International Journal of Mechanical Sciences最新文献

英文中文

Nanoscale deformation and removal mechanisms in Al/SiC composites Al/SiC复合材料的纳米形变和去除机制

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-06 DOI: 10.1016/j.ijmecsci.2026.111208

Peipei Jing, Chen Yang, Bing-Feng Ju, Chaoqun Dang

Ultra-precision machining of Al/SiC composites remains challenging due to the pronounced mechanical contrast between the ductile Al matrix and brittle SiC reinforcements. Understanding their nanoscale deformation and removal mechanisms is essential for achieving high-quality surfaces. In this study, we employ a dual-mode nano-scratching approach that uniquely combines ex-situ (long tracks, 10–200 mN) and in-situ (short tracks, 0.5–20 mN) experiments to elucidate the load-dependent removal regimes and underlying damage mechanisms. The Al phase exhibits continuous plastic flow mediated by dislocation activity, accompanied by force fluctuations and variations in the friction coefficient (0.2–0.6). In contrast, the SiC phase accommodates deformation through dislocations, stacking faults, amorphization, and microcracking, showing stable force responses and a consistently low friction coefficient (∼0.1). A critical discovery is the identification of phase-sequence-dependent interfacial cracking that preferentially initiates during SiC-to-Al transitions. This behavior is driven by dislocation pile-ups at the interface and manifests as a characteristic “dip–rise” signature in the friction coefficient. Furthermore, subsurface microcracks in SiC are shown to originate from heterogeneous strain gradients transferred from the overlying Al rather than direct loading. With increasing loads, SiC fracture becomes dominant, producing extensive surface and subsurface damage. Under the maximum load (200 mN), the Al matrix undergoes pronounced grain refinement via the cooperative action of continuous and discontinuous dynamic recrystallization. Collectively, these findings uncover phase-specific deformation pathways and interfacial dynamics, providing a mechanistic foundation for optimizing the ultra-precision machining of heterogeneous composites.

Al/SiC复合材料的超精密加工仍然具有挑战性，因为韧性Al基体和脆性SiC增强材料之间存在明显的力学差异。了解它们的纳米级变形和去除机制对于获得高质量的表面至关重要。在这项研究中，我们采用双模纳米刮擦方法，独特地结合了非原位（长轨道，10-200 mN）和原位（短轨道，0.5-20 mN）实验来阐明载荷相关的去除机制和潜在的损伤机制。在位错活动性的介导下，Al相表现出连续的塑性流动，伴随着力的波动和摩擦系数的变化（0.2-0.6）。相比之下，SiC相可以通过位错、层错、非晶化和微裂纹进行变形，表现出稳定的力响应和持续的低摩擦系数（~ 0.1）。一个关键的发现是确定相序列相关的界面开裂，优先启动在硅到铝的转变。这种行为是由界面处的位错堆积驱动的，并在摩擦系数中表现为特征的“倾斜-上升”特征。此外，SiC的亚表面微裂纹是由上覆Al传递的非均匀应变梯度而不是直接加载引起的。随着载荷的增加，碳化硅断裂占主导地位，产生广泛的表面和亚表面损伤。在最大载荷（200 mN）下，Al基体通过连续和不连续动态再结晶的共同作用，晶粒细化明显。总的来说，这些发现揭示了相特定变形路径和界面动力学，为优化非均质复合材料的超精密加工提供了机制基础。

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引用次数: 0

Dumbbell-shaped chiral metamaterials for multi-polarized broadband vibration suppression 用于多极化宽带振动抑制的哑铃形手性超材料

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111195

Shenghao Xu, Junhong Park

Three-dimensional (3D) mechanical metamaterials provide new insights into broadband vibration suppression. However, their conventional design strategies typically extend two-dimensional (2D) counterparts onto the central region or surfaces of a hexahedral framework to attenuate multi-polarized vibration. This approach yields a wide bandgap. However, it results in very large structural volume and mass. To solve this problem, this study introduces a novel dumbbell-shaped chiral mechanical metamaterial (DCM), which combines a 2D planar frame with dumbbell-shaped resonators. The proposed DCM leverages the inertial amplification effect to perform lightweight vibration suppression, while simultaneously generating an ultra-broad bandgap-12 times wider than that of conventional chiral metamaterial (CCM)-by coupling in-plane resonance with compressive-torsional motions. Furthermore, its twisted variant, TDCM, can perform multi-polarized broadband vibration suppression comparable with that of 3D metamaterials. A dynamic equivalent mass model (DEM) and a 3D equivalent spectral element model (ESEM) are then developed to accurately predict the bandgap range and vibration responses. Afterwards, the vibration attenuation mechanism is analyzed in terms of the spatial distribution of energy in the frequency domain. The obtained results show high consistency with theoretical predictions.

三维（3D）机械超材料为宽带振动抑制提供了新的见解。然而，他们的传统设计策略通常是将二维（2D）对应物扩展到六面体框架的中心区域或表面，以衰减多极化振动。这种方法产生宽的带隙。然而，它导致了非常大的结构体积和质量。为了解决这一问题，本研究引入了一种新型的哑铃形手性机械超材料（DCM），该材料将二维平面框架与哑铃形谐振器相结合。所提出的DCM利用惯性放大效应来进行轻量化的振动抑制，同时通过与压缩扭转运动耦合的平面内共振产生超宽的带隙——比传统手性超材料（CCM）的带隙宽12倍。此外，其扭曲变体TDCM可以实现与3D超材料相当的多极化宽带振动抑制。然后建立了动态等效质量模型（DEM）和三维等效谱元模型（ESEM）来准确预测带隙范围和振动响应。然后，从频域能量空间分布的角度分析了振动衰减机理。所得结果与理论预测具有较高的一致性。

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引用次数: 0

Aviation hydraulic joint leakage prediction with normal-tangential contact mechanics model 基于法向-切向接触力学模型的航空液压接头泄漏预测

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111215

Wurui Ta , Hang Zhao , Lei Meng , Yitao Zhang , Hui Cheng , Kaifu Zhang , Youhe Zhou

The sealing performance of flared aviation hydraulic pipe joints directly determines whether the aircraft can operate safely. Accurately analyzing the contact forces at the joints and revealing the mechanism through which they affect the sealing performance are essential for resolving leakage failures. In this study, a new leakage prediction model for flared aviation hydraulic pipe joints is developed based on a contact mechanics model that accounts for both normal and tangential forces acting on the sealing interface. The average separation of the interface was redefined from variations in the void volume of rough surfaces, and its relationship with the leakage rate was derived through contact deformation, enabling prediction of the joint’s leakage behavior. The results demonstrate that the proposed model considers the impact of tangential forces on interfacial sealing, improving leakage rate prediction accuracy by 28.5%. This improvement is mainly attributed to accounting for the influence of tangential forces on the average separation of the interface and the real contact area. This work provides insight into the leakage mechanism at metal-metal sealing interfaces and offers theoretical guidance for analyzing sealing performance and optimizing assembly process parameters of hydraulic pipe joints.

扩口航空液压管接头的密封性能直接决定着飞机能否安全运行。准确分析接头处的接触力并揭示其影响密封性能的机理对于解决泄漏故障至关重要。基于接触力学模型，考虑了作用在密封界面上的法向力和切向力，建立了一种新的喇叭口航空液压管接头泄漏预测模型。根据粗糙表面空隙体积的变化重新定义了界面的平均分离，并通过接触变形导出了其与泄漏率的关系，从而实现了对接头泄漏行为的预测。结果表明，该模型考虑了切向力对界面密封的影响，泄漏率预测精度提高了28.5%。这种改进主要是由于考虑了切向力对界面和实际接触面积平均分离的影响。该工作为深入了解金属-金属密封界面的泄漏机理提供了理论指导，为液压管接头密封性能分析和装配工艺参数优化提供了理论指导。

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引用次数: 0

Multi-level evolutionary design of targeted rigid-flexible parallel rehabilitation robot 目标刚柔并联康复机器人的多级进化设计

IF 7.3 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111201

Chenglei Liu, Jianjun Zhang, Jun Wei, Xiankun Zhao, Anyu Shi, Jingke Song, Shijie Guo

引用次数: 0

Tilted elliptical vibration cutting textures for enhanced crankshaft lubrication 倾斜椭圆振动切削纹理增强曲轴润滑

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111186

Lin Wang, Zhixuan Bian, Haoxia Tian, Zixuan Chen, Luo Haoqi, Ziyi Wu, Shanyi Ma, Jianguo Zhang, Jianfeng Xu

The transmission durability and operational safety of crankshafts, which are key components in robotics and automation, are governed by surface quality and lubrication performance. Owing to the distinctive geometry of crankshafts, conventional machining of SCM420H steel often induces subsurface damage and brittle fractures that cannot be removed by polishing. In contrast, ultrasonic elliptical vibration cutting (UEVC) improves surface form accuracy and finish while simultaneously generating microstructures that enhance tribological performance. In this study, a hydrodynamic lubrication model based on surface topography theory was established and integrated with coefficient of friction (COF) measurements and wear-track morphology to systematically evaluate the effects of UEVC on crankshafts. The results show that wear on UEVC specimens is dominated by plastic deformation and fine scratches, with no fracture, delamination, or excessive debris, such as that observed in conventionally ground samples. The machining process also enhances surface quality and stabilizes frictional behavior, producing periodic concaves and scallop-like vibration textures that reduce the COF by 49.6 % and decrease the wear rate by >34.5 %. These improvements arise from enhanced lubricant retention, more uniform load distribution, and the suppression of stress concentrations on micro-protrusions. These findings highlight the advantages of UEVC for precision machining and lubrication optimization of crankshaft components and provide guidance for improving tribological performance.

曲轴是机器人和自动化中的关键部件，其传动耐久性和运行安全性取决于其表面质量和润滑性能。由于曲轴的独特几何形状，SCM420H钢的常规加工通常会导致亚表面损伤和脆性断裂，无法通过抛光消除。相比之下，超声椭圆振动切割（UEVC）提高了表面形状的精度和光洁度，同时产生了增强摩擦学性能的微结构。基于表面形貌理论，建立了曲轴流体动力润滑模型，并结合摩擦系数测量和磨损轨迹形貌，系统评价了UEVC对曲轴的影响。结果表明，UEVC试样的磨损主要以塑性变形和细小划痕为主，没有常规研磨试样中观察到的断裂、分层或过量碎屑。加工过程还提高了表面质量并稳定了摩擦行为，产生了周期性的凹形和扇贝状振动织构，使COF降低了49.6%，磨损率降低了34.5%。这些改进来自于增强的润滑剂保留，更均匀的负载分布，以及对微突出的应力集中的抑制。这些研究结果突出了UEVC在曲轴部件精密加工和润滑优化方面的优势，为提高曲轴部件的摩擦学性能提供了指导。

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引用次数: 0

Physics-infused KAN for turbulent flow prediction and CFD integration 湍流预测与CFD集成的物理注入KAN

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111185

Chen Ouyang , Chuanqi Zhao , Hong Liu , Huiying Zhang , Tiange Ma , Shengli Xu

Computational fluid dynamics (CFD) has become an indispensable tool for simulating turbulent flows, yet solving the Reynolds-averaged Navier–Stokes (RANS) equations remain time-consuming, memory-intensive, and computationally expensive. To address this challenge, a novel prediction model, the Physics-Informed Kolmogorov–Arnold Networks (PI-KAN) model, is proposed for effectively learning turbulent flow field representations from CFD results. The model integrates the physics-informed neural network (PINN) framework and the attention mechanism with the Kolmogorov–Arnold Network (KAN). In PI-KAN, for efficient complex geometric information extraction, the encoder strategically integrates KAN and multi-head attention mechanisms, forming the Kansformer architecture. This enables PI-KAN to directly predict flow fields from given flow conditions and geometry while incorporating physical constraints to enhance accuracy and reduce computational cost. Validation confirms that PI-KAN delivers high-accuracy predictions using just 46,500 trainable parameters, compared with 548,460 in the MLP. When its predictions are used as initial conditions in the CFD solver, the convergence process is substantially accelerated, and the model exhibits strong generalization to unseen flow configurations. These results suggest that PI-KAN can serve as an efficient and accurate surrogate model for accelerating CFD simulations in complex engineering applications.

计算流体动力学（CFD）已经成为模拟湍流不可缺少的工具，但求解reynolds -average Navier-Stokes （RANS）方程仍然是耗时、内存密集型和计算成本高的问题。为了解决这一挑战，提出了一种新的预测模型，即物理信息Kolmogorov-Arnold网络（PI-KAN）模型，用于从CFD结果中有效地学习湍流流场表示。该模型将物理信息神经网络（PINN）框架和注意机制与Kolmogorov-Arnold网络（KAN）相结合。在PI-KAN中，为了高效地提取复杂的几何信息，编码器将KAN和多头注意机制巧妙地集成在一起，形成了Kansformer架构。这使得PI-KAN能够根据给定的流动条件和几何形状直接预测流场，同时结合物理约束来提高精度并降低计算成本。验证证实，PI-KAN仅使用46,500个可训练参数就能提供高精度的预测，而MLP的可训练参数为548,460个。将其预测作为CFD求解器的初始条件时，收敛过程大大加快，模型对未知流态具有较强的泛化能力。这些结果表明，PI-KAN可以作为一种高效、准确的替代模型来加速复杂工程应用中的CFD模拟。

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引用次数: 0

Vibration Power Flow and Dynamic Interactions in Nonlinear Mistuned Bladed Disk System 非线性失谐叶片系统的振动功率流与动力相互作用

IF 7.3 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111190

Kaixin Shao, Tengxiao Wang, Jie Yuan, Xin Dong, Jian Yang

引用次数: 0

Prior knowledge-guided vibration signal augmentation for mechanical fault diagnosis 基于先验知识的振动信号增强机械故障诊断

IF 7.3 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111161

Wei Yang, Zhaojun Yang, Wei Luo, Jialong He

引用次数: 0

Deep learning-assisted multi-objective optimization of labyrinthine ventilated acoustic metamaterials 深度学习辅助迷宫式通风声学超材料的多目标优化

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111189

Gen Li , Lihua Tang , Vladislav Sorokin , Huan He , Cuipeng Xia , Zongxi Li

Labyrinthine ventilated acoustic metamaterials (LVAMs) offer great potential for applications requiring both strong sound insulation and efficient airflow, yet simultaneously optimizing these conflicting objectives remains a fundamental challenge. Unlike previous studies that directly regress the sound transmission loss (STL), this work introduces a novel deep-learning-assisted, transfer-matrix-based framework, enabling analytical STL computation for arbitrary serial combinations of unit cells. A forward predictor network (FPN) is trained to predict the four components of the transfer matrix for a single unit cell, enabling analytical computation of the STL of arbitrary serial combinations of unit cells. To enhance robustness, a peak-softening preprocessing strategy and a value-weighted loss function are introduced, yielding predictions that better align with experimentally realizable performance. The FPN is integrated with the NSGA-II algorithm to jointly optimize STL and ventilation capacity, quantified by the open area ratio, thereby obtaining Pareto-optimal designs that balance these objectives. Numerical studies demonstrate the scalability and flexibility of the proposed method across various frequency ranges and STL thresholds. Experimental validation of 3D-printed prototypes confirms close agreement between predictions, simulations, and measurements, with the proposed framework outperforming direct STL prediction, particularly near resonance frequencies. This work highlights the potential of using intermediate physical representations in deep-learning-assisted optimization of multifunctional acoustic metamaterials.

迷宫式通风声学超材料（LVAMs）为需要强隔音和高效气流的应用提供了巨大的潜力，但同时优化这些相互冲突的目标仍然是一个根本性的挑战。与之前直接回归声音传输损失（STL）的研究不同，这项工作引入了一种新的深度学习辅助、基于传输矩阵的框架，使分析STL计算能够用于任意单元格的串行组合。前向预测网络（FPN）被训练来预测单个单元格的传输矩阵的四个组成部分，从而能够对任意单元格序列组合的STL进行分析计算。为了增强鲁棒性，引入了峰值软化预处理策略和值加权损失函数，从而产生更符合实验可实现性能的预测。将FPN与NSGA-II算法相结合，共同优化STL和通风量，通过开放面积比量化，从而得到平衡这两个目标的pareto最优设计。数值研究证明了该方法在不同频率范围和STL阈值下的可扩展性和灵活性。3d打印原型的实验验证证实了预测、模拟和测量之间的密切一致，所提出的框架优于直接的STL预测，特别是在共振频率附近。这项工作强调了在多功能声学超材料的深度学习辅助优化中使用中间物理表示的潜力。

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引用次数: 0

Sensitivity-analysis guided Bayesian optimization for crystal plasticity parameter identification 灵敏度分析指导贝叶斯优化晶体塑性参数辨识

IF 9.4 1区工程技术 Q1 ENGINEERING, MECHANICAL

International Journal of Mechanical Sciences

Pub Date : 2026-01-05 DOI: 10.1016/j.ijmecsci.2026.111205

Junyun Pan , Huadong Fu , Kaikai Qiu , Weidong Li , Jianxin Xie

Twinning-induced plasticity (TWIP) is a crucial deformation mechanism in many alloys directly linked with their ductility. In addition to experimental approaches, crystal plasticity (CP) modeling is another handy way to study TWIP. Nevertheless, reliable determination of the parameters of complex CP models imposes a first challenge prior to any meaningful modeling practices. In the present work, we crafted a framework for efficiently and robustly determining the parameters of CP models. The novelty of the framework lies in combining the Sobol sensitivity analysis and Bayesian optimization in sequence, where the former is for reducing the dimension of the input parameters followed by the latter to find the values of a shrunk set of parameters. The robustness and accuracy of the framework were demonstrated with three case studies of varying complexities where CP models with varied considerations of twinning and for three typical metallic crystal structures, namely, face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal close-packed (HCP) were accounted for. Compared to alternatives, our framework is capable of pinpointing a smaller optimization objective, making it both robust and efficient in identifying the parameters of CP models. The efficient and robust determination of CP model parameters with the proposed framework set the foundation for in-depth investigations of deformation mechanisms involving twinning, dislocation, etc.

孪生诱发塑性（TWIP）是许多合金的重要变形机制，与合金的延性直接相关。除了实验方法外，晶体塑性（CP）建模是研究TWIP的另一便捷方法。然而，在任何有意义的建模实践之前，可靠地确定复杂CP模型的参数是第一个挑战。在目前的工作中，我们设计了一个框架，用于有效和鲁棒地确定CP模型的参数。该框架的新颖之处在于将Sobol灵敏度分析和贝叶斯优化按顺序结合起来，前者是对输入参数进行降维，后者是对输入参数进行降维，得到一个缩减后的参数集的值。通过三个不同复杂性的案例研究证明了框架的鲁棒性和准确性，其中考虑了不同孪晶因素的CP模型以及三种典型的金属晶体结构，即面心立方（FCC），体心立方（BCC）和六边形紧密堆积（HCP）。与备选方案相比，我们的框架能够精确定位较小的优化目标，使其在识别CP模型参数方面既鲁棒又高效。该框架有效且稳健地确定了CP模型参数，为深入研究孪晶、位错等变形机制奠定了基础。

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