Joining cast aluminum alloys remain challenging due to their inherent brittleness and susceptibility to cracking under thermo-mechanical loading. This limitation is particularly pronounced in A380, which exhibits severe petal formation and unstable bushing development during friction drilling. To investigate potential improvements, this study examines the friction drill joining (FDJ) of A380 using three distinct upper-sheet materials—Cu, Al6061, and AISI304—focusing on their influence on heat distribution, material flow, and bushing formation. A combined numerical and data-driven approach was adopted. Friction drilling tests under a single calibrated process condition were performed to ensure the appropriateness of input parameters for the ABAQUS simulations, which evaluated temperature evolution, von Mises stress, and bushing formation within the joint. Numerical results indicate that the presence of an upper sheet modifies heat generation and stress distribution, promoting more uniform material flow and improved bushing geometry under the investigated condition. A multilayer feed-forward artificial neural network (ANN) was further employed to predict bushing length, peak temperature, and stress response across different process scenarios. The study provides insight into the thermo-mechanical behavior of hybrid-layer FDJ assemblies and establishes a framework for guiding future numerical investigations and material selection in lightweight multi-material applications.
The dynamic analysis of composite structures exposed to supersonic flow is of significant importance, especially when subjected to severe dynamic loads. This paper investigates the nonlinear dynamic response of fluid-conveying polymethyl methacrylate cylindrical shells reinforced by carbon nanotubes (CNTs) under a hygrothermal environment and subjected to aerodynamic and harmonic loads. The main novelties of this study are the simultaneous consideration of internal fluid flow, external supersonic aerodynamic force, harmonic excitation, and hygrothermal effects on the nonlinear dynamic response of CNT-reinforced cylindrical shells. To this end, the governing equations for the shell are derived based on the first-order shear deformation theory (FOSDT) and nonlinear strain–displacement relations, and are solved using the generalized differential quadrature method (GDQM), Galerkin method, and the fourth-order Runge–Kutta method (4th-RKM). The moving fluid inside the shell is modeled using linear potential flow theory (LPFT), while supersonic flow is modeled with first-order piston theory (FOPT). Additionally, various patterns of CNT distribution across the shell thickness are considered. Finally, the effects of fluid parameters, such as aerodynamic force, fluid velocity, and density, ambient temperature difference, ambient humidity difference, and solid parameters like volume fraction and distribution pattern of CNTs, and geometric ratios of the shell on the increase or decrease of the shell’s vibration amplitude are analyzed and evaluated. Potential applications include advanced aerospace components, and high-performance pipelines or structures where fluid–structure interaction under dynamic aerodynamic pressure is critical. The findings provide crucial data for the design and stability analysis of such next-generation structures.
This study systematically investigates all structural parameters of the flow field in metallic bipolar plates (BPs) within proton exchange membrane fuel cells (PEMFCs). Using combined experimental verification and simulation, the effects of dimensional parameters on formability—characterized by maximum thinning rate, maximum stress, and thinning non-uniformity—were analyzed, including the interactions between factors. Response surface modeling identified significant factor interactions, while correlation and regression analyses quantified individual effects. Finally, engineering case validation demonstrated that the optimized parameters achieved safe maximum thinning rates below the theoretical fracture threshold (29.0
In practical driving scenarios, intelligent vehicles are frequently exposed to complex and time-varying operating conditions, under which conventional model predictive control (MPC) with fixed parameters often struggles to simultaneously maintain lateral path-tracking accuracy and vehicle stability. To address this issue, this paper proposes an adaptive MPC-based lateral path-tracking strategy with a speed-scheduled prediction horizon, in which the longitudinal speed is treated as a measurable time-varying parameter. On this basis, a fuzzy adaptation mechanism is further incorporated into the MPC framework, where the lateral error and heading error are selected as the inputs of the fuzzy controller to adjust the weighting coefficients in the objective function online. By dynamically coordinating the relative emphasis on tracking accuracy and stability, the proposed method is compared with a conventional MPC controller using fixed horizon parameters and fixed weights. Co-simulation results demonstrate that the proposed controller can effectively improve path-tracking performance while preserving real-time computational capability.
Cavitation is a transient multiphase phenomenon characterized by extreme temperatures, pressures, and high‑speed microjets. It produces three primary effects—thermal, chemical, and mechanical—which are distinct yet strongly coupled, and play critical roles in surface engineering. This review systematically summarizes the theoretical foundations, numerical simulation strategies, and experimental investigations of cavitation multi‑effects, with particular emphasis on their applications in material surface strengthening and processing. Different cavitation generation mechanisms, including ultrasonic, hydrodynamic, and laser‑induced cavitation, are compared, and their respective contributions to surface modification are analyzed. The relative significance and coupling behaviors of the thermal, chemical, and mechanical effects are critically evaluated, highlighting recent progress in quantifying how each effect contributes to material deformation and the enhancement of surface performance. Major numerical approaches—from single‑bubble dynamics to multiphase computational fluid dynamics (CFD) and multiscale coupling methods—are assessed in terms of predictive accuracy, computational cost, and practical applicability. Persistent challenges are discussed, such as the lack of unified theoretical models, limited in‑situ diagnostic techniques, and insufficient integration between theory and experiment. The novelty of this review lies in bridging the understanding of multi‑effect mechanisms with multiscale simulations, thereby advancing the development of controllable cavitation‑based surface modification technologies. This work provides a foundation for process optimization and the realization of intelligent surface engineering.
This paper takes functionally graded carbon nanotube reinforced composite (FG-CNTRC) sandwich cylindrical-conical coupled shells as research objects, and establishes a dynamic model based on first-order shear deformation theory (FSDT), von Kármán nonlinear strain theory and Isogeometric analysis (IGA). A numerical solution framework combining modal coordinate reduction and explicit time integration is proposed: low-order orthogonal modal bases are extracted to transform high-dimensional nonlinear motion equations into low-dimensional modal space equations, significantly reducing computational complexity. Meanwhile, p-order refinement, h-mesh refinement and k-continuity refinement (p/h/k-refinement), three typical refinement schemes in IGA, are adopted to clarify the convergence conditions for calculating structural vibration characteristics and verify the reliability of the method. The influences of carbon nanotube volume fraction, distribution patterns and structural geometric parameters on vibration response are systematically investigated. This work, for the first time, combines IGA with efficient model order reduction techniques, effectively addressing the high computational cost caused by large matrix bandwidth under the IGA framework in the nonlinear dynamic response analysis of complex FG-CNTRC coupled shells. The proposed approach features superior computational efficiency and accuracy, and reveals the regulation mechanism of key material and geometric parameters on structural dynamic performance. The present study provides an efficient theoretical tool and scientific basis for the vibration-resistant optimization design of FG-CNTRC cylindrical-conical coupled shell structures.
To address the strong nonlinear coupling between laser cladding parameters and coating quality for Stellite 12 on 2205 duplex stainless steel, this study proposes an interpretable learning and multi-objective optimization framework for process prediction and inverse parameter design. An Echo State Network optimized by the Enhanced Whale Optimization Algorithm is constructed to predict cladding efficiency, dilution rate, and forming coefficient, and SHAP analysis is employed to quantify the marginal contribution of each parameter and enhance model interpretability. For inverse optimization, a Hybrid Quantum Multi-Objective Grey Wolf Optimizer is developed, and a density-weighted ideal point method is applied to select a robust compromise solution from the Pareto front. The proposed model achieves high prediction accuracy with R² greater than 0.98 and outperforms conventional approaches. Experimental results confirm that the optimized parameters increase cladding efficiency by 17.39
Noise and redundant information of fault samples will lead to sample covariance deviating from real covariance in practical industrial applications. The covariance deviation reduces the accuracy of fault diagnosis, since the measure of the sample covariance can effectively capture the cross-modal correlations between the fault samples on space learning. To address the deviation issue, we propose a bearing fault diagnosis method based on cross-modal inverse hyperbolic sine space learning. The method designs a novel covariance correction way based on inverse hyperbolic sine. In the correction way, the sample covariance of the fault samples is decomposed into singular vectors and singular values, and the singular values are corrected by inverse hyperbolic sine constraints. Based on the singular vectors and the corrected singular values, we obtain the inverse hyperbolic sine covariance, and visually demonstrate the reduction in deviation between our covariance and the real covariance of the fault samples. Then, our covariance is integrated into canonical correlation analysis framework, and a novel cross-modal inverse hyperbolic sine space learning model is further developed by modality extension. According to theoretical derivation of the model, we obtain analytical solutions of space projection directions, and multi-modal fault features with good class separability are directly acquired through space projection, which can effectively improve the accuracy of fault diagnosis. The experimental results demonstrate that the proposed cross-modal inverse hyperbolic sine space learning method achieves nearly 100
Deep learning-based fault diagnosis faces severe bottlenecks due to the scarcity of labeled data under variable operating conditions and the lack of physical interpretability in “black-box” models. Specifically, the distribution discrepancy between ideal simulation data and complex real-world signals hinders the effective transfer of physical knowledge. To address these challenges, this paper proposes a novel cross-domain diagnosis method that deeply integrates dynamic mechanism modeling with conditional generative adversarial networks. A high-fidelity bearing dynamic model is constructed to generate a labeled synthetic source domain, providing robust physical priors to reduce reliance on expensive experiments. A “mechanism-generation-transfer” closed-loop framework is established. In this framework, simulation signals act as physical constraints to guide the CGAN, generating augmented samples that possess both theoretical validity and realistic noise characteristics. Furthermore, a dual-path adversarial mechanism featuring a gradient reversal layer is designed to dynamically align simulation knowledge with real-world data in the feature space. Experimental validation on rolling mill bearing datasets demonstrates that the proposed method outperforms state-of-the-art transfer strategies. The results confirm that this approach successfully synergizes physical interpretability with data adaptability, offering a robust solution for scenarios with limited samples and significant domain shifts.
The gear mesh stiffness (MS) characteristics have direct effect on its transmission error (TE), and then affect the noise level. To control meshing instability caused by tooth deformation, tooth tip modification is an important mean, but it will introduce tooth profile errors. Firstly, considering the inconsistency between base and root circle under different tooth numbers, the potential energy method is improved to calculate the MS of spur gear. Then, combined with tooth tip modification theory, a new analytical method for MS calculation considering tooth profile error is proposed, and the correctness is verified by finite element (FE) method. When modification is not conducted, the maximum error in calculating mean and P-P stiffness is 1.85
The gear transmission system is a critical component in locomotive propulsion, where root cracks constitute a common and severe failure mode. Current research often overlooks the synergistic effects of complex internal and external excitations on crack propagation. This study pioneers a comprehensive analysis of the meshing stiffness and contact characteristics of locomotive gears with root cracks, specifically addressing the coupled influence of crack parameters, assembly errors, tooth surface friction, and wheel‒rail excitations. The time-varying meshing stiffness calculated via the potential energy method decreases as the crack depth and angle increase. More significantly, simulations provide critical quantitative insights: the optimal center distance error is − 0.015 mm, whereas a positive deviation of 0.030 mm causes a drastic increase in the maximum equivalent stress at the crack tip to 510.8 MPa. More critically, external excitations are identified as the dominant risk factor. High-order wheel polygon excitation, such as that of the 24th order, can increase the maximum Von Mises stress of the driving gear to a critical level of 426.0 MPa, and severe rail corrugation with a 1.5 mm wave depth can also increase the stress to an alarming 674.0 MPa. These findings offer vital theoretical support for advanced gear fault diagnosis and the development of targeted maintenance strategies in railway engineering.
In recent years, large-range non-resonant piezoelectric actuators have been widely used due to their high precision and fast response characteristics. However, during the stepping process, they often accompany a reverse movement. To address this issue, this paper proposes a rhombic inertial viscous sliding piezoelectric linear actuator inspired by the movement of an arrow. The working principle of this driver mimics the movement characteristics of an arrow, achieving the storage and release of piezoelectric stack energy. The rhombic frame is optimized through finite element methods, and its dynamic model is established. At the same time, the motion characteristics of the driver are analyzed by combining the LuGre friction model. Based on this, a prototype of the actuator was fabricated and experimental tests were conducted. The experimental results show that at 60 V and 10 Hz, the resolution of the actuator can reach 1.2 μm; at 100 V and 650 Hz, the maximum output speed is 8.95 mm/s; and at 100 V and 100 Hz, the maximum load is approximately 3 N. This actuator reduces the reverse phenomenon to a certain extent and verifies the feasibility of the proposed structure.
This paper presents the development of a robust control strategy for the take-off, hovering, and landing phases of an electric Vertical Take-Off and Landing (eVTOL) vehicle. Controlling such vehicles is particularly challenging due to the nonlinear nature of their dynamics and the variations in flight conditions. To address these challenges, this work proposes the use of a Singular Value Decomposition (SVD)-guided Linear Quadratic Regulator with output feedback (LQRy), synthesized on an augmented plant with target zeros and combined with a Gain Scheduling strategy. The LQRy controller is designed for multiple linearized operating points within the flight envelope, while the Gain-Scheduling technique ensures smooth transitions between the different operational conditions encountered in this envelope. The effectiveness of the proposed controller is assessed through frequency-domain robustness analysis, considering uncertainties in the inertia matrix within the linear model, and, finally, through nonlinear simulations. The results demonstrate that the proposed approach enables stable flight, ensuring effective disturbance rejection and consistent performance under different operating conditions. For the linear model, the results show that the overshoot, Steady-state time, and rise time requirements are satisfied according to the performance criteria established in this work. For the nonlinear model, it can be observed that the altitude response achieved 0
Most structural optimization problems involving shallow domes with geometrically nonlinear behavior are formulated as single-objective, and the structure’s weight is the objective function to be minimized. However, other objective functions, such as the structure’s weight, can be incorporated into these formulations, introducing more realistic aspects into the desired optimal designs and significantly assisting the decision-maker in making their preferred choices. This paper extends the usual single-objective formulations by proposing new multi-objective formulations for structural optimization problems. Therefore, new structural optimization problems are proposed and solved, with objective functions including the structure’s weight, the first natural frequency of vibration, the first critical load factor for global stability, and compliance. Three multi-objective evolutionary algorithms are adopted to solve the proposed optimization problems and analyze several benchmark problems. Pareto fronts present the set of non-dominated solutions for the multi-objective formulations, and solutions are extracted from these sets according to multi-criteria decision-making.
External fluted wheel fertilizer spreaders often face significant discharge pulsation and poor spreading uniformity. The main novelty of this research lies in replacing the traditional deterministic response surface methodology (RSM) with an uncertainty-aware Gaussian process regression (GPR) model utilizing a Matérn 5/2 kernel to accurately capture highly nonlinear and stochastic granular flow characteristics. To achieve this, a parameter optimization framework combining discrete element method (DEM) simulation, probabilistic machine learning, and Bayesian optimization under small-sample conditions is proposed. A hybrid sampling strategy combining Box–Behnken design and Latin hypercube sampling generated key operational data to train the GPR surrogate model, which was then compared with RSM using different kernel functions. Cross-validation results demonstrated that the GPR model incorporating the Matérn 5/2 kernel performed best, achieving a coefficient of determination of approximately 0.61 and effectively quantifying prediction uncertainty. This GPR-driven Bayesian optimization framework identified an optimal configuration: a spreader height of 400 mm, a rotational speed of 300 r/min, and 7 fluted teeth. Simulation results showed that this combination reduced the fertilization coefficient of variation (CV) to 6.31
Concentricity between the inner bore and outer rim is a critical quality indicator of large-sized grinding wheels, directly affecting wheel run-out, grinding force fluctuation, surface integrity, and tool life. In current production lines, concentricity is still measured manually with dial indicators, which is time-consuming, operator-dependent, and prone to scratching the abrasive surface. Machine-vision inspection is a promising alternative, but on large wheels only a partial arc fits in the field of view, while the porous abrasive surface, specular reflections, shadows, and contour breakpoints make conventional edge extraction and circle fitting unreliable. To address these challenges, this paper proposes a non-contact machine-vision pipeline with three coupled novelties. First, a multi-channel sub-pixel contour fusion strategy independently extracts edges from the red, green, and blue channels and fuses them after sub-pixel refinement, recovering edge information lost by single-channel grayscale conversion. Second, an improved density-based clustering algorithm is constructed in a polar feature space with an angle-weighted composite distance metric, enabling stable separation of inner and outer arc points under breakpoints and uneven density. Third, an angle-constrained robust circle-fitting algorithm augments the geometric residual with an angular-distribution consistency penalty and a dynamic neighborhood, suppressing biased fits caused by short or asymmetric arcs. On a standard wheel with reference dimensions 115.08 mm, 280.12 mm, and concentricity 0.08 mm, the proposed method yields absolute errors of 0.04 mm, 0.07 mm, and 0.05 mm, respectively, outperforming six baseline combinations and meeting on-line inspection requirements.
To address the challenge of inaccurate endurance prediction for de-icing robots on high-voltage transmission lines—which can lead to catastrophic operational failures—this paper proposes a dynamic energy consumption modeling and endurance prediction method based on the Non-Homogeneous Semi-Markov Chain (NHSMMC). First, refined physical energy models for linear locomotion, slope de-icing, and obstacle crossing are systematically constructed. The core innovation lies in the synergistic integration of physical mechanics and stochastic processes: by employing multinomial logistic regression functions, real-time terrain slopes and ice thicknesses are mapped into dynamically updated non-homogeneous state transition probabilities. Furthermore, Gamma-distributed random dwell times are incorporated to characterize the temporal fluctuations of various tasks, overcoming the static limitations and temporal oversimplification of traditional Markov models. Validation through MATLAB/Simulink “stress tests” demonstrates that the NHSMMC model accurately captures instantaneous energy spikes induced by abrupt environmental variations.Quantitative analysis reveals that the NHSMMC reduces the Root Mean Square Error (RMSE) by 24.6
Accurate identification of coal and rock along the excavation path of a bolter miner during floor roadway excavation is a key enabler for intelligent tunneling, supporting coal–rock interface tracking and autonomous cutting. However, the harsh and highly variable underground environment makes reliable coal–rock identification extremely challenging. Focusing on the floor excavation process of a bolter miner, this study proposes a coal–rock identification method based on multisource process-signal fusion. First, in-situ cutting vibration signals are processed by variational mode decomposition (VMD), to suppress high-frequency noise and extract physically meaningful time–frequency features. These features are then fused with operational parameters such as cutting motor current, feed depth and drum height to construct a six-dimensional feature sequence. On this basis, a deep learning architecture combining a convolutional neural network (CNN), a long short-term memory (LSTM) network and an attention mechanism is developed, in which the CNN captures local cross-feature patterns, the LSTM models the temporal evolution of the cutting process, and the attention module highlights key time intervals associated with coal–rock transitions. Field data collected from a floor roadway excavation are used to train and validate the model, taking into account the actual geological distribution of coal and rock and the corresponding excavation responses. Experimental results show that the proposed model achieves a recognition accuracy of 97.44
Additive manufactured Inconel 718 parts are an important material choice for turbine blades in aircraft engines. To obtain the Johnson-Cook constitutive model parameters in finite element simulation, this study addresses issues such as surface quality and dimensional accuracy control, reduced machining efficiency, and impaired part performance caused by mismatched cutting parameters during drilling operations due to performance differences between additive manufacturing components and forged components. This study focuses on Inconel 718, employing quasi-static tensile and Hopkinson bar tests to determine the Johnson-Cook constitutive model parameters as A = 1021 MPa, B = 836 MPa, C = 0.028, n = 0.46, and m = 1.15. The finite element analysis model constructed based on these constitutive parameters showed simulation results with an error of less than 7
This study presents a thermo-mechanical nonlinear vibration analysis of microbeams with nonideal simply supported boundary conditions within the framework of the Modified Couple Stress Theory. The proposed model simultaneously incorporates size-dependent microscale effects, thermally induced axial loading, and boundary flexibility within a unified Euler–Bernoulli beam formulation. The governing equations are derived using Hamilton’s principle, nondimensionalized, and analytically solved through the multiple-scale perturbation method to obtain both linear and nonlinear dynamic characteristics of the system. The influences of temperature variation, material length-scale ratio, and nonideal boundary restraint on the natural frequencies, mode shapes, nonlinear to linear frequency ratios, and frequency response behavior are systematically investigated. Numerical results reveal that thermal loading reduces the effective structural stiffness and decreases the natural frequencies, whereas increasing the material length scale parameter and boundary restraint enhances the microscale stiffness and shifts the vibration frequencies upward. The nonlinear response exhibits pronounced hardening-type behavior, while increasing temperature amplifies the nonlinear effect and increasing the material length scale ratio and boundary restraint reduce the relative nonlinear sensitivity of the system. The obtained results demonstrate the strong coupled influence of thermal loading, support nonideality, and size-dependent mechanics on the dynamic behavior of microbeam systems. The proposed framework provides useful physical insight for the design and analysis of microscale beam-based devices operating under thermo-mechanical conditions.
