understanding of the printing mode-microstructural features and mechanical properties relationships for high strength aluminum alloy fabricated by μ-LPBF. It also provides a reference for the high-quality fabrication of the miniaturized metallic structures with fine features and satisfactory surface quality.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104892"},"PeriodicalIF":10.3,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive capture-based cellular automata for addressing challenges in modeling grain growth competition in additive manufacturing","authors":"Zhengtong Shan ,&nbsp;Ho Won Lee ,&nbsp;Dong-Kyu Kim","doi":"10.1016/j.addma.2025.104908","DOIUrl":"10.1016/j.addma.2025.104908","url":null,"abstract":"<div><div>Cellular automata (CA) models are widely used to predict microstructure evolution during solidification in additive manufacturing (AM). However, conventional time-stepping CA frameworks often require fine temporal resolution to mitigate multiple grain assignment errors and discretization inaccuracies—particularly under steep thermal gradients and rapid solidification conditions. To address these challenges, an adaptive capture (AC) algorithm is introduced within the conventional time-stepping framework. This algorithm dynamically computes precise capture times for each competing grain and reconstructs grain envelope evolution based on the local undercooling of newly captured cells. As a result, accurate grain structure prediction can be achieved even under coarse time-step conditions, with accuracy comparable to that of fine-resolution CA models, while significantly improving computational efficiency. The AC-CA framework is systematically evaluated under both idealized and practical AM conditions to quantify the impact of time-step size and mesh resolution on grain growth prediction. By coupling with finite element (FE)-derived thermal fields, the model is validated in laser powder bed fusion (LPBF) simulations, demonstrating high scalability and fidelity in multiscale microstructure prediction. Additionally, the AC-CA model incorporates accelerating strategies, such as the simplified thermal unit method, which significantly improve computational efficiency. This enables the simulation of larger domains with billions of computational cells while maintaining high fidelity. In summary, the AC-CA approach effectively addresses long-standing challenges associated with time resolution in time-stepping CA models and provides a robust, efficient solution for microstructure simulation in additive manufacturing.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104908"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144748794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced strength with retained ductility in SLM-processed high-entropy alloys via dislocation regulation in L2₁-BCC co-precipitate","authors":"Xue Li ,&nbsp;Xiaoqiang Wang ,&nbsp;Xianglong Dai ,&nbsp;Yi Li ,&nbsp;Yan Zhou ,&nbsp;Yuan Wu ,&nbsp;Xinjian Yuan ,&nbsp;Shifeng Wen ,&nbsp;Yusheng Shi","doi":"10.1016/j.addma.2025.104899","DOIUrl":"10.1016/j.addma.2025.104899","url":null,"abstract":"<div><div>L2₁-BCC co-precipitates were formed in a Fe-Co-Ni-Cr-Al-Ti high-entropy alloy fabricated via selective laser melting, followed by a specific heat treatment process. Two types of co-precipitates were identified based on the scale of the BCC phase, with both exhibiting fully coherent interfaces. For the first time, the dynamic interaction mechanism between co-precipitates and dislocations was revealed through in-situ transmission electron microscope. First, the cross-slip of dislocations occurred, promoting uniform deformation within the co-precipitates. Additionally, back stress exerted by the BCC phase facilitated the activation of slip systems in the L2₁ phase. The dislocation interaction with the L2₁ phase shifted from conventional bypassing to cutting, mitigating stress concentration at the FCC/L2₁ phase boundary. Consequently, a high-density dislocation zone formed in the L2₁ phase near the interface, which not only prevented dislocation pile-up but also promoted cross-slip. Finally, a cracking prevention mechanism associated with the gradient precipitation phase was identified. The co-precipitate structure achieved a remarkable 73.6 % enhancement (813 MPa to 1411 MPa) in ultimate tensile strength compared to the single L2₁ precipitate system while maintaining considerable ductility. These findings provide a foundation for the development of multiphase structural designs in high-entropy alloys and a breakthrough in the strength-ductility trade-off.</div></div>","PeriodicalId":7172,"journal":{"name":"Additive manufacturing","volume":"109 ","pages":"Article 104899"},"PeriodicalIF":11.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}