Materials & Design最新文献_第3页

Productive automation of calibration processes for crystal plasticity model parameters via reinforcement learning 通过强化学习实现晶体塑性模型参数校准过程的生产自动化

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113470

Jonghwan Lee , Burcu Tasdemir , Suchandrima Das , Michael Martin , David Knowles , Mahmoud Mostafavi

Crystal Plasticity Finite Element (CPFE), which merges crystal plasticity principles with finite element analysis, can simulate the anisotropic grain-level mechanical behaviour of polycrystalline materials. Due to the benefit of CPFE, it has been widely utilised to analyse processes such as manufacturing, damage, and deformation where the microstructure plays a prominent role. However, this method is computationally expensive and requires the robust calibration of its parameters, which can be many. In this work, we propose a framework to address difficulties in calibrating multi-parameter CPFE. The Deep Deterministic Policy Gradient (DDPG) algorithm, a Deep Reinforcement Learning (DRL) approach, is utilised to optimise the CPFE parameters. Additionally, a Python-based environment is developed to fully automate the calibration process. To allow comparison with the conventional optimisation method, the Particle Swarm Optimisation (PSO) algorithm is also used, which shows the DDPG framework yields more accurate calibration. The generalisation performance of the proposed framework is also demonstrated by calibrating each parameter set of two different CPFE models for monotonic loading of the stainless steel type, 316L. Moreover, the effectiveness of the framework in the more complex condition is also demonstrated by calibrating the CPFE parameters for a two-cycle cyclic behaviour of a 316H stainless steel material. The reliability of these calibrated parameters is also validated in the cyclic simulation after two cycles.

晶体塑性有限元（CPFE）将晶体塑性原理与有限元分析相结合，可以模拟多晶材料各向异性的晶粒级机械行为。由于 CPFE 的优点，它已被广泛用于分析制造、损坏和变形等过程，其中微观结构起着重要作用。然而，这种方法计算成本高昂，需要对其参数进行稳健校准，而校准的参数可能很多。在这项工作中，我们提出了一个框架，以解决多参数 CPFE 校准方面的困难。深度确定性策略梯度（DDPG）算法是一种深度强化学习（DRL）方法，可用于优化 CPFE 参数。此外，还开发了一个基于 Python 的环境，以实现校准过程的完全自动化。为了与传统的优化方法进行比较，还使用了粒子群优化（PSO）算法，结果表明 DDPG 框架能产生更精确的校准。通过校准两种不同 CPFE 模型的每个参数集，对 316L 不锈钢类型的单调加载进行校准，也证明了所提框架的通用性能。此外，通过校准 316H 不锈钢材料两周期循环行为的 CPFE 参数，也证明了该框架在更复杂条件下的有效性。这些校准参数的可靠性也在两个周期后的循环模拟中得到了验证。

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引用次数: 0

Microfluidic wet spinning of soft polydimethylsiloxane polymer optical fibers 微流体湿法纺制聚二甲基硅氧烷软聚合物光纤

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113466

Khushdeep Sharma , Wuchao Wang , Sebastian Valet , Tina Künniger , Michał Góra , Kongchang Wei , Bernhard Weisse , Lucas Bahin , René M. Rossi , Fabien Sorin , Luciano F. Boesel

Polymer optical fibers (POFs) are an essential component of photonic textile sensors for the development of healthcare@home technologies. However, fabricating tailored POFs exhibiting the required properties for such applications remains challenging. Here, an innovative method to fabricate soft POFs based on polydimethylsiloxane is introduced: the hydrogel-assisted microfluidic wet spinning (HA-MWS) technology. Combined with a straightforward post-processing step, the HA-MWS enabled the production of soft POFs with tailored moduli (0.35 MPa to 5.0 MPa), low surface roughness (

R_{q} <

6 nm), and, consequently, low attenuation (<0.25 dB/cm). The quasi-static and dynamic mechanical, chemical, optical, thermal, and surface properties of the soft POFs produced by HA-MWS were investigated in detail. A photonic textile sensor demonstrator was built with these soft POFs with tailored pressure sensitivity in the range of 2–300 kPa, modulus close to that of skin, and high-temperature stability between -30

{}^{\circ}C

and 90

{}^{\circ}C

. This methodology can potentially become a standard tool for designing POFs with tunable properties for healthcare monitoring, soft robotics, and biomedicine applications.

聚合物光纤（POF）是光子纺织传感器的重要组成部分，可用于开发 "医疗保健@家庭 "技术。然而，制造具有此类应用所需特性的定制 POF 仍具有挑战性。本文介绍了一种基于聚二甲基硅氧烷制造软 POF 的创新方法：水凝胶辅助微流体湿法纺丝（HA-MWS）技术。结合简单的后处理步骤，HA-MWS 能够生产出具有定制模量（0.35 MPa 至 5.0 MPa）、低表面粗糙度（Rq< 6 nm）的软 POF，从而实现低衰减（<0.25 dB/cm）。对 HA-MWS 制成的软 POF 的准静态和动态机械、化学、光学、热学和表面特性进行了详细研究。利用这些软 POF 制作的光子纺织品传感器演示器具有量身定制的 2-300 kPa 压力灵敏度、接近皮肤的模量以及 -30 C∘ 和 90 C∘ 之间的高温稳定性。这种方法有可能成为设计具有可调特性的 POF 的标准工具，用于医疗保健监测、软机器人和生物医学应用。

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引用次数: 0

Machine learning for structure-guided materials and process design 用于结构引导材料和工艺设计的机器学习

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113453

Lukas Morand , Tarek Iraki , Johannes Dornheim , Stefan Sandfeld , Norbert Link , Dirk Helm

In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic and generic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material microstructures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these microstructures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target microstructures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable microstructure. The functionality of the approach is demonstrated at manufacturing crystallographic textures with desired properties in a simulated metal forming process.

近年来，人们越来越关注在工艺-结构-性能链的背景下加速材料创新。在这方面，必须考虑到制造工艺，并量身定制材料设计方法，以支持下游工艺设计方法。作为朝这一方向迈出的重要一步，我们提出了一种涵盖材料工程中整个工艺-结构-性能链的整体通用优化方法。我们的方法特别采用机器学习来解决两个关键的识别问题：一个是材料设计问题，包括识别出表现出所需性能的近乎最佳的材料微结构；另一个是工艺设计问题，即找到制造这些微结构的最佳加工路径。这两个识别问题都是典型的 "假问题"，这对求解方法提出了巨大挑战。然而，这些问题的非唯一性为加工提供了重要优势：有了几种性能相似的目标微结构，就能有效地指导加工过程，制造出最佳的微结构。在模拟金属成型工艺中制造具有所需性能的晶体纹理时，演示了该方法的功能。

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引用次数: 0

Anomaly detection by X-ray tomography and probabilistic fatigue assessment of aluminum brackets manufactured by PBF-LB 通过 X 射线断层扫描和概率疲劳评估对 PBF-LB 制造的铝支架进行异常检测

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-19 DOI: 10.1016/j.matdes.2024.113467

L. Rusnati , M. Yosifov , S. Senck , R. Hubmann , S. Beretta

The assessment of safety-critical components for fatigue applications is a key requirement for metal additive manufacturing (AM) applications. Material anomalies play a relevant role in determining the fatigue resistance properties of a component. X-ray computed tomography (CT) helps collect important information on these flaws, such as their size and position within a part.

In this study, we discuss how to employ anomaly data detected on an AlSi10Mg bracket manufactured by laser-powder bed fusion to describe the prospective allowable life of a component under a given operating condition.

A statistical analysis was conducted on the specimens and component to derive the correlation between different resolution scans and analyze the uncertainties of the micro-CT measurements. The full-scale non-destructive evaluation (NDE) can be constrained to large voxel sizes. Eventually, the authors proposed a fully probabilistic route for assessment instead of a simple deterministic assessment based on safety factors. This assessment enables designers to consider the uncertainties of the assessment (uncertainties of micro-CT detection and the model for fatigue strength).

对安全关键部件进行疲劳应用评估是金属增材制造（AM）应用的一项关键要求。材料异常在确定部件的抗疲劳性能方面发挥着重要作用。在本研究中，我们讨论了如何利用激光粉末床融合技术制造的 AlSi10Mg 支架上检测到的异常数据来描述给定工作条件下部件的预期允许寿命。我们对试样和部件进行了统计分析，以得出不同分辨率扫描之间的相关性，并分析微型 CT 测量的不确定性。全尺寸无损评价（NDE）可能受限于较大的体素尺寸。最终，作者提出了一种完全概率化的评估方法，而不是基于安全系数的简单确定性评估。这种评估方法使设计人员能够考虑评估的不确定性（微型 CT 检测和疲劳强度模型的不确定性）。

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引用次数: 0

Achieving excellent strength and plasticity of aluminum alloy through refining and densifying precipitates 通过细化和致密化沉淀实现铝合金的优异强度和塑性

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113439

Renjie Dai , Zhenjun Zhang , Keqiang Li , Rui Liu , Jiapeng Hou , Zhan Qu , Baishan Gong , Zhefeng Zhang

Strength and plasticity are basic mechanical properties for wrought Al alloys, and generally exhibit a trade-off relationship. Herein through analyzing the respective effects of precipitates on yield strength and strain hardening, we proposed and quantitatively analyzed a strategy for synchronously strengthening and plasticizing Al alloys by refining and densifying the precipitates, defined as RDP effect. The precipitates were highly refined and densified in three high-Zn 7xxx alloys in order to verify the validity of the RDP effect. The tensile tests show that the high-Zn Al alloys possess ultra-high strength and good plasticity compared to the traditional Al alloys. Further analysis reveals that the densification of precipitates mainly contributes to the ultra-high strength, accounting for over 75%, while the refinement of precipitates suppresses the dislocation annihilation, thus increasing the strain-hardening capacity. Together, these two factors finally contribute to the excellent strength and plasticity matching. This finding will provide strong support for the positive impact of RDP effect on improving the balance between strength and ductility. Besides, this strategy may be considered as an effective one for simultaneously improving the strength and plasticity in high-performance Al alloys.

强度和塑性是锻造铝合金的基本机械性能，通常呈现出一种权衡关系。在此，我们通过分析析出物对屈服强度和应变硬化的各自影响，提出并定量分析了通过细化和致密化析出物实现铝合金同步强化和塑化的策略，即 RDP 效应。为了验证 RDP 效应的有效性，我们对三种高锌 7xxx 合金中的析出物进行了高度细化和致密化处理。拉伸试验表明，与传统的铝合金相比，高锌铝合金具有超高强度和良好的塑性。进一步的分析表明，析出物的致密化是超高强度的主要原因，占 75% 以上，而析出物的细化抑制了位错湮灭，从而提高了应变硬化能力。这两个因素共同作用，最终实现了优异的强度和塑性匹配。这一发现将有力地支持 RDP 效应对改善强度和延展性之间平衡的积极影响。此外，这种策略可被视为同时提高高性能铝合金强度和塑性的有效方法。

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引用次数: 0

Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc 机器学习辅助设计和制备具有高铋和低 Hc 的 Fe85Si2B8.5P3.5C1 非晶/纳米晶合金

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113461

Shengdong Tang , Rui Sun , Yifan He , Guichang Liu , Ruixuan Wang , Yuqin Liu , Chengying Tang

Four machine learning (ML) models including eXtreme Gradient boosting (XGBT), k-Nearest Neighbor (kNN), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN) were employed to predict saturation flux density (B_s), coercivity (H_c), grain size, magnetostriction (λ), and Curie temperature (T_c) of Fe-based amorphous/nanocrystalline alloys. To maximize predictive ability of ML models, grid-search and normalization were used to search the most proper parameters of ML and pre-process raw data, respectively. XGBT had best predictive and generalization ability for predicting B_s and H_c with coefficient of determination (R²) of 0.992 and 0.967, respectively. Based on the feature importance analysis from the XGBT model, the Fe₈₅Si₂B_8.5P_3.5C₁ amorphous alloy ribbon with good magnetic properties, such as high B_s, low H_c, was designed and prepared by melt spinning. X-ray diffraction (XRD), differential scanning calorimetry (DSC), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), B–H loop tracer, and magnetostriction instrument were used to identify the phase structure and physical properties of the Fe₈₅Si₂B_8.5P_3.5C₁ alloy. It was found that the Fe₈₅Si₂B_8.5P_3.5C₁ alloy had good magnetic properties with B_s of 1.82 T and the H_c of 2.02 A/m after annealing at 723 K for 180 s, in good agreement with the designed results by machine learning.

采用了四种机器学习（ML）模型，包括梯度提升（XGBT）、k-近邻（kNN）、梯度提升决策树（GBDT）和人工神经网络（ANN），预测铁基非晶/纳米晶合金的饱和磁通密度（Bs）、矫顽力（Hc）、晶粒尺寸、磁致伸缩性（λ）和居里温度（Tc）。为了最大限度地提高 ML 模型的预测能力，分别采用了网格搜索和归一化方法来搜索最合适的 ML 参数和预处理原始数据。XGBT 在预测 Bs 和 Hc 方面具有最佳的预测能力和概括能力，其判定系数 (R2) 分别为 0.992 和 0.967。根据 XGBT 模型的特征重要性分析，设计并通过熔融纺丝制备了具有高 Bs、低 Hc 等良好磁性能的 Fe85Si2B8.5P3.5C1 非晶合金带。利用 X 射线衍射 (XRD)、差示扫描量热 (DSC)、透射电子显微镜 (TEM)、振动样品磁力计 (VSM)、B-H 回路示踪仪和磁致伸缩仪鉴定了 Fe85Si2B8.5P3.5C1 合金的相结构和物理性质。结果发现，Fe85Si2B8.5P3.5C1 合金具有良好的磁性能，在 723 K 退火 180 秒后，Bs 为 1.82 T，Hc 为 2.02 A/m，与机器学习的设计结果非常吻合。

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引用次数: 0

Phase-field modeling and computational design of structurally stable NMC materials 结构稳定的 NMC 材料的相场建模和计算设计

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113464

Eduardo Roque , Javier Segurado , Francisco Montero-Chacón

Lithium Nickel Manganese Cobalt Oxides (NMC) are one of the most used cathode materials in lithium-ion batteries, and they will become more relevant in the following years due to their potential in electric vehicles. Unfortunately, this material experiences microcracking during the battery operation due to the volume variations, which is detrimental to the battery performance and limits the lifetime of the electrodes. Thus, understanding mechanical degradation is fundamental for the development of advanced batteries with improved capacity and limited degradation. In this work, we propose a chemo-mechanical model, including a stochastic phase-field fracture approach, to design structurally stable NMC electrodes. We include the degradation in the mechanical and chemical contributions. The heterogeneous NMC microstructure is considered by representing the material's tensile strength with a Weibull distribution function, which allows to represent complex and non-deterministic crack patterns.

We use our model to provide a comprehensive analysis of mechanical degradation in NMC111 electrodes, including the effect of particle size, C-rate, and depth of charge and discharge. Then, we analyze the influence of the electrode composition (namely, Ni content) on the structural integrity. We use this information to provide design guides for functionally-graded electrodes with high capacity and limited degradation.

镍锰钴锂氧化物（NMC）是锂离子电池中使用最多的正极材料之一，由于其在电动汽车中的应用潜力，在未来几年中将变得更加重要。遗憾的是，这种材料在电池运行过程中会因体积变化而产生微裂纹，从而影响电池性能并限制电极的使用寿命。因此，了解机械降解是开发具有更高容量和有限降解的先进电池的基础。在这项工作中，我们提出了一种化学机械模型，包括随机相场断裂方法，用于设计结构稳定的 NMC 电极。我们将降解包括在机械和化学贡献中。我们利用模型对 NMC111 电极的机械降解进行了全面分析，包括粒度、C 率以及充放电深度的影响。然后，我们分析了电极成分（即镍含量）对结构完整性的影响。我们利用这些信息为具有高容量和有限降解的功能分级电极提供设计指导。

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引用次数: 0

Metasurface-Assisted mutual coupling suppression in circularly polarized MIMO antenna array for Sub-6 GHz applications 用于 6 千兆赫以下应用的圆极化多输入多输出天线阵列中的元表面辅助相互耦合抑制技术

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-17 DOI: 10.1016/j.matdes.2024.113445

Muhammad Usman Raza , Kai Zhang , Sen Yan

A double-sided decoupling metasurface (DSDM) method is proposed to reduce the mutual coupling between very closely spaced circularly polarized (CP) MIMO antenna elements for sub-6 GHz applications. A proposed DSDM structure with a square-shaped patch layer is placed over the array to reduce mutual coupling by non-propagating evanescent waves and manipulating the polarization of propagating reflected CP waves. This decoupling mechanism relies on the DSDM’s negative electric permittivity extracted from the meta-atom. When the CP waves were incident to DSDM polarizer through the excited CP antenna of the array, the polarization states of the reflected and transmitted CP waves were changed as controlled by DSDM. In reflection mode, the negative permittivity of DSDM produce two type of the waves reflected waves generated the polarization mismatch and evanescent waves that reduce the coupling between CP antennas, while the transmission mode, controlling the radiation pattern at φ = 45° or φ = 135°. The low-profile proposed decoupling structure was fabricated and experimentally validated. The decoupling design significantly mitigated the measured mutual coupling between the CP antenna elements at 3.5 GHz by more than 15 dB and by more than 13 dB at 3.02 GHz to 3.67 GHz, compared to the reference array. The proposed design achieves less than a 3 dB axial ratio, maximum realized gain of 5.52 dBic at 3.5 GHz. An excellent agreement between the simulated and measured outcomes has been studied.

本文提出了一种双面去耦元表面（DSDM）方法，用于降低间距很近的圆极化（CP）多输入多输出（MIMO）天线元件之间的相互耦合，适用于 6 GHz 以下的应用。拟议的 DSDM 结构带有一个方形贴片层，置于阵列上方，通过非传播的蒸发波和操纵传播的反射 CP 波的极化来减少相互耦合。这种去耦机制依赖于从元原子中提取的 DSDM 负电介电常数。当 CP 波通过阵列的受激 CP 天线入射到 DSDM 偏振器时，反射和传输的 CP 波的偏振态会在 DSDM 的控制下发生变化。在反射模式下，DSDM 的负介电常数会产生两种波，即产生极化失配的反射波和降低 CP 天线间耦合的蒸发波，而在传输模式下，辐射模式可控制在 φ = 45° 或 φ = 135°。所提出的低剖面去耦结构已制作完成并通过实验验证。与参考阵列相比，去耦设计在 3.5 GHz 频率下极大地降低了 CP 天线元件之间的互耦，降低幅度超过 15 dB，在 3.02 GHz 至 3.67 GHz 频率下降低幅度超过 13 dB。拟议设计的轴向比小于 3 dB，在 3.5 GHz 实现了 5.52 dBic 的最大增益。研究表明，模拟结果与测量结果非常吻合。

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引用次数: 0

Deep supercooling solidification towards tuned amorphous/nanocrystalline dual phases for superior magnetic nanocrystalline alloys 通过深度过冷凝固实现非晶/纳米晶双相调谐，打造卓越的磁性纳米晶合金

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-16 DOI: 10.1016/j.matdes.2024.113469

Kebing Wang , Chen Wu , Lingfeng Wang , Xinyang Zhang , Qiming Chen , Mi Yan

Nanocrystalline soft magnetic alloys featuring with amorphous-nanocrystalline dual-phase structure are critical for energy conversion and transportation at elevated frequencies. Their applications however, are refrained by limited saturation magnetic flux density (B_s) due to unavoidable addition of a considerable quantity of non-magnetic elements for glass forming ability (GFA). Furthermore, engineering of the amorphous-nanocrystalline microstructure is critical for the coercivity (H_c), which urges development of advanced approach. In this study, a deep supercooling solidification has been proposed, which not only promotes the formation of short-range packing and icosahedron/icosahedron-like structures for enhanced GFA, but also induces an optimized microstructure consisting of highly disordered amorphous matrix to facilitate nanograin refinement. Based on such strategy, Finemet-based nanocrystalline alloy with superior magnetic properties (B_s = 1.71 T, H_c = 5.0 A/m) has been achieved without additional glass forming element. Such superior performance is correlated to the unique magnetic domain structure involving straight domain walls and smooth movement. The deep supercooling strategy not only breaks the trade-off between the B_s and GFA to allow the design of nanocrystalline alloys with large ferromagnetic content, but also serves as an effective method for microstructure optimization for nanocrystalline alloys.

具有非晶-非晶双相结构的纳米晶软磁合金对于高频率下的能量转换和传输至关重要。然而，由于在玻璃成型能力（GFA）中不可避免地添加了大量非磁性元素，它们的应用受到饱和磁通密度（Bs）的限制。此外，非晶-非晶微观结构的工程设计对矫顽力（Hc）至关重要，因此需要开发先进的方法。本研究提出了一种深度过冷凝固方法，它不仅能促进短程堆积和二十面体/类二十面体结构的形成以提高 GFA，还能诱导由高度无序的非晶基质组成的优化微结构，从而促进纳米晶粒的细化。基于这种策略，Finemet 纳米晶合金在不添加玻璃成型元素的情况下实现了卓越的磁性能（Bs = 1.71 T，Hc = 5.0 A/m）。这种优异的性能与独特的磁畴结构有关，其中包括笔直的畴壁和平滑的运动。深度过冷策略不仅打破了 Bs 和 GFA 之间的权衡，从而可以设计出铁磁含量高的纳米晶合金，而且还是优化纳米晶合金微观结构的有效方法。

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引用次数: 0

Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg 用混合智能方法研究后处理对缺口快速成型 AlSi10Mg 机械性能的影响

IF 7.6 2区材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY

Materials & Design

Pub Date : 2024-11-16 DOI: 10.1016/j.matdes.2024.113462

Erfan Maleki , Sara Bagherifard , Okan Unal , Mario Guagliano

This study introduces a Hybrid Intelligence approach to investigate the Process-Structure-Property-Performance (PSSP) relationship in additively manufactured (AM) materials, specifically focusing on V-notched laser powder bed fused (L-PBF) AlSi10Mg specimens. The Humen Intelligence (HI) component managed the design, manufacturing processes, post-processing, structural characterization, mechanical testing, and data collection. In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. This integration of HI and AI enabled a more thorough exploration of these relationships; data collected from our previous research were complemented with new experiments conducted to assess the effects of various heat treatments (HTs) and surface post-treatments (SPTs) on the fatigue behavior of the specimens. The techniques applied included stress relief (SR), T6 thermal treatments, sand blasting (SB), shot peening (SP), severe vibratory peening (SVP), laser shock peening (LSP), tumble finishing (TF), abrasive flow machining (AFM), chemical polishing (CP), electrochemical polishing (ECP), and chemical milling (CM), along with their combinations. A total of 54 different post-processing techniques were examined in this study. The experimental data, covering surface texture, microstructure, porosity, hardness, and residual stress, were used to develop an ML model that analyzed the fatigue behavior of the specimens. This approach represents a significant advancement toward integrated mechanistic and data-driven materials engineering, offering valuable insights for optimizing fatigue performance in practical applications.

本研究介绍了一种混合智能方法，用于研究增材制造（AM）材料的工艺-结构-性能（PSSP）关系，特别侧重于 V 型缺口激光粉末床熔融（L-PBF）AlSi10Mg 试样。虎门智能（HI）组件负责管理设计、制造工艺、后处理、结构表征、机械测试和数据收集。与此同时，人工智能（AI）利用先进的机器学习（ML）算法，执行与预测、敏感性分析和参数分析相关的任务。人工智能识别模式并开发预测模型，从而更深入地了解工艺参数如何影响材料特性和性能。HI 和人工智能的整合使我们能够更深入地探索这些关系；我们从以前的研究中收集的数据得到了新实验的补充，以评估各种热处理 (HT) 和表面后处理 (SPT) 对试样疲劳行为的影响。应用的技术包括应力消除 (SR)、T6 热处理、喷砂 (SB)、喷丸强化 (SP)、剧烈振动强化 (SVP)、激光冲击强化 (LSP)、滚筒精加工 (TF)、磨料流加工 (AFM)、化学抛光 (CP)、电化学抛光 (ECP) 和化学铣削 (CM) 及其组合。本研究共考察了 54 种不同的后处理技术。实验数据包括表面纹理、微观结构、孔隙率、硬度和残余应力，用于开发分析试样疲劳行为的 ML 模型。这种方法代表了在综合机械和数据驱动材料工程方面取得的重大进展，为优化实际应用中的疲劳性能提供了宝贵的见解。

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