Additive manufacturing letters最新文献_第2页

Linking process parameters to residual stress and distortion in directed energy deposition repair via machine learning and response surface methodology 通过机器学习和响应面方法将工艺参数与定向能沉积修复中的残余应力和变形联系起来

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-02-21 DOI: 10.1016/j.addlet.2026.100370

Joachim C.G. Eng , Louis N.S. Chiu , Aijun Huang , Bernard Rolfe , Wenyi Yan

Laser-directed energy deposition (L-DED) filling repair is often compromised by printing defects like cracking induced by excessive thermal residual stresses. Optimising process parameters is challenging, as complex thermal histories make trial-and-error costly and simulations computationally inefficient. To bridge this, a finite element (FE)-driven machine learning (ML) framework was developed to optimise multi-layer multi-track filling repair bulk quality. The methodology ensures consistent fill volume by enforcing nominal single-track dimensions. A thermomechanically validated FE model generated training data via design of experiment (DoE) strategies. Among evaluated algorithms, the multilayer perceptron (MLP) achieved superior accuracy (R² = 0.98, NRMSE = 2.7 %) as an efficient surrogate. Integrated response surface methodology (RSM) highlighted a critical trade-off, revealing moderate energy density as the optimal compromise for balancing residual stress and distortion. Furthermore, parameter influence analysis identified scan speed as the dominant control variable, closely followed by laser power and preheat temperature. Ultimately, this framework provides a robust and efficient tool for defining optimal process windows in l-DED filling repair.

激光定向能量沉积（L-DED）填充修复经常受到打印缺陷的影响，如由过高的热残余应力引起的裂纹。优化工艺参数具有挑战性，因为复杂的热历史使得试错成本高昂，模拟计算效率低下。为了解决这个问题，开发了一个有限元（FE）驱动的机器学习（ML）框架，以优化多层多轨道填充修复体质量。该方法通过强制执行标称单道尺寸来确保一致的填充体积。通过实验（DoE）策略的设计，一个热力学验证的有限元模型生成训练数据。在评估的算法中，多层感知器（MLP）作为一个有效的替代算法获得了更高的精度（R2 = 0.98, NRMSE = 2.7%）。综合响应面方法（RSM）强调了一个关键的权衡，揭示适度的能量密度是平衡残余应力和变形的最佳折衷。参数影响分析表明，扫描速度是主要控制变量，其次是激光功率和预热温度。最终，该框架为定义l-DED填充修复的最佳工艺窗口提供了一个强大而有效的工具。

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引用次数: 0

Predicting porosity in laser powder bed fusion of metals (PBF-LB/M) using scanning data and machine learning 利用扫描数据和机器学习预测激光粉末床熔融金属（PBF-LB/M）的孔隙率

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-02-02 DOI: 10.1016/j.addlet.2026.100362

Jesus Rivas , Cesar Terrazas , Hugo Estrada , Francisco Medina , James P. Carney

Laser powder bed fusion of metals (PBF-LB/M) remains prone to process-induced porosity, and practical tools that connect scan behavior to defect formation are still limited. In this study, we present a framework that predicts porosity from scan data acquired on a commercial PBF-LB/M system using an embedded high-speed scan acquisition device. The device records scanning position, time, and laser power at high temporal resolution, enabling reconstruction of layer-wise exposure paths that are normally inaccessible to users. From these data, we derive porosity-prone “hotspot” regions based on high linear energy input, energy input gradients, and deceleration or acceleration events along the scan path. Ground-truth porosity is obtained from X-ray Computed Tomography (XCT) of a compact qualification artifact containing multiple geometries, including lattice structures. Across the machine learning evaluated models, LSBoost provides the best overall performance when predicting total pore counts per layer with a Mean Absolute Error (MAE) ≈ 7.29. Prediction of larger pores with equivalent diameters greater than 0.100 mm was slightly more accurate (MAE = 4.747), indicating stronger correlation for critical part anomalies. However, porosity outliers tend to be underestimated, highlighting both the need for improved calibration and the benefit of additional in situ process signals that capture interactions with other process variables such as powder or gas flow. Overall, the results demonstrate that scan-derived hotspot features, combined with machine learning, are a viable basis for in situ identification and prediction of porosity-prone layers in PBF-LB/M.

激光粉末床熔融金属（PBF-LB/M）仍然容易产生工艺引起的孔隙，并且将扫描行为与缺陷形成联系起来的实用工具仍然有限。在这项研究中，我们提出了一个框架，该框架使用嵌入式高速扫描采集设备，从商用PBF-LB/M系统获取的扫描数据中预测孔隙度。该设备以高时间分辨率记录扫描位置、时间和激光功率，从而能够重建通常用户无法访问的分层曝光路径。从这些数据中，我们根据高线性能量输入、能量输入梯度和扫描路径上的减速或加速事件，得出了容易出现孔隙的“热点”区域。地面真实孔隙度是通过x射线计算机断层扫描（XCT）获得的，该图像包含多种几何形状，包括晶格结构。在机器学习评估模型中，LSBoost在预测每层总孔隙数时提供了最佳的整体性能，平均绝对误差(MAE)≈7.29。等效直径大于0.100 mm的较大孔隙的预测精度略高（MAE = 4.747），说明关键部位异常相关性较强。然而，孔隙度异常值往往被低估，这突出了改进校准的必要性和额外的原位过程信号的好处，这些信号可以捕获与其他过程变量（如粉末或气体流量）的相互作用。总体而言，结果表明，扫描衍生的热点特征与机器学习相结合，是PBF-LB/M中易孔隙层的原位识别和预测的可行基础。

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引用次数: 0

Computational predictions of complex property trajectories in compositionally graded alloys 成分梯度合金复杂性能轨迹的计算预测

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.addlet.2026.100359

Jixuan Dong , Hasan Al Jame , Zachary C. Cordero , S. Mohadeseh Taheri-Mousavi

Additive manufacturing enables net-shaped compositionally graded components that satisfy conflicting property requirements through spatial variations in alloy chemistry and microstructure. Although current path-planning methods for compositionally graded alloys emphasize avoiding deleterious phases, property evolution along compositional gradients is equally important because abrupt property changes can degrade structural integrity. In light of this concern, this study integrates high-throughput calculation of phase diagrams (CALPHAD)-based integrated computational materials science (ICME) simulations with variance-based global sensitivity analysis to introduce a framework for designing smoother property transitions. Thermophysical and mechanical properties along binary gradients between pairs of Inconel 718, Monel K-500, and Invar 36 were computed, revealing strongly nonlinear property transitions. Sensitivity analysis identified aluminum as a key driver of variability in thermal expansion coefficient along a transition, and this variability was reduced by tailoring the compositions in the terminal alloys. This framework can be used for similar identification and and tailoring of various property variability to achieve optimal component-level performance.

增材制造可以通过合金化学和微观结构的空间变化来满足相互冲突的性能要求。虽然目前的成分梯度合金路径规划方法强调避免有害相，但沿着成分梯度的性能演变同样重要，因为突然的性能变化会降低结构的完整性。鉴于这一问题，本研究将基于相图的高通量计算（CALPHAD）的综合计算材料科学（ICME）模拟与基于方差的全局灵敏度分析相结合，以引入设计更平滑的性能转变的框架。计算了Inconel 718、Monel K-500和Invar 36对之间二元梯度的热物理和力学性能，揭示了强烈的非线性性质转变。灵敏度分析表明，铝是热膨胀系数沿过渡变化的关键驱动因素，通过调整末端合金的成分可以降低这种变化。该框架可用于类似的识别和裁剪各种属性可变性，以实现最佳的组件级性能。

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引用次数: 0

Continuous microstructure variations with graded properties in directed energy deposition 定向能沉积中具有梯度特性的连续微观结构变化

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-03-06 DOI: 10.1016/j.addlet.2026.100372

Michèle Bréhier , Daniel Weisz-Patrault , Christophe Tournier

Directed energy deposition additive manufacturing is a versatile technique for fabricating complex geometries, where precise control of process parameters is crucial for tailoring microstructure and part properties. Microstructure control strategies usually involve variation of material composition (i.e., functionally graded materials) or interlayer time delay. However, the obtained microstructures are usually uniform in the print direction and exhibit sharp transitions from one layer to the next in the build direction. This paper targets continuous microstructural variation by exploiting active cooling strategies to control cooling conditions. To do so, the scanning speed is continuously varied, necessitating accommodating the bead size variations with non-standard trajectory generation based on a phenomenological law. The proposed strategy is demonstrated on thin-wall structures made of IN718 using a powder-based laser directed energy deposition. The results reveal a continuous microstructural transition along the print direction, characterized by two distinct microstructural regimes with markedly different morphological features and crystallographic textures. This demonstrates the capability of scanning speed modulation to engineer heterogeneous microstructures within a single component, offering insights into tailoring material properties for specific engineering applications.

定向能沉积增材制造是一种用于制造复杂几何形状的通用技术，其中精确控制工艺参数对于定制微观结构和零件性能至关重要。微观结构控制策略通常包括改变材料成分（即功能梯度材料）或层间延迟。然而，所获得的微观结构通常在打印方向上是均匀的，并且在构建方向上从一层到下一层表现出急剧的转变。本文利用主动冷却策略控制冷却条件，针对连续微观组织变化。为了做到这一点，扫描速度是连续变化的，因此需要根据现象学规律生成非标准轨迹来适应磁珠尺寸的变化。采用粉末基激光定向能沉积技术在IN718薄壁结构上进行了验证。结果表明，沿打印方向存在连续的微观结构转变，其特征是两种不同的微观结构，具有明显不同的形态特征和晶体结构。这证明了扫描速度调制在单个组件内设计异质微结构的能力，为定制特定工程应用的材料特性提供了见解。

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引用次数: 0

Future foundries: A convergent manufacturing platform 未来代工厂：融合制造平台

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-02-10 DOI: 10.1016/j.addlet.2026.100364

Shramana Ghosh , Miguel Hoffmann , Lauren Heinrich , Kenton B. Fillingim , Joshua Vaughan , Brian K. Post , Thomas Feldhausen

This article introduces the Future Foundries platform developed at Oak Ridge National Laboratory, a first-generation research system designed to demonstrate convergent manufacturing. Convergent manufacturing brings together additive, subtractive, and transformative processes in a digitally interconnected environment to enable end-to-end production workflows. By linking traditionally discrete steps, convergent platforms accelerate production, improve repeatability, and support high-mix, low-volume manufacturing.

The Future Foundries platform exemplifies this vision in practice by combining four modular, vendor-agnostic process cells that include robotic WAAM, induction heating, optical metrology, and machining, coordinated through an automated pallet handler and a ROS 2-based digital thread. This architecture provides the flexibility and scalability needed for agile production in small and medium-sized manufacturing enterprises and for field deployable manufacturing.

Two case studies illustrate the platform’s capabilities. The first presents an integrated workflow for fabricating, transforming, and repairing critical replacement components, showing how consolidated thermal, additive, inspection, and machining operations reduce manual part handling and streamline process flow. The second case study highlights coordinated multi-part production enabled by automated pallet logistics and multi-cell scheduling. Together, these examples showcase convergent manufacturing as a practical and scalable strategy for strengthening domestic casting and forging capacity, improving supply-chain resilience, and enabling rapid, adaptable production of mission-critical components.

本文介绍了橡树岭国家实验室开发的Future Foundries平台，这是第一代研究系统，旨在演示融合制造。融合制造将增材制造、减法制造和变革性生产流程整合到一个数字化互联环境中，从而实现端到端的生产工作流程。通过连接传统的离散步骤，融合平台加速了生产，提高了可重复性，并支持高混合、小批量生产。Future Foundries平台在实践中体现了这一愿景，它结合了四个模块化的、与供应商无关的工艺单元，包括机器人WAAM、感应加热、光学计量和加工，通过自动化托盘处理器和基于ROS 2的数字线程进行协调。该体系结构为中小型制造企业的敏捷生产和现场可部署制造提供了所需的灵活性和可伸缩性。两个案例研究说明了该平台的功能。第一部分介绍了制造、改造和修复关键替换部件的集成工作流程，展示了综合热、添加、检查和加工操作如何减少人工部件处理并简化工艺流程。第二个案例研究强调了由自动化托盘物流和多单元调度实现的协调多部分生产。总之，这些例子表明，融合制造是一种实用且可扩展的战略，可以加强国内铸造和锻造能力，提高供应链弹性，实现关键任务部件的快速、适应性生产。

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引用次数: 0

Printing multifunctional high-performance polymer parts via the hybridization of high temperature material extrusion thermal and chemical reactive bonding 通过高温材料挤压热和化学反应键合的杂交，打印多功能高性能聚合物部件

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-02-24 DOI: 10.1016/j.addlet.2026.100367

Ian Y. Ho , Jacob Viar , Hutchison Peter , Henry Claesson , Christopher Williams

Direct fabrication of multifunctional, high-performance components integrating structural dielectric and conductive materials remains a key challenge in advanced manufacturing. Additive manufacturing (AM) enables embedding functional elements layer-by-layer into structural parts. Hybrid material extrusion (MEX) systems have combined thermal reaction bonding (MEX-TRB, or fused filament fabrication, FFF) and chemical reaction bonding (MEX-CRB, or direct ink writing, DIW) modalities within the same system to directly produce multifunctional parts. However, these systems have been limited to depositing conductive traces within commodity polymers (e.g., PLA, ABS, PETG) at ambient conditions to prevent premature ink curing. These polymers lack the thermal stability to withstand high-performance ink sintering temperatures (often >250 °C), limiting conductivity of the dispensed functional inks and resulting applications.

This work introduces a novel hybrid MEX-CRB/MEX-TRB system with an actively cooled MEX-CRB head to prevent ink curing while operating in chamber temperatures up to 110 °C, enabling the printing of high-performance polymers. In-situ characterization using embedded thermocouples confirmed that the cooling system maintains ink below critical curing thresholds. Conductivity measurements demonstrated successful in-situ sintering and conductive network formation of silver conductive traces within the heated chamber. Polyphenylene sulfide (PPS) parts with embedded silver traces were fabricated, leveraging the chamber for both polymer printing and trace sintering. Compared to traditional hybrid approaches requiring part shuttling between modalities, this integrated system reduces interlayer cooling and improves polymer interlayer adhesion. These results show that the high-temperature hybrid MEX system effectively balances conflicting thermal requirements of high-performance polymers and conductive inks, enabling efficient multimodality printing for advanced electrical applications.

直接制造集成结构介质和导电材料的多功能高性能部件仍然是先进制造的关键挑战。增材制造（AM）能够将功能元件逐层嵌入结构件中。混合材料挤压（MEX）系统在同一系统内结合了热反应键合（MEX- trb，或熔丝制造，FFF）和化学反应键合（MEX- crb，或直接墨水书写，DIW）模式，直接生产多功能部件。然而，这些系统仅限于在环境条件下在商品聚合物（例如PLA， ABS， PETG）中沉积导电痕迹，以防止油墨过早固化。这些聚合物缺乏热稳定性，无法承受高性能油墨的烧结温度（通常为250°C），从而限制了分配功能油墨的导电性和由此产生的应用。这项工作介绍了一种新型的混合MEX-CRB/MEX-TRB系统，该系统具有主动冷却的MEX-CRB头，以防止油墨固化，同时在高达110°C的腔室温度下工作，从而实现高性能聚合物的打印。使用嵌入式热电偶的现场表征证实，冷却系统使油墨保持在临界固化阈值以下。电导率测量表明，在加热室中成功地原位烧结和银导电痕迹的导电网络形成。制备了嵌入银迹的聚苯硫醚（PPS）部件，利用该腔体进行聚合物打印和痕量烧结。与传统的混合方法相比，这种集成系统减少了层间冷却，提高了聚合物层间的粘附性。这些结果表明，高温混合MEX系统有效地平衡了高性能聚合物和导电油墨相互冲突的热要求，为先进的电气应用实现了高效的多模态印刷。

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引用次数: 0

Multi-camera fault detection in fused filament fabrication printing 熔丝加工印刷中的多相机故障检测

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-01-26 DOI: 10.1016/j.addlet.2026.100360

Shanthalakshmi Kilambi , Aster Tournoy , Muhamad Amani , Jovana Jovanova , Baris Caglar , Kunal Masania

Fused filament fabrication is a popular extrusion 3D printing technology because of its affordability and accessibility. However, the approach often suffers from printing errors that result in wasted time, materials and energy. Convolutional neural networks can be trained to recognise a wide spectrum of printing anomalies from image data in real time, but past work has been limited to a few defect classifications at a time. Here, we introduce a fault detection system, designed to identify a range of errors without interrupting the printing process. Real-time detection is achieved using a pre-trained image recognition and pattern recognition convolutional neural network (CNN) with two mounted cameras on the print bed and a nozzle camera. Two CNN models are developed to classify images into common 3D printing errors for the two camera systems. The nozzle camera model achieves a high validation accuracy of 97.7%. The side camera model achieves comparable performance with a validation accuracy of 97.6%. To integrate the two CNNs into one unified system, a logic-based priority framework was used to improve reliability beyond individual model accuracies by resolving conflicting predictions and leveraging complementary viewing angles from both camera types to detect a broader range of defects. The data fusion framework identifies 12 common errors and has significantly improved the robustness of error classification, in-situ and in real-time, with inference times as small as 220 milliseconds. The results demonstrate the feasibility of a robust multi-input fault detection system to advance the reliability of extrusion 3D printing.

熔融长丝制造是一种流行的挤出3D打印技术，因为它的可负担性和可及性。然而，这种方法经常会出现打印错误，从而浪费时间、材料和能源。经过训练，卷积神经网络可以实时识别图像数据中的各种打印异常，但过去的工作仅限于一次对几个缺陷进行分类。在这里，我们介绍了一个故障检测系统，旨在识别一系列错误而不中断印刷过程。使用预训练的图像识别和模式识别卷积神经网络（CNN）实现实时检测，该网络在打印床上安装了两个摄像头和一个喷嘴摄像头。开发了两个CNN模型，将两种相机系统的图像分类为常见的3D打印错误。喷嘴相机模型的验证精度达到了97.7%。侧摄像头模型的验证准确率为97.6%，达到了相当的性能。为了将两个cnn集成到一个统一的系统中，使用了基于逻辑的优先级框架，通过解决相互冲突的预测和利用两种相机类型的互补视角来检测更大范围的缺陷，从而提高了单个模型精度之外的可靠性。数据融合框架识别了12种常见错误，显著提高了错误分类的鲁棒性，无论是在现场还是在实时，推理时间可低至220毫秒。结果表明，鲁棒多输入故障检测系统对提高挤压3D打印的可靠性是可行的。

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引用次数: 0

Advances in Additive Manufacturing for Architectural Acoustics: Design, Materials, Validation, and Implementation Challenges in Building Construction 建筑声学增材制造的进展：建筑施工中的设计、材料、验证和实施挑战

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-02-20 DOI: 10.1016/j.addlet.2026.100368

Taiwo Martins Esan, Williams Kehinde Kupolati, Chris Ackerman

Additive Manufacturing (AM), commonly referred to as 3D printing, has emerged as a transformative technology in the construction sector, enabling unprecedented geometric freedom, automation, and material efficiency. While its adoption for structural applications has accelerated, its integration into building acoustics remains limited and fragmented. This systematic review synthesizes findings from 79 peer-reviewed studies to evaluate the current state of AM for architectural acoustic applications, with emphasis on materials, design strategies, validation methods, and implementation challenges. Four dominant constraint categories are identified: material limitations (≈38%), structural and design constraints (≈19%), modeling and prediction inaccuracies (≈16%), and acoustic performance limitations, particularly narrow operational bandwidths (≈27%). The review highlights the critical role of geometry-driven design, including acoustic metamaterials and micro-perforated panels, enabled by AM processes such as material extrusion, vat photopolymerization, and powder bed fusion. However, persistent challenges related to sustainability, process scalability, surface quality, and limited field-scale validation restrict broader adoption. Emerging solutions, including AI-assisted material formulation, generative acoustic design, and hybrid computational–experimental workflows, are identified as key enablers for progress. The findings provide actionable insights for engineers and designers seeking to deploy AM-based acoustic components in buildings and establish a roadmap for advancing scalable, sustainable, and high-performance architectural acoustics.

增材制造（AM），通常被称为3D打印，已经成为建筑领域的一项变革性技术，实现了前所未有的几何自由度、自动化和材料效率。虽然它在结构应用中的应用已经加速，但它与建筑声学的整合仍然有限和分散。本系统综述综合了79项同行评议的研究结果，以评估AM在建筑声学应用中的现状，重点是材料、设计策略、验证方法和实施挑战。确定了四个主要的约束类别：材料限制（≈38%），结构和设计限制（≈19%），建模和预测不准确性（≈16%），以及声学性能限制，特别是窄操作带宽（≈27%）。该综述强调了几何驱动设计的关键作用，包括声学超材料和微穿孔板，这些都是通过增材制造工艺（如材料挤压、容器光聚合和粉末床融合）实现的。然而，与可持续性、工艺可扩展性、表面质量和有限的现场规模验证相关的持续挑战限制了该技术的广泛应用。新兴的解决方案，包括人工智能辅助材料配方、生成声学设计和混合计算实验工作流程，被认为是取得进展的关键推动因素。研究结果为工程师和设计师在建筑中部署基于am的声学组件提供了可行的见解，并为推进可扩展、可持续和高性能的建筑声学建立了路线图。

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引用次数: 0

COLD: Control and optimization of layer deposition in large-scale additive manufacturing 大规模增材制造中层沉积的控制与优化

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-04-01 Epub Date: 2026-03-03 DOI: 10.1016/j.addlet.2026.100371

Diana Martins , João R. Matos , Diogo Sousa , Fernando Gomes de Almeida

Large-scale thermoplastic 3D printing is an emerging technology that enables cheaper and faster real-scale prototyping and rapid tooling. However, it still presents limitations that can lead to deformations in printed parts due to inadequate cooling conditions during the process. This study proposes COLD: a control and optimization of layer deposition system, based on temperature monitoring, to prevent print failures caused by excessive heat accumulation resulting from insufficient layer cooling time. A thermal camera was attached to a robotic printing system, and, after each layer is deposited, the average temperature of the regions where the next layer will be deposited is evaluated until it reaches a defined threshold. Conical thin wall parts were printed with and without COLD using Polypropylene 30% Glass Fiber, and the results show that it can contribute significantly to the success of large-scale additive manufacturing.

大规模热塑性3D打印是一项新兴技术，可以实现更便宜，更快的实际规模原型和快速模具。然而，它仍然存在局限性，由于在过程中冷却条件不足，可能导致打印部件变形。本研究提出了COLD：一种基于温度监测的层沉积系统的控制和优化，以防止由于层冷却时间不足而导致的热积累过多而导致打印失败。在机器人打印系统上安装了热像仪，在每一层沉积之后，将评估下一层沉积区域的平均温度，直到达到定义的阈值。采用30%聚丙烯玻璃纤维对锥形薄壁件进行了冷打印和不冷打印，结果表明冷打印对大规模增材制造的成功有显著的帮助。

引用次数: 0

Comparing geometry and mechanical performance of as built and Hirtisation® treated Al-Cu-Mg-Ag-Ti-B-Si-Fe rhombic dodecahedron lattices manufactured by laser powder bed fusion 比较激光粉末床熔合制备的Al-Cu-Mg-Ag-Ti-B-Si-Fe菱形十二面体晶格的几何形状和力学性能

IF 4.7 Q2 ENGINEERING, MANUFACTURING

Additive manufacturing letters

Pub Date : 2026-02-01 Epub Date: 2025-12-13 DOI: 10.1016/j.addlet.2025.100350

Ira Papamalama , Emilie Beevers , Berk Baris Celik , Michael Doppler , Selma Hansal , Brecht Van Hooreweder

This study investigates the morphological, geometrical, and mechanical characteristics of Al-Cu-Mg-Ag-Ti-B-Si-Fe aluminum alloy lattice structures fabricated via Laser Powder Bed Fusion (LPBF), with emphasis on quasi-static and fatigue mechanical performance. Using the Gibson–Ashby model as a framework, three relative lattice densities (RLDs) were examined in both as-built (AB) and Hirtisation® surface treated (HIRT) conditions. Results confirm that geometric strut waviness is inherent to the LPBF process, affecting both AB and HIRT samples. However, the Hirtisation® treatment notably reduces surface roughness and dross, enhancing fatigue life and surface uniformity. While strut length remains unchanged after post-treatment, a reduced strut diameter (thickness) alters the final density, directly impacting RLD and strength. Quasi-static tests validate the predicted strength–density relationship, with denser lattices exhibiting higher compressive strength. Fatigue testing reveals combined stretch and bending-dominated response, marked by crush bands and hybrid brittle-ductile failure mode. These findings deepen understanding of LPBF lattice structures and demonstrate the effectiveness of surface treatment in enhancing fatigue resistance and mechanical performance.

本文研究了激光粉末床熔合制备Al-Cu-Mg-Ag-Ti-B-Si-Fe铝合金晶格结构的形态、几何和力学特性，重点研究了准静态和疲劳力学性能。使用Gibson-Ashby模型作为框架，在as-built （AB）和Hirtisation®表面处理（HIRT）条件下检查了三个相对晶格密度（rld）。结果证实几何支撑波浪形是LPBF过程固有的，影响AB和HIRT样品。然而，Hirtisation®处理显著降低了表面粗糙度和杂质，提高了疲劳寿命和表面均匀性。虽然后处理后的支撑长度保持不变，但减少支撑直径（厚度）会改变最终密度，直接影响RLD和强度。准静态试验验证了预测的强度-密度关系，网格密度越大，抗压强度越高。疲劳试验揭示了拉伸和弯曲共同主导的响应，以破碎带和脆性-韧性混合破坏模式为标志。这些发现加深了对LPBF晶格结构的理解，并证明了表面处理在提高抗疲劳性能和力学性能方面的有效性。

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引用次数: 0