Additive manufacturing最新文献_第2页

Layer-wise adaptive control of thin wall geometries in laser-powder direct energy deposition 激光粉末直接能量沉积薄壁几何形状的分层自适应控制

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-31 DOI: 10.1016/j.addma.2025.105073

Vittorio Sala, Ambra Vandone, Michele Banfi, Oliver Avram, Federico Mazzucato, Stefano Baraldo, Anna Valente

Direct Energy Deposition (DED) is an Additive Manufacturing technology that enables the manufacturing of metallic parts by melting the material as it is deposited. By stacking single passes over subsequent layers, DED can produce thin-wall structures with high aspect ratios, with potential applications in aerospace and prosthetics. However, the reliable fabrication of thin walls is challenged by heat accumulation and speed reductions at sharp corners, leading to local variations in the deposition rate. Static part programs rely on passive self-stabilization, requiring extensive optimization experiments to find the optimal deposition strategy that deposits the target part height, avoiding local lacks of material or protrusions. While active feedback loops can adjust DED process parameters, their effectiveness in thin-wall structures with sharp corners is limited by temporal real-time constraints, since deviations at a given location can only be corrected after the laser returns to that position in a subsequent pass. This study presents a robust layer-wise adaptive control method for manufacturing thin walls using DED that monitors and optimizes the local stand-off distance. Our approach introduces three key innovations: high-speed online stand-off measurement, a robust layer-wise proportional control system, and an adaptive part program generator. Extensive experiments on triangular thin walls demonstrate that our method significantly reduces height deviations due to protrusions on the corners compared to passive self-stabilization while maintaining a high average layer thickness. Moreover, our control approach proved resilient to variations in DED process parameters, nozzle occlusion, and applies to several thin-wall geometries described by closed contours, enabling the fabrication of complex structures for aerospace and prosthetic applications.

直接能量沉积（DED）是一种增材制造技术，可以通过在沉积过程中熔化材料来制造金属部件。通过在后续层上堆叠单道，DED可以生产具有高纵横比的薄壁结构，在航空航天和假肢领域具有潜在的应用前景。然而，薄壁的可靠制造受到热积累和尖角处速度降低的挑战，导致沉积速率的局部变化。静态零件程序依赖于被动自稳定，需要大量的优化实验来找到沉积目标零件高度的最佳沉积策略，避免局部缺乏材料或突出。虽然主动反馈回路可以调整DED工艺参数，但它们在具有尖角的薄壁结构中的有效性受到时间实时约束的限制，因为在给定位置的偏差只能在激光在随后的通道中返回到该位置后才能纠正。本研究提出了一种鲁棒的分层自适应控制方法，用于使用DED制造薄壁，该方法可以监测和优化局部隔离距离。我们的方法引入了三个关键创新：高速在线隔离测量，鲁棒分层比例控制系统和自适应零件程序生成器。在三角形薄壁上进行的大量实验表明，与被动自稳定相比，我们的方法在保持较高的平均层厚的同时，显着减少了由于角上突出引起的高度偏差。此外，我们的控制方法被证明能够适应DED工艺参数的变化，喷嘴遮挡，并适用于由封闭轮廓描述的几种薄壁几何形状，从而能够制造用于航空航天和假肢应用的复杂结构。

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引用次数: 0

Enhancing the electric conductivity and surface smoothness of photosintered copper films on polyimide substrates 提高聚酰亚胺基板上光烧结铜膜的导电性和表面光洁度

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-30 DOI: 10.1016/j.addma.2025.105072

Victoria V. Beltran , Ruiqi Wang , Jun Young Hong , Youngsoo Jung , Sanghwan Moon , Do-Kyun Kwon , Jung-Kun Lee

The growing demand for rapid processing techniques in electronic materials has driven the development of efficient and scalable methods such as optically activated sintering by intense pulsed light (IPL) annealing. IPL quickly heat conductive metal films to 500 – 600 °C at which metal nanoparticles can be sintered. If the metal film consists of micro- and nano-size particles, sintered nanoparticles connect micro-size particles and enhance the electric conductivity of the film. As a millisecond-scale processing technique, IPL is especially suitable for use on sensitive substrates like polymers which can be easily damaged by conventional sintering of metal particles. This study, motivated by high-frequency packaging needs, focuses on improving DC electrical performance and surface morphology of copper electrodes on polyimide through a multi-step approach. First, computational simulations were performed to establish the damage threshold of the device. Second, IPL annealing was used to optically sinter screen-printed Cu films and enhance their conductivity. Third, Cu films were further processed using the infiltration of Ag MOD ink, cold rolling (CR), and IPL annealing, which decreased the porosity and surface roughness of Cu films. This integrated processing strategy yields conductive and smooth copper films on polyimide substrates. The resistivity of copper films is 8.32 × 10⁻⁶ Ω·cm which is slightly larger than that of bulk copper. The surface roughness is as low as 0.279 µm and the adhesion of the films on polyimide substrate is rated at 4B. These results show that the proposed method effectively improves the microstructure and DC electrical performance of Cu films and provides a promising basis for future studies to quantitatively assess high-frequency transmission performance.

对电子材料快速加工技术日益增长的需求推动了高效和可扩展方法的发展，如强脉冲光（IPL）退火的光活化烧结。IPL快速加热导电金属薄膜到500 - 600 °C，金属纳米颗粒可以烧结。如果金属薄膜由微颗粒和纳米颗粒组成，则烧结的纳米颗粒将微颗粒连接起来，并增强薄膜的导电性。作为一种毫秒级的加工技术，IPL特别适合用于像聚合物这样的敏感基质，这些基质很容易被传统的金属颗粒烧结破坏。本研究以高频封装需求为动力，重点通过多步骤方法改善聚酰亚胺上铜电极的直流电气性能和表面形貌。首先，进行了计算模拟，建立了器件的损伤阈值。其次，利用IPL退火技术对丝网印刷Cu薄膜进行光学烧结，提高其导电性。第三，采用Ag MOD油墨浸渍、冷轧（CR）和IPL退火对Cu薄膜进行进一步处理，降低了Cu薄膜的孔隙率和表面粗糙度。这种集成的加工策略在聚酰亚胺衬底上产生导电和光滑的铜膜。铜膜的电阻率为8.32 × 10−6 Ω·cm，略大于体铜的电阻率。表面粗糙度低至0.279 µm，在聚酰亚胺基板上的附着力达到4B。结果表明，该方法有效改善了Cu薄膜的微观结构和直流电学性能，为今后定量评价Cu薄膜高频传输性能的研究提供了良好的基础。

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引用次数: 0

Formulation of direct ink writing suspensions from coarse and reused B4C powders with ultra-high-temperature pressureless SPS 用超高温无压SPS将粗粒和重复使用的B4C粉末配制成直接油墨书写悬浮液

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-30 DOI: 10.1016/j.addma.2025.105071

Flavie Lebas, Alice Barrioulet, Ghislain Josse, Sylvain Marinel, Charles Manière

Pressureless of boron carbide (B₄C) is very promising to produce high performance B₄C parts useful in many applications. However, processing dense, complex-shaped components from coarse B₄C powders remains particularly challenging due to coarsening-driven sintering and the very high temperatures required. In this work, direct ink writing (DIW) printable B₄C suspensions were formulated using a tailored anionic carboxymethylcellulose binder, specifically designed to enable rapid, high height printing of coarse B₄C powders. The recyclability of defective printed parts was also investigated. Conventional dilatometric sintering confirmed that coarse B₄C powders undergo extensive grain coarsening and incomplete densification at 2200 °C, and sintering aids did not yield significant improvements. To overcome these limitations, ultra-high temperature pressureless spark plasma sintering (UHTP-SPS) was applied at ∼2350°C with rapid heating (200 °C/min), achieving near-full densification without additives. The resulting bimodal microstructure delivered high hardness values up to 33.6 GPa while maintaining flexural strength despite grain growth. Notably, recycled-route parts showed comparable properties to conventional ones, confirming the feasibility of reusing defective components. This study establishes a promising pathway for the cost-effective and sustainable fabrication of dense B₄C components from coarse powders through rapid and high-temperature sintering.

无压碳化硼（B4C）是一种非常有前途的材料，可用于生产高性能的B4C零件。然而，从粗B4C粉末中加工致密、形状复杂的部件仍然特别具有挑战性，因为粗化驱动烧结和所需的温度非常高。在这项工作中，使用定制的阴离子羧甲基纤维素粘合剂配制了直接墨水书写（DIW）可打印的B4C悬浮液，该粘合剂专门用于实现粗B4C粉末的快速、高高度打印。对印刷缺陷件的可回收性进行了研究。常规的膨胀烧结证实，粗B4C粉末在2200℃时发生了广泛的晶粒粗化和不完全致密化，烧结助剂没有产生明显的改善。为了克服这些限制，采用超高温无压火花等离子烧结（UHTP-SPS），在~ 2350°C下快速加热（200 °C/min），在没有添加剂的情况下实现了近乎完全致密化。由此产生的双峰组织提供了高达33.6 GPa的高硬度值，尽管晶粒长大，但仍保持弯曲强度。值得注意的是，回收路线部件显示出与传统部件相当的性能，证实了重复使用缺陷部件的可行性。本研究为从粗粉中快速高温烧结制备致密B4C组分开辟了一条具有成本效益和可持续性的途径。

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引用次数: 0

Nonlinear response-driven inverse design of tubular metamaterials 管状超材料非线性响应驱动逆设计

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-29 DOI: 10.1016/j.addma.2025.105070

Yansong Liu , Meng Zou , Yingchun Qi , Jiyin Xie , Yuzhi Wang , Jiafeng Song , Shucai Xu , Weiguang Fan , Qingyu Yu

To address the research gap in response-driven inverse design of tubular metamaterial configurations, this study proposes an image-driven modeling and data-driven closed-loop optimization framework that enables efficient structural design based on arbitrary target force–displacement (F-D) curves. A parameterized image generation strategy governed by 10 structural parameters is developed, and an integrated image-to-simulation platform (IPATC) is built to generate a database containing 35050 structure–response samples. Based on this dataset, a multimodal input deep learning model is constructed to accurately predict 200-dimensional F-D curves, achieving an average R² of 0.89 and RMSE of 1.45 kN on the validation set. The derived mechanical indicators, namely energy absorption (EA) and initial peak crushing force (IPCF), exhibit average prediction errors of 2.35 % and 2.47 %, respectively. A CNN-based inverse model is further built to regress structural parameters from target curves, while shapley additive explanations (SHAP) analysis reveals the sensitivity of different curve regions to each parameter. To handle the non-uniqueness of the curve-to-structure mapping, a particle swarm optimization (PSO) algorithm is introduced to refine the initial prediction, forming a CNN_PSO hybrid strategy. The optimization reduces the prediction error (1–R²) from 0.33 to 0.14, with the best performance achieved at particle number n = 20, inertia weight w ≥ 0.7 and learning factors c₁ = 2.0, c₂ = 1.0, yielding an RMSE of 1.69 kN. Finally, the experimental results further demonstrate that the inversely designed configurations exhibit high consistency with the target configurations in terms of local buckling positions, collapse sequences, and overall deformation patterns, highlighting the accuracy and physical reliability of the proposed framework in performance-driven design. This study provides an effective technical route for intelligent configuration design of tubular metamaterials driven by mechanical response.

为了解决响应驱动的管状超材料构型逆向设计的研究空白，本研究提出了一种图像驱动的建模和数据驱动的闭环优化框架，使基于任意目标力-位移（F-D）曲线的有效结构设计成为可能。开发了一种由10个结构参数控制的参数化图像生成策略，并构建了一个集成的图像-仿真平台（IPATC），生成了包含35050个结构响应样本的数据库。基于该数据集，构建多模态输入深度学习模型，对200维F-D曲线进行准确预测，验证集上的平均R2为0.89，RMSE为1.45 kN。导出的力学指标，即能量吸收（EA）和初始峰值破碎力（IPCF），平均预测误差分别为2.35 %和2.47 %。进一步建立基于cnn的逆模型，从目标曲线回归结构参数，同时shapley加性解释（SHAP）分析揭示了不同曲线区域对各参数的敏感性。为解决曲线-结构映射的非唯一性问题，引入粒子群优化算法对初始预测进行细化，形成CNN_PSO混合预测策略。优化后的预测误差（1-R2）从0.33降低到0.14，在粒子数n = 20，惯性权重w≥ 0.7，学习因子c1 = 2.0,c2 = 1.0时达到最佳效果，RMSE为1.69 kN。最后，实验结果进一步表明，反设计构型在局部屈曲位置、坍塌顺序和整体变形模式方面与目标构型具有较高的一致性，突出了所提框架在性能驱动设计中的准确性和物理可靠性。该研究为基于力学响应驱动的管状超材料智能化结构设计提供了有效的技术途径。

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引用次数: 0

Airborne acoustic emission enables sub-scanline keyhole porosity quantification and effective process characterization for metallic laser powder bed fusion 机载声发射可以实现亚扫描线锁孔孔隙度量化和有效的金属激光粉末床熔融过程表征

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-27 DOI: 10.1016/j.addma.2025.105062

Haolin Liu , David Guirguis , Xuzhe Zeng , Logan Maurer , Vigknesh Rajan , Niloofar Sanaei , Chi-Ta Yang , Jack L. Beuth , Anthony D. Rollett , Levent Burak Kara

Keyhole-induced (KH) porosity, which arises from unstable vapor cavity dynamics under excessive laser energy input, remains a significant challenge in laser powder bed fusion (LPBF). This study presents an integrated experimental and data-driven framework using airborne acoustic emission (AE) to achieve high-resolution quantification of KH porosity. Experiments conducted on an LPBF system involved in situ acquisition of airborne AE and ex situ porosity imaging via X-ray computed tomography (XCT), synchronized spatiotemporally through photodiode signals with submillisecond precision. We introduce KHLineNum, a spatially resolved porosity metric defined as the number of KH pores per unit scan length, which serves as a physically meaningful indicator of the severity of KH porosity in geometries and scanning strategies. Using AE scalogram data and scan speed, we trained a lightweight convolutional neural network to predict KHLineNum with millisecond-scale temporal resolution, achieving an

R^{2}

value exceeding 0.8. Subsequent analysis identified the

35

–

45

kHz frequency band of AE as particularly informative, consistent with known KH oscillations. Beyond defect quantification, the framework also enables AE-driven direct inference of KH regime boundaries on the power–velocity process map, offering a noninvasive and scalable component to labor-intensive post-process techniques such as XCT. We believe this framework advances AE-based monitoring in LPBF, providing a pathway toward improved quantifiable defect detection and process control.

在激光能量输入过大的情况下，由不稳定的气腔动力学引起的锁孔诱导（KH）孔隙是激光粉末床熔合（LPBF）中的一个重要挑战。本研究提出了一个综合实验和数据驱动的框架，利用机载声发射（AE）来实现高分辨率的KH孔隙度量化。在LPBF系统上进行的实验涉及通过x射线计算机断层扫描（XCT）原位采集机载声发射和非原位孔隙度成像，通过光电二极管信号进行时空同步，精度为亚毫秒。我们引入了KHLineNum，这是一种空间分解孔隙度度量，定义为每单位扫描长度的KH孔隙数量，它可以作为几何形状和扫描策略中KH孔隙度严重程度的物理有意义的指标。利用AE尺度图数据和扫描速度，我们训练了一个轻量级的卷积神经网络，以毫秒尺度的时间分辨率预测KHLineNum， R2值超过0.8。随后的分析发现，35-45 kHz的声发射频段信息特别丰富，与已知的KH振荡一致。除了缺陷量化之外，该框架还支持ae驱动的功率-速度过程图上KH状态边界的直接推断，为劳动密集型后处理技术（如XCT）提供非侵入性和可扩展的组件。我们相信这个框架在LPBF中推进了基于ae的监控，为改进的可量化缺陷检测和过程控制提供了途径。

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引用次数: 0

PowderJet: Spherical metal powder production via multi-orifice droplet-on-demand metal jetting 粉末喷射：通过多孔液滴按需金属喷射生产球形金属粉末

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-27 DOI: 10.1016/j.addma.2025.105069

Viktor Sukhotskiy , Jesse Ahlquist , Shahryar Mooraj , Ziheng Wu , Eric S. Elton , Alexandre Reikher , Hunter B. Henderson , Alexander A. Baker

Leading metal additive manufacturing techniques, such as laser powder bed fusion and directed energy deposition, rely on high-quality spherical metal powders. However, traditional powder production methods like gas atomization face limitations, including low in-spec yield, asphericity, and internal porosity. We introduce PowderJet, a powder production platform that uses electromagnetic pulses to eject liquid metal droplets from a multi-orifice nozzle. Unlike stochastic methods, PowderJet tightly controls powder size, distribution, and purity through a droplet-on-demand approach. We detail the system’s design, operation, and performance using a combined experimental and computational fluid dynamics (CFD) framework. Initial results with Al4008 and Cu110 alloys demonstrate successful production, yielding unsieved aluminum powder batches with a mean diameter of 200 µm and a narrow size distribution (15 µm standard deviation). The produced powders are highly spherical, achieving a roundness > 0.95. PowderJet operates with a small melt volume (3 mL) and supports continuous refilling, enabling production rates between 30 and 140 cm³/hr depending on jetting frequency, number of orifices and particle size. CFD simulations show that future systems could achieve rates exceeding 1000 cm³/hr for particle sizes as small as 40 µm. PowderJet’s high yield of in-spec powder makes it ideal for producing precious or hazardous materials that are inefficient to manufacture using conventional methods. This platform offers a scalable, precise, and efficient solution for producing high-quality powders tailored for advanced manufacturing applications.

领先的金属增材制造技术，如激光粉末床熔合和定向能沉积，依赖于高质量的球形金属粉末。然而，传统的粉末生产方法，如气体雾化，面临着一些限制，包括低成品率、非球形和内部孔隙率。我们介绍了粉末生产平台PowderJet，该平台使用电磁脉冲从多孔喷嘴喷射液态金属液滴。与随机方法不同，PowderJet通过按需滴剂方法严格控制粉末大小、分布和纯度。我们使用实验和计算流体动力学（CFD）相结合的框架详细介绍了系统的设计、操作和性能。Al4008和Cu110合金的初步结果表明，生产成功，生产出的未筛选铝粉批次平均直径为200 µm，尺寸分布窄（15 µm标准差）。生产的粉末是高度球形的，达到圆度>； 0.95。PowderJet的熔体体积很小（3 mL），支持连续再填充，根据喷射频率、孔数和粒径的不同，生产率在30至140 立方厘米/小时之间。CFD模拟表明，未来的系统可以实现超过1000 cm³/hr的颗粒尺寸小到40 µm的速率。PowderJet的高产量符合规格的粉末使其成为生产珍贵或危险材料的理想选择，这些材料使用传统方法制造效率低下。该平台提供了一个可扩展的，精确的，高效的解决方案，为生产高质量的粉末定制先进的制造应用。

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引用次数: 0

Physics-informed neural networks for thermal modeling transferable across paths, print parameters, and beam profiles 物理信息神经网络的热建模可转移的路径，打印参数，和光束轮廓

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-25 DOI: 10.1016/j.addma.2025.105060

Meysam Faegh , Rakshith Reddy Sanvelly , Reihane Arabpoor , Prahalada Rao , Tuhin Mukherjee , Azadeh Haghighi

Accurate thermal prediction is a critical step toward achieving high-quality metal additive manufacturing (AM) components, where temperature evolution is tightly coupled with the complex laser scan path, process parameters, and laser profiles. Achieving accurate and near real-time thermal predictions is essential for process map optimization prior to printing, enabling rapid evaluations within optimization loops without the prohibitive cost of simulations. However, such fast predictions have been limited by conventional modeling approaches, which are either based on time-consuming numerical simulations or require large volumes of data to train machine learning models. In this work, a Physics-Informed Neural Network (PINN) framework is introduced, through which near real-time, data-free thermal prediction is enabled. Power-velocity-position maps for a given scan layer within the Gcode along with laser profiles are directly embedded into the neural network, and the underlying thermal physics is enforced without the use of external training data. The method is verified against numerical simulations, with a maximum relative error of only 3.36 % at peak temperatures.

By leveraging the transfer learning capability, the model achieves a 60 % reduction in training time, allowing adaptation across various path planning strategies, process maps, and beam profiles. Furthermore, immediate thermal field estimations of a given path across various process maps are enabled by a quick-shot prediction approach, offering a practical solution for near real-time predictions in AM process design optimization workflows. Finally, the study provides key insights into training PINNs and optimizing architecture, establishing a foundation for more accurate, real-time thermal predictions in metal AM.

准确的热预测是实现高质量金属增材制造（AM）部件的关键一步，其中温度演变与复杂的激光扫描路径、工艺参数和激光轮廓紧密相关。实现准确和接近实时的热预测对于打印前的工艺图优化至关重要，可以在优化循环中进行快速评估，而无需过高的模拟成本。然而，这种快速预测受到传统建模方法的限制，传统建模方法要么基于耗时的数值模拟，要么需要大量数据来训练机器学习模型。在这项工作中，引入了一个物理信息神经网络（PINN）框架，通过它可以实现近实时、无数据的热预测。Gcode中给定扫描层的功率-速度-位置图与激光配置文件一起直接嵌入到神经网络中，并且无需使用外部训练数据即可强制执行底层热物理。数值模拟结果表明，该方法在峰值温度下的最大相对误差仅为3.36 %。通过利用迁移学习能力，该模型在训练时间上减少了60% %，允许跨各种路径规划策略、过程图和光束轮廓进行适应。此外，通过快速预测方法，可以实现跨各种工艺图的给定路径的即时热场估计，为增材制造工艺设计优化工作流程中的近实时预测提供了实用的解决方案。最后，该研究为训练pin和优化架构提供了关键见解，为金属增材制造中更准确、实时的热预测奠定了基础。

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引用次数: 0

Enhancing printability of titanium aluminides using laser powder bed fusion by exploiting an in-situ massive phase transformation 利用原位大规模相变技术提高钛铝化物的可印刷性

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-25 DOI: 10.1016/j.addma.2025.105067

Srinivas Aditya Mantri , Tirthesh Ingale , Xuesong Xu , Abhishek Sharma , Karl Peter Davidson , M.S.K.K.Y. Nartu , Sai Sree Varahabatla , Narendra B. Dahotre , R.V. Ramanujan , Rajarshi Banerjee

Alloys based on γ-TiAl have attracted considerable worldwide attention for high temperature energy applications. Additive manufacturing (AM) of these alloys is often challenging due to the rapid solidification rates and the subsequent rapid thermal cycling encountered. Severe cracking is usually observed, especially in the case of laser powder bed fusion (LPBF) processing of γ-TiAl based alloys. Here, we demonstrate a novel strategy for successful LPBF processing of low crack alloys of γ-TiAl based Ti-48Al-2Cr-2Nb, with excellent room temperature properties including compressive yield stress 1113 ± 17 MPa, ultimate compressive true stress 1616 ± 89 MPa, good strain-hardenability, and overall compressive strain 7.8 ± 1.2 %. This room temperature yield stress is nearly twice as previously reported values for AM processed γ-TiAl based Ti-48Al-2Cr-2Nb alloy. The improved cracking resistance and mechanical properties can be attributed to a rapid in situ solid-state massive phase transformation which occurs when the stage is heated to a temperature of 900 °C as compared to 600 °C. This massive transformation converts large columnar α grains, to uniform fine equiaxed γ grains. The present study reveals an innovative approach for AM processing of low toughness materials, i.e., by triggering an in-situ, post solidification, solid-state phase transformation to form fracture-tolerant, fine, equiaxed grains, offering enhanced mechanical properties.

基于γ-TiAl的合金在高温能源方面的应用引起了全世界的广泛关注。由于这些合金的快速凝固速率和随后的快速热循环，增材制造（AM）通常具有挑战性。在激光粉末床熔合（LPBF）加工γ-TiAl基合金时，经常观察到严重的开裂现象。这里,我们展示小说成功的战略LPBF处理低裂纹合金γTi-48Al-2Cr-2Nb钛铝合金,具有优良的室温性能包括压缩屈服应力1113 ± 17 MPa,极限压缩真应力 1616±89  MPa, strain-hardenability好,整体压缩应变7.8 ±1.2  %。这种室温屈服应力几乎是先前报道的AM加工γ-TiAl基Ti-48Al-2Cr-2Nb合金的两倍。抗裂性能和力学性能的提高可归因于当阶段加热到900°C时发生的快速原位固态大块相变，而不是600°C。这种大规模的转变将大柱状α晶粒转变为均匀的细等轴γ晶粒。本研究揭示了一种用于增材制造低韧性材料的创新方法，即通过触发原位、凝固后的固态相变，形成耐断裂、细小、等轴的晶粒，从而提高机械性能。

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引用次数: 0

Giant flexoelectricity of additively manufactured polylactic acid 增材制聚乳酸的大挠曲电

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-25 DOI: 10.1016/j.addma.2025.105066

Dylan J. Balter , Colin McMillen , Alec Ewe , Jonathan Thomas , Samuel Silverman , Lalitha Parameswaran , Luis Fernando Velásquez-García , Emily Whiting , Steven Patterson , Hilmar Koerner , Keith A. Brown

Flexoelectricity is the electrical response that originates when insulating materials are subjected to a strain gradient. This effect is generally considered to be small but known to depend sensitively on material microstructure. This paper explores the hypothesis that the microstructure produced by additive manufacturing (AM) can strongly influence flexoelectricity. Surprisingly, it is found that minor changes to this microstructure produced using fused filament fabrication, a mainstream approach for additively manufacturing thermoplastics, can lead to enormous changes in the magnitude and polarity of the flexoelectric response of polylactic acid (PLA). To explain these changes, a layer dipole model (LDM) is proposed that connects the in-plane shear in each layer to the electrical polarization that it produces. This model explains three independent mechanisms that were identified and that collectively allow one to drastically increase the flexoelectric effect by 173 fold: (1) choosing printing settings to optimize the geometry of pores between extruded lines, (2) choosing the infill of each layer such that bending-induced strain produces productive in-plane shear stresses, and (3) post-deposition annealing of the printed material to increase its crystallinity. This understanding will enable future sensors in which the structural material is also responsible for electromechanical functionality.

挠曲电是绝缘材料受到应变梯度时产生的电响应。这种影响通常被认为是很小的，但已知它敏感地依赖于材料微观结构。本文探讨了增材制造（AM）产生的微观结构对柔性电的影响。令人惊讶的是，研究发现，使用熔融长丝制造（增材制造热塑性塑料的主流方法）产生的这种微观结构的微小变化会导致聚乳酸（PLA）挠性电响应的幅度和极性发生巨大变化。为了解释这些变化，提出了一种层偶极子模型（LDM），将每层的面内剪切与其产生的电极化联系起来。该模型解释了已确定的三种独立机制，它们共同允许人们将挠曲电效应大幅提高173倍：(1)选择印刷设置以优化挤出线之间孔隙的几何形状，(2)选择每层的填充，使弯曲引起的应变产生有效的面内剪切应力，以及(3)印刷材料的沉积后退火以增加其结晶度。这种理解将使未来的传感器结构材料也负责机电功能。

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In-situ neutron diffraction and crystal plasticity modeling of additively manufactured 15–5PH stainless steel: Effect of temperature and building strategy 增材制造15-5PH不锈钢的原位中子衍射和晶体塑性建模：温度和制造策略的影响

IF 11.1 1区工程技术 Q1 ENGINEERING, MANUFACTURING

Additive manufacturing

Pub Date : 2025-12-25 DOI: 10.1016/j.addma.2025.105068

Xiaodan Zhang , Hobyung Chae , Lisong Cao , E-Wen Huang , Jun Hyun Han , Soo Yeol Lee , Huamiao Wang

The deformation behavior and lattice strain evolution of additively manufactured (AM) stainless steel under tension are comprehensively investigated by using in-situ neutron diffraction and the elastic-viscoplastic self-consistent model incorporating phase transformation (EVPSC-PT). Two tensile specimens with distinct building orientations (AM-V and AM-H samples) are fabricated, and tensile tests are performed at both room temperature (300 K) and cryogenic temperature (100 K). The stress-strain response and lattice strains of the AM steels are analyzed based on experimental results and predictions from the EVPSC-PT model. The AM steels exhibit a unique microstructure and macroscopic deformation behavior depending on the building orientation and testing temperature. The lattice strains of the two samples demonstrate an orientation-dependent variation due to the material's elastic anisotropy, with the (200) orientation showing the lowest stiffness in both FCC and BCC phases. Phase transformation (PT) plays a critical role in the mechanical behavior of AM steel, and EVPSC-PT model further reveals that phase transformation accommodates plastic strain and delays stress accumulation in the early stage, while the high hardening capacity of the transformed martensite enhances overall work hardening after transformation completes.

采用原位中子衍射和结合相变的弹粘塑性自一致模型（EVPSC-PT）对增材制造（AM）不锈钢在拉伸作用下的变形行为和晶格应变演化进行了全面研究。制作了两个具有不同建筑取向的拉伸试样（AM-V和AM-H试样），并在室温（300 K）和低温（100 K）下进行了拉伸试验。基于实验结果和EVPSC-PT模型的预测，分析了AM钢的应力应变响应和晶格应变。AM钢具有独特的微观结构和宏观变形行为，这取决于建筑取向和测试温度。由于材料的弹性各向异性，两种样品的晶格应变表现出与取向相关的变化，（200）取向在FCC和BCC相中都表现出最低的刚度。相变（PT）对AM钢的力学行为起着关键作用，EVPSC-PT模型进一步揭示了相变在早期适应塑性应变并延缓应力积累，而相变马氏体的高硬化能力增强了相变完成后的整体加工硬化。

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