Results in Materials最新文献_第7页

Integrating experiments, finite element analysis, and explainable machine learning for natural fiber based hybrid composites 整合实验，有限元分析和可解释的机器学习为天然纤维为基础的混合复合材料

Results in Materials

Pub Date : 2025-12-04 DOI: 10.1016/j.rinma.2025.100835

Md Moinul Hasan Sadik , Akash Baral , Md Sojeeb Ahmed , Md Khalid Al Zuhanee , Md Ashraful Islam , Md Abdul Wakil , Md Abdul Hasib

The reliable prediction of mechanical properties in natural fiber–based composites is crucial for their advancement as sustainable structural materials. The development of natural fiber–based hybrid composites is often hindered by costly, resource-intensive, and time-consuming experimental iterations. While Machine Learning (ML) offers a powerful alternative for accelerating property prediction, standard models often function as 'black boxes,' which limits their trustworthiness and fails to provide the scientific insights needed for true material design. This study addresses this critical gap by developing and validating an integrated framework that combines experiments, Finite Element Analysis, and Explainable Machine learning to predict and interpret mechanical properties of banana fiber reinforced composites. Four supervised machine learning (ML) models—Random Forest, XGBoost, Gradient Boosting, and k-Nearest Neighbor—were developed and trained on a combined literature–experimental dataset. Among them, the XGBoost model exhibited the highest predictive accuracy, achieving R² values of 0.71 for tensile strength and 0.76 for flexural strength in validation. To address the “black box” challenge of ML, SHapley Additive exPlanations (SHAP) analysis was employed, revealing fiber length and filler percentage as the most influential features governing strength predictions. The integration of ML with experimental and numerical validation demonstrates a powerful framework for reducing trial-and-error in composite design, while providing interpretable insights into the role of key material parameters. This work establishes explainable ML as an effective and sustainable tool for optimizing natural fiber–based hybrid composites.

对天然纤维基复合材料力学性能的可靠预测对其作为可持续结构材料的发展至关重要。基于天然纤维的混合复合材料的开发经常受到昂贵、资源密集和耗时的实验迭代的阻碍。虽然机器学习（ML）为加速属性预测提供了强大的替代方案，但标准模型通常起到“黑盒子”的作用，这限制了它们的可信度，并且无法提供真正材料设计所需的科学见解。本研究通过开发和验证结合实验、有限元分析和可解释机器学习的集成框架来预测和解释香蕉纤维增强复合材料的机械性能，从而解决了这一关键空白。四个监督机器学习（ML）模型——随机森林、XGBoost、梯度增强和k-近邻——被开发并在文献-实验数据集上进行训练。其中，XGBoost模型的预测精度最高，在验证中，抗拉强度的R2值为0.71，抗折强度的R2值为0.76。为了解决机器学习的“黑匣子”挑战，采用了SHapley添加剂解释（SHAP）分析，揭示纤维长度和填料百分比是控制强度预测的最具影响力的特征。机器学习与实验和数值验证的集成展示了一个强大的框架，可以减少复合材料设计中的试错，同时为关键材料参数的作用提供可解释的见解。这项工作建立了可解释的ML作为优化天然纤维基混合复合材料的有效和可持续的工具。

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引用次数: 0

Implementation of ZnO-functionalized macroporous cellular geopolymer foams: influence of porosity on radiative and photocatalytic properties zno功能化大孔泡沫地质聚合物的制备：孔隙率对辐射和光催化性能的影响

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100824

S. Benkhirat , E. Ribeiro , S. Eichendorff , E. Beche , K. Nouneh , G. Plantard

This work is part of a context of development of efficient photocatalytic materials for solar photoconversion in order to produce reactive species for degradation. Cellular foams, perfectly suited candidates, are the main subject of this work. Geopolymer foams were developed using two shaping methods, namely a replication method and a direct foaming method using a pore-forming agent. Macroporous cellular foams with a wide range of porous characteristics and porosities ranging from 0.06 to 0.9 were thus developed. The radiative properties were calculated by an experimental approach coupled with modeling. The absorption and diffusion coefficients, which depend on the porous characteristics, vary from 2 to more than 12 m²/kg depending on the porosity of the foams. The absorption capacity is directly correlated to the average porosity of macroporous cellular foams, which promotes the diffusion of radiation within the cellular structure. The photoconversion process via the evaluation of the photocatalytic efficiency was then studied for the different foams. It appears that the photocatalytic efficiency is correlated to the porous characteristics of the foams. The photocatalytic efficiency of geopolymer foams consisting of closed or partially connected pores varies slightly. On the other hand, beyond the porosity threshold of 0.5, the photocatalytic efficiency of reticulated cellular foams consisting of an open porous network increases significantly depending on the porosity. A cellular geopolymer foam with a porosity equal to 0.89 gives an efficiency of 0.7 %. This value is of the order of magnitude of those obtained in the literature for reticulated foams with high porosity. It also appears that catalytic performances are correlated with the radiative properties and in particular with the absorption coefficient of the foams. This original study establishes a correlation between porosity and photocatalytic performance highlighting the importance of porous properties. It seems essential to move towards the shaping of reticulated alveolar foams with high porosity consisting of open pores and millimeter sizes.

这项工作是开发用于太阳能光转化的高效光催化材料的一部分，目的是产生用于降解的活性物质。细胞泡沫，非常适合的候选者，是这项工作的主要主题。采用两种成型方法开发地聚合物泡沫，即复制法和使用成孔剂的直接发泡法。大孔泡沫具有广泛的孔隙特性，孔隙度在0.06 ~ 0.9之间。采用实验方法与模型相结合的方法计算了辐射特性。吸收和扩散系数取决于多孔特性，根据泡沫的孔隙率从2到大于12m2 /kg不等。吸收能力与大孔泡沫的平均孔隙率直接相关，促进了辐射在细胞结构内的扩散。通过光催化效率的评价，研究了不同泡沫的光转化过程。结果表明，光催化效率与泡沫材料的多孔性有关。由封闭或部分连接的孔隙组成的地聚合物泡沫的光催化效率略有不同。另一方面，超过孔隙率阈值0.5时，由开放多孔网络组成的网状泡沫的光催化效率随孔隙率的增加而显著增加。孔隙率为0.89的多孔性地聚合物泡沫的效率为0.7%。该值与文献中获得的高孔隙率网状泡沫的数量级相当。研究还表明，泡沫的催化性能与泡沫的辐射特性，特别是吸收系数有关。这项原始研究建立了孔隙度和光催化性能之间的相关性，突出了多孔性的重要性。似乎有必要朝着网状肺泡泡沫的形成方向发展，这些泡沫具有高孔隙率，由开放的孔和毫米大小组成。

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引用次数: 0

Characterization and effects of process parameters on the mechanical, microstructural, and thermal behavior of PEEK in fused deposited modeling 3D printing 表征及工艺参数对熔融沉积建模3D打印PEEK的力学、显微组织和热行为的影响

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100833

Shimelis Tamene Gobena , Abraham Debebe Woldeyohannes

Polyether ether ketone (PEEK) is a high-performance thermoplastic polymer known for its exceptional mechanical strength, high thermal stability, and chemical resistance, making it suitable for aerospace, automotive, and biomedical applications. The aim of this study is to characterize and analyze the effects of process parameters on 3D Printed PEEK, using numerical modeling and experimental investigation. Finite element analysis (FEA) using ANSYS Workbench was applied to investigate molten PEEK flow through the nozzle and assess the influence of critical process parameters including printing temperature (380 °C, 400 °C, and 420 °C), nozzle diameter (Ø0.4 mm), layer thicknesses (0.2 mm, 0.25 mm, and 0.3 mm), and filament feeding speeds (20 mm/s, 25 mm/s, and 30 mm/s). The simulation considered relevant boundary conditions and thermal-fluid material properties to replicate realistic flow behavior. The results demonstrated that nozzle diameter significantly affected pressure drop, while printing temperature and feeding speed influenced viscosity and temperature distribution within the nozzle. A smaller nozzle diameter increased pressure drop, while higher temperatures and feeding speeds improved molten PEEK flow behavior, positively impacting the quality of printed parts. Phase transformations during extrusion were also observed, from solid to vitreous and then to liquid as the temperature increased. These transitions were found to influence the bonding quality and uniformity of extruded layers. Experimental validation was performed on 3D-printed PEEK samples using the same set of process parameters.

The simulation modeling was employed to select critical printing parameters and understand the flow behavior of PEEK prior to actual FDM printing. Mechanical testing showed that impact strength improved with increasing printing temperature, ranging from 44 kJ/m² at 380 °C to 50 kJ/m² at 420 °C, while achieving a UTS of 120 MPa and modulus of 4.6 GPa. Interaction effects indicated that lower printing speeds (20 mm/s) enhanced interlayer adhesion through prolonged thermal contact, especially with moderate layer thicknesses of 0.2–0.25 mm.

Unlike previous studies, this work also investigated multiple properties, Mechanical properties, physical properties, including porosity and density, alongside thermal stability, crystallinity, and microstructure. The results demonstrate that careful optimization of printing parameters significantly improves the overall performance of 3D-printed PEEK. These findings provide comprehensive guidance for producing high-quality, mechanically robust PEEK components.

聚醚醚酮（PEEK）是一种高性能热塑性聚合物，以其卓越的机械强度，高热稳定性和耐化学性而闻名，适用于航空航天，汽车和生物医学应用。本研究的目的是通过数值模拟和实验研究来表征和分析工艺参数对3D打印PEEK的影响。利用ANSYS Workbench对熔融PEEK在喷嘴中的流动进行了有限元分析，并评估了打印温度（380°C、400°C和420°C）、喷嘴直径（Ø0.4 mm）、层厚（0.2 mm、0.25 mm和0.3 mm）和进丝速度（20 mm/s、25 mm/s和30 mm/s）等关键工艺参数的影响。模拟考虑了相关的边界条件和热流体材料性质，以复制真实的流动行为。结果表明，喷嘴直径对压降有显著影响，打印温度和给料速度对喷嘴内粘度和温度分布有显著影响。较小的喷嘴直径增加了压降，而较高的温度和进料速度改善了熔融PEEK的流动行为，对打印部件的质量产生了积极的影响。在挤压过程中也观察到相变，随着温度的升高，从固体到玻璃状，然后到液体。发现这些转变影响了挤压层的结合质量和均匀性。使用相同的工艺参数对3d打印的PEEK样品进行实验验证。利用仿真模型选择关键打印参数，了解PEEK在实际FDM打印前的流动特性。力学测试表明，随着打印温度的升高，材料的冲击强度有所提高，从380℃时的44 kJ/m2增加到420℃时的50 kJ/m2，打印时的UTS为120 MPa，模量为4.6 GPa。相互作用效应表明，较低的印刷速度（20mm /s）通过长时间的热接触增强了层间的附着力，特别是在0.2-0.25 mm的中等层厚中。与之前的研究不同，这项工作还研究了多种性能，机械性能，物理性能，包括孔隙率和密度，以及热稳定性，结晶度和微观结构。结果表明，仔细优化打印参数可以显著提高3d打印PEEK的整体性能。这些发现为生产高质量，机械坚固的PEEK组件提供了全面的指导。

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引用次数: 0

Aims and Scope 目标及范围

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/S2590-048X(25)00197-9

引用次数: 0

TPS and natural composites elements, techniques, challenges, and applications TPS和天然复合材料的元素、技术、挑战和应用

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100816

Yosra M. Atteya, Mohamed F. Aly, Mahmoud M. Farag

Thermoplastic starch (TPS) are natural composites that have shown in the recent decay a promising outcomes as a natural material friend to the environment, that could replace the synthetic petroleum derived plastics. TPS materials have a varied ranges of mechanical and physical characteristics that differs with the difference of the used components, and with the preparation method. Also, TPS composites have a variable textures and structural features, that is formed according to the incorporated fillers and reinforcements, whether they are cellulosic fibers or resins, renewable or nonrenewable, metallic or organic, synthetic or natural. This review aims to comprise and categories the parameters that influence the TPS chemical, physical and mechanical characteristics, and to study how the fillers (reinforcements) type, size, treatment procedures and preparation methodology affect the composite features, characteristics and applications.

热塑性淀粉（TPS）是一种天然复合材料，作为一种对环境无害的天然材料，有望取代合成的石油衍生塑料。TPS材料具有不同范围的机械和物理特性，这些特性随所用组分的不同和制备方法的不同而不同。此外，TPS复合材料具有可变的纹理和结构特征，这是根据所加入的填料和增强剂形成的，无论它们是纤维素纤维或树脂，可再生或不可再生，金属或有机，合成或天然。本文旨在对影响TPS化学、物理和机械特性的参数进行归纳和分类，并研究填料（增强剂）的类型、尺寸、处理程序和制备方法如何影响复合材料的特性、特性和应用。

引用次数: 0

Deposition of magnetite nano-particles on gray cast iron via hot alkaline and hydrothermal conversion methods: with regarding corrosion behaviour 通过热碱性和水热转化方法在灰铸铁上沉积磁铁矿纳米颗粒：与腐蚀行为有关

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100828

Arezou Abyazi, Faezeh Fakheri

In this study, the formation of Fe₃O₄ nanoparticle coatings on gray cast iron was investigated. Two blackening methods, including a hot alkaline (open system) and a hydrothermal method (closed system), were employed. The hot alkaline method faced challenges due to the presence of near-surface graphite, leading to the formation of a porous and defective coating containing significant amounts of hematite (Fe₂O₃), as confirmed by thermodynamic calculations. In contrast, a dense and uniform high-quality Fe₃O₄ coating was produced by the hydrothermal method, and the issues related to surface graphite were effectively resolved by converting it into carbon-based nanomaterials inside the autoclave. Quantitative comparison showed that the hot alkaline coated specimens had i_corr = (2.11 ± 0.04) × 10⁻⁶ A cm⁻², bond strength = 1.34 ± 0.04 MPa, and R_a ≈ 1.42 ± 0.36 μm, whereas the hydrothermally treated samples exhibited i_corr = (1.54 ± 0.03) × 10⁻⁶ A cm⁻², bond strength = 2.56 ± 0.03 MPa, and R_a ≈ 40.36 ± 1.2 nm, indicating markedly improved corrosion resistance, adhesion, and surface quality. The resulting coatings were characterized using XRD, Field Emission Scanning Electron Microscopy (FE-SEM), Atomic Force Microscopy (AFM), Pull-off adhesion test, and corrosion tests, including Open Circuit Potential (OCP), potentiodynamic polarization, and Electrochemical Impedance Spectroscopy (EIS).

本文研究了灰口铸铁表面纳米Fe3O4涂层的形成。采用热碱性（开放体系）和水热法（封闭体系）两种发黑方法。热力学计算证实，由于近表面石墨的存在，热碱性方法面临挑战，导致形成含有大量赤铁矿（Fe2O3）的多孔和有缺陷的涂层。相比之下，水热法制备了致密均匀的高质量Fe3O4涂层，通过在热压釜内将其转化为碳基纳米材料，有效地解决了表面石墨的相关问题。定量比较表明，热碱性涂层样品的icorr =（2.11±0.04）× 10−6 A cm−2，结合强度= 1.34±0.04 MPa, Ra≈1.42±0.36 μm，而水热处理样品的icorr =（1.54±0.03）× 10−6 A cm−2，结合强度= 2.56±0.03 MPa, Ra≈40.36±1.2 nm，耐蚀性、粘附性和表面质量均有显著提高。采用XRD、场发射扫描电镜（FE-SEM）、原子力显微镜（AFM）、拉脱附着力测试和腐蚀测试（包括开路电位（OCP）、动电位极化和电化学阻抗谱（EIS））对涂层进行了表征。

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引用次数: 0

Innovative approach to SFRC analysis: Using gene expression programming (GEP) to predict stress-strain behavior of steel fiber-reinforced concrete under compression SFRC分析的创新方法：使用基因表达程序（GEP）预测钢纤维增强混凝土在压缩下的应力-应变行为

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100829

Husam Al Qablan

The stress–strain diagram of SFRC is a crucial tool for evaluating its mechanical properties, as it clearly illustrates the enhanced strength, ductility, and energy absorption provided by the steel fibers. The present research presents a novel method for developing a predictive model for predicting the compression stress-strain curve of steel fiber reinforced concrete (SFRC), offering a reliable tool to capture its complex mechanical response. The Generalized Reduced Gradient Method (GRG) and Gene Expression Programming (GEP) were employed to develop a formula that incorporates the main influencing factors, including steel fiber volume percentage, yield stress, reinforcing index, and concrete compressive strength and strain. These characteristics were carefully investigated to develop a powerful formula capable of accurately predicting the stress-strain diagram of SFRC. The model is based on a large dataset of 182 experimental samples used for both development and validation. The models' accuracy was assessed through the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (

R^{2}

) by comparing actual and predicted values. The model's predictions correspond well with experimental data, demonstrating its robustness and reliability in predicting the stress-strain curves. This study sheds light on the intricate interaction of material characteristics in SFRC. It provides a powerful tool for engineers and researchers seeking to enhance the mechanical performance of fiber-reinforced concrete structures.

SFRC的应力应变图是评估其力学性能的重要工具，因为它清楚地说明了钢纤维提供的增强的强度，延展性和能量吸收。本研究提出了一种新的方法来建立预测钢纤维混凝土（SFRC）压缩应力-应变曲线的预测模型，为捕获其复杂的力学响应提供了可靠的工具。采用广义降阶梯度法（GRG）和基因表达式编程（GEP）建立了综合钢纤维体积率、屈服应力、配筋指标、混凝土抗压强度和应变等主要影响因素的计算公式。对这些特性进行了仔细的研究，以建立一个能够准确预测SFRC应力-应变图的强大公式。该模型基于182个实验样本的大型数据集，用于开发和验证。通过比较实际值和预测值，通过均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）来评估模型的准确性。该模型的预测结果与实验数据吻合较好，证明了该模型预测应力-应变曲线的鲁棒性和可靠性。该研究揭示了SFRC材料特性之间复杂的相互作用。它为工程师和研究人员寻求提高纤维增强混凝土结构的力学性能提供了有力的工具。

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引用次数: 0

Effect of pore structure on void formation in lotus type porous Cu/solder joints 孔隙结构对荷叶型多孔铜/焊点孔隙形成的影响

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100827

Jin-Kwan Lee , Keun-Soo Kim , Jae-Ho Shin , Seung-Min Cho , Sung Yi , Sang-Wook Kim , Soong-Keun Hyun

Void formation in solder joints is a critical reliability challenge in high-power electronics, as it degrades thermal dissipation and mechanical integrity. This study investigates a novel structural approach to mitigate this issue. The influence of an uni-directional porous structure in lotus-type porous Cu (Louts Cu) on void formation in solder joints was investigated. All pores in lotus Cu were infiltrated with SAC305 (Sn–3.0Ag–0.5Cu) solder paste. Then reflow soldering was performed under three different atmospheric conditions: air, nitrogen, and vacuum. The microstructure and void fraction were characterized. The shear strength was evaluated. The shear strength of Louts Cu joint was slightly higher than that of non-porous Cu joint. The characterization of void fraction in joints exhibited that lotus Cu joint is lower than that of non-porous Cu joint.

在大功率电子设备中，焊点上的空洞形成是一个关键的可靠性挑战，因为它会降低散热和机械完整性。本研究探讨了一种新的结构方法来缓解这一问题。研究了荷型多孔铜（Louts Cu）中单向多孔结构对焊点空穴形成的影响。用SAC305 （Sn-3.0Ag-0.5Cu）钎料膏浸润荷花铜的所有孔隙。然后在三种不同的大气条件下进行回流焊：空气、氮气和真空。对其微观结构和孔隙率进行了表征。对其抗剪强度进行了评价。Louts铜接头的抗剪强度略高于无孔铜接头。节理孔隙率表征表明，莲花铜节理的孔隙率低于无孔铜节理。

引用次数: 0

Europium-doped silica nanoparticles for efficient and sustainable lead removal from aqueous solutions 铕掺杂二氧化硅纳米颗粒用于有效和可持续地从水溶液中去除铅

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100826

Hasan Shamseddine , Nour Abi Aad , Ghassan Younes , Rami Al-Oweini

This study reports the synthesis and characterization of Europium-doped silica nanoparticles (adsorbent) (S₁), prepared via an eco-friendly sol-gel method, aimed at the efficient removal of lead (Pb²⁺) ions from aqueous solutions. Comprehensive structural and surface analyses were performed using Scanning Electron Microscopy (SEM), Dynamic Light Scattering (DLS), Fourier-transform Infrared Spectroscopy (FTIR), and Thermogravimetric Analysis (TGA) to confirm the formation and stability of the nanoparticles.

The maximum adsorption capacity of S₁ for Pb²⁺ ions was achieved at a solid-to-solution ratio of 4 g/L. Equilibrium was reached within 60 min. The adsorption kinetics followed a pseudo-second-order model, indicating that chemisorption played a significant role in the extraction process. Furthermore, the adsorption isotherm data fitted well with the Langmuir model, yielding a maximum adsorption capacity of 25.73 mg/g at 298.15 K. This suggests that adsorption predominantly occurs at specific active sites on the nanoparticle surface, involving interactions between Pb (II) ions and oxygen atoms present on the surface.

Thermodynamic studies revealed that the adsorption process is spontaneous. The enthalpy data indicated that the interaction involves both electrostatic and chemical contributions. A 90 % recovery of the used S₁ material was achieved using 20 mM HCl under the following conditions: 2-h contact time, temperature of 298 K, shaking rate of 180 rpm, and an adsorbent dose of 10 mg/L. The material maintained its efficiency over five adsorption-desorption cycles.

These results demonstrate the potential of europium-doped silica nanoparticles as effective and reusable adsorbents for sustainable heavy metal remediation, contributing to advancements in colloid and interface science for environmental applications.

本研究报道了通过环保溶胶-凝胶法制备的铕掺杂二氧化硅纳米颗粒（吸附剂）（S1）的合成和表征，旨在有效去除水溶液中的铅（Pb2+）离子。利用扫描电镜（SEM）、动态光散射（DLS）、傅里叶变换红外光谱（FTIR）和热重分析（TGA）对纳米颗粒进行了全面的结构和表面分析，以确认纳米颗粒的形成和稳定性。当固液比为4 g/L时，S1对Pb2+离子的吸附量最大。60分钟内达到平衡。吸附动力学服从准二级模型，表明化学吸附在萃取过程中起重要作用。吸附等温线数据与Langmuir模型拟合良好，在298.15 K下吸附量最大为25.73 mg/g。这表明吸附主要发生在纳米颗粒表面的特定活性位点，涉及表面Pb （II）离子与氧原子之间的相互作用。热力学研究表明，吸附过程是自发的。焓数据表明，相互作用包括静电和化学贡献。在接触时间2 h、温度298 K、振荡速度180转/分、吸附剂剂量10 mg/L的条件下，用20 mM的HCl可使S1废料回收率达到90%。该材料在五次吸附-解吸循环中保持了其效率。这些结果证明了铕掺杂二氧化硅纳米颗粒作为可持续重金属修复的有效和可重复使用的吸附剂的潜力，促进了胶体和界面科学在环境应用中的进步。

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引用次数: 0

Bottom-up optimization of additive manufacturing: Process–structure–property relationships in laser powder bed fusion 增材制造的自下而上优化：激光粉末床熔合的工艺结构-性能关系

Results in Materials

Pub Date : 2025-12-01 DOI: 10.1016/j.rinma.2025.100832

Naol Dessalegn Dejene , Dame Alemayehu Efa

In additive manufacturing (AM), especially Laser (Powder Bed Fusion (L-PBF), having excellent and consistent mechanical properties is important for high-value applications in aerospace, automobile industries, and biomedical engineering. This study employs a novel, complementary bottom-up approach to systematically investigate how scanning strategies (spiral, island, and bi-directional) and part orientations influence the microstructural and mechanical properties of L-PBF components. Microstructural characterization, using optical microscopy, scanning electron microscopy (SEM), and electron backscatter diffraction (EBSD), allowed the evaluation of grain size, porosity, and crystallographic texture. Mechanical properties and their relationship with the microstructural features of tensile strength, yield strength, elastic modulus, strain percentage, and hardness were evaluated. Ultimately, the results demonstrate that both scanning strategies and part orientations have a considerable effect on the mechanical properties of key features such as strength, ductility, and anisotropy. Specifically, island scanning showed the best ductility while spiral scanning demonstrated better strength at the cost of greater brittleness. Additionally, this study demonstrated that controlling scanning strategies and part orientation offers valuable insights for optimizing the L-PBF process, with significant implications for the design and fabrication of high-performance components in demanding industrial applications.

在增材制造（AM）中，特别是激光（粉末床融合（L-PBF）），具有优异和一致的机械性能对于航空航天，汽车工业和生物医学工程中的高价值应用非常重要。本研究采用了一种新颖的、互补的自下而上的方法，系统地研究了扫描策略（螺旋、岛状和双向）和零件取向如何影响L-PBF组件的微观结构和力学性能。显微结构表征，使用光学显微镜，扫描电子显微镜（SEM）和电子背散射衍射（EBSD），允许评估晶粒尺寸，孔隙率和晶体结构。评估了拉伸强度、屈服强度、弹性模量、应变率和硬度等力学性能及其与微观组织特征的关系。最终，结果表明，扫描策略和零件取向对强度、延展性和各向异性等关键特征的力学性能都有相当大的影响。其中，岛状扫描表现出最好的延展性，螺旋扫描表现出更好的强度，但脆性较大。此外，该研究表明，控制扫描策略和零件方向为优化L-PBF工艺提供了有价值的见解，对要求苛刻的工业应用中高性能部件的设计和制造具有重要意义。

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引用次数: 0