国际机械系统动力学学报(英文)最新文献

Adolescent idiopathic scoliosis (AIS) is a sideway curvature of the spinal column combined with a vertebral rotation that usually occurs in adolescents without any known causes. Bracing, the most common conservative treatment of AIS, has not fully exploited the benefits of the active control approaches powered by artificial intelligence (AI), although AI has entered a wide range of applications. The correction forces exerted by the brace are controlled passively by regulating the tightness of the brace's strap. Besides, training the learning-based control methods using a virtual model is of high importance in the AIS brace treatment, since training using trial and error on human subjects may result in unexpected pressure and injuries on the patient's torso. However, digital twin (DT) modeling, an emerging technology, has not been implemented into the AIS brace treatment yet. In this paper, reinforcement learning-based position-based impedance control (RLPIC) is proposed to enable a robotic brace to learn the desired physical interaction between the robotic brace and the human torso. A five-dimensional (5D) three-layer DT is also developed to be used for training the RLPIC in a simulated environment. The 5D three-layer DT consists of a physical system, a three-layer digital model of the physical system, including the robotic brace, human torso, and the physical human–robot interaction (HRI), a bidirectional connection between them, and an optimization dimension. A neural network-based regression model is also proposed to estimate the unknown parameters of the digital model. Numerical simulations and real-time experiments are performed to validate the 5D three-layer DT model. The proposed RLPIC trained using the 5D three-layer DT is verified using numerical simulations in terms of position tracking, velocity tracking, and HRI control. It is concluded that the proposed learning-based interaction control approach can improve the HRI control by learning the desired interaction in a simulated environment.

Traditional boundary element method (BEM) faces significant challenges in addressing dynamic problems in thin-walled structures. These challenges arise primarily from the complexities of handling time-dependent terms and nearly singular integrals in structures with thin-shapes. In this study, we reformulate time derivative terms as domain integrals and approximate the unknown functions using radial basis functions (RBFs). This reformulation simplifies the treatment of transient terms and enhances computational efficiency by reducing the complexity of time-dependent formulations. The resulting domain integrals are efficiently evaluated using the scaled coordinate transformation BEM (SCT-BEM), which converts domain integrals into equivalent boundary integrals, thereby improving numerical accuracy and stability. Furthermore, to tackle the challenges inherent in thin-body structures, a nonlinear coordinate transformation is introduced to effectively remove the near-singular behavior of the integrals. The proposed method offers several advantages, including greater flexibility in managing transient terms, lower computational costs, and improved stability for thin-body problems.

The performance and lifespan of Li-ion batteries used in electric vehicles are influenced by operating and environmental conditions. An understanding of the mechanisms leading to performance degradation and capacity fading can aid in the design of better battery systems. In the present study, numerical models are developed to estimate the capacity fading, battery performance, and residual life. Furthermore, key associated parameters are identified as state of charge, charging protocols, and temperature. Later on, a deep machine learning (DML) model consisting of one input, four hidden, and one output layer is developed to estimate the residual life of a battery system. The five input parameters considered include voltage, current, temperature, number of cycles, and time, apart from residual life as the output parameter. The proposed DML model consists of five dense layers and three dropout layers with 2889 trainable parameters in total, with higher neuron counts in initial layers to process diverse inputs and fewer neurons in later layers to ensure compact feature representation as well as to make better and faster predictions. Results from the numerical and DML models are compared to the reported experimental results, where good agreement is observed. Thus, the developed model is tested on Lithium based Nickel Manganese Cobalt Oxide and Nickel Cobalt Aluminum Oxide batteries, for which parametric studies are performed to investigate the influence of the operating temperature, rate of charge/discharge, and pulse charging on the battery life. Therefore, the technologies proposed in this study can contribute to the development of intelligent battery management systems, enabling enhanced performance, and hence prolonged life of battery systems.

This study proposes a computational framework for developing a multibody dynamics (MBD) model to accurately predict the vibration behavior of front-loading washing machines. The framework integrates component-level experiments and mathematical modeling to characterize the dynamic behavior of key components, including the free-stroke damper, connecting bushing, and gasket, which significantly influence the machine's vibration. Simplified, yet precise, mathematical models were developed and validated against experimental data to represent these components' dynamic characteristics. The validated models were then integrated into a comprehensive MBD model of a front-loading washing machine. This model was further verified by comparing its predicted vibrations with experimental results obtained from actual washing machines. A parametric study assessed the model's accuracy under various unbalanced mass conditions and revolutions per minute ranges, which revealed that the model is capable of generalization across different operating scenarios. Although some errors remain in specific cases involving phase differences, the overall average error is 20.11%, with a standard deviation of 4.10%. These results demonstrate that the proposed framework effectively captures the vibration behavior of front-loading washing machines, offering a reliable tool for enhancing design and operational efficiency.

Vibrating flip-flow screens are widely used in the field of screening; its actual operation is affected by the impact force of materials, but existing research usually ignores this effect. Based on this background, considering the influence of material impact force and moment on vibrating flip-flow screens, this paper develops a dynamic model and a vibration differential equation of a vibrating flip-flow screen, performs the analysis of material movement and calculation of the material impact force, and includes the material impact force in the dynamic characteristic analysis of a vibrating flip-flow screen. The results indicate the following: (1) The impact forces $$ and $$ account for 29% and 57.58% of the excitation force amplitude, respectively, indicating that they are of the same magnitude as the excitation force. Material impact increases the vibration amplitudes of the main and floating frames, and therefore, cannot be ignored in vibrating flip-flow screen design. (2) By comparing the vibrating flip-flow screen's responses with and without the impact, it is found that impact force significantly influences the system response, causing the displacement curve to shift and the amplitude–frequency curve to have periodic fluctuations and peak values. (3) The effects of impact parameters on the dynamic characteristics of a vibrating flip-flow screen are studied. The results show that increases in material mass and material binding coefficient lead to a decrease in the system natural frequencies. Due to the impact force, the amplitude–frequency curve of the main frame peaks at a frequency lower than the first order of the natural frequency, and the amplitude–frequency curve of the floating frame peaks in the intervals of 5–10 Hz and 20–25 Hz. The results provide a theoretical reference for the design of vibrating flip-flow screens. The operating frequency of vibrating flip-flow screens should be selected to avoid the peak value due to the impact force, which helps extend the working life.

This paper proposes a hybrid algorithm based on the physics-informed kernel function neural networks (PIKFNNs) and the direct probability integral method (DPIM) for calculating the probability density function of stochastic responses for structures in the deep marine environment. The underwater acoustic information is predicted utilizing the PIKFNNs, which integrate prior physical information. Subsequently, a novel uncertainty quantification analysis method, the DPIM, is introduced to establish a stochastic response analysis model of underwater acoustic propagation. The effects of random load, variable sound speed, fluctuating ocean density, and random material properties of shell on the underwater stochastic sound pressure are numerically analyzed, providing a probabilistic insight for assessing the mechanical behavior of structures in the deep marine environment.

Non-Gaussian random vibrations have gained more attention in the dynamics-research community due to the frequently encountered non-Gaussian dynamic environments in engineering practice. This work proposes a novel non-Gaussian random vibration test method by simultaneous control of multiple correlation coefficients, skewness, and kurtoses. The multi-channel time-domain coupling model is first constructed which is mainly composed of the designed parameters and independent signal sources. The designed parameters are related to the defined correlation coefficients and root mean square values. The synthesized multiple non-Gaussian random signals are unitized to provide independent signal sources for coupling. The first four statistical characteristics of the synthesized non-Gaussian random signals are theoretically derived so that the relationships among the generated signals, independent signal sources, and correlation coefficients are achieved. Subsequently, a multi-channel closed-loop equalization procedure for non-Gaussian random vibration control is presented to produce a multi-channel correlated non-Gaussian random vibration environment. Finally, a simulation example and an experimental verification are provided. Results from the simulation and experiment indicate that the multi-channel response spectral densities, correlation coefficients, skewnesses, and kurtoses can be stably and effectively controlled within the corresponding tolerances by the proposed method.

Tracking control of multibody systems is a challenging task requiring detailed modeling and control expertise. Especially in the case of closed-loop mechanisms, inverse kinematics as part of the controller may become a game stopper due to the extensive calculations required for solving nonlinear equations and inverting complicated functions. The procedure introduced in this paper substitutes such advanced human expertise by artificial intelligence through the utilization of surrogates, which may be trained from data obtained by classical simulation. The necessary steps are demonstrated along a parallel mechanism called λ-robot. Based on its mechanical model, the workspace is investigated, which is required to set proper initial conditions for generating data covering the used operation space of the robot. Based on these data, artificial neural networks are trained as surrogates for inverse kinematics and inverse dynamics. They provide forward control information such that the remaining error behavior is governed by a linear ordinary differential equation, which allows applying a linear quadratic regulator (LQR) from linear control theory. An additional feedback loop of the tracking error accounts for model uncertainties. Simulation results validate the applicability of the proposed concept.

Cover Caption: Active vibration control in MIMO systems is a critical research area addressing the complexities of managing vibrations in various engineering applications. Using FEM, piezoelectric theory and FSDT, dynamic response analysis and active vibration control of smart FGM composite plate with FGPM surface actuators and sensors are introduced. To analyze the control efficiency of FGPM sensors and actuators on the FGM host structure, the LQR controller is utilized. It is emphasized that active vibration control of FGM plates can be performed effectively with the proper selection of FGPM sensors and actuators and their accurate distribution on the plate.

Tooth breakage is a common issue in geared systems. The high-contact-ratio spur gear system (HCRSG) maintains continuous transmission despite tooth breakage, but experiences increased impact vibration. In aviation, even if the gear teeth break, the gear's transmission cannot be stopped immediately. Therefore, studying gear system dynamics with tooth breakage is crucial for assessing the reliability of mechanical equipment. This study treats the tooth-back contact induced by backlash as the tooth-back collision and presents the multi-state meshing-collision pattern of HCRSG with one tooth breakage (OTB), including triple-tooth, double-tooth, single-tooth meshes, disengagement, and tooth-back collision. Time-varying meshing stiffness and load distribution coefficients of HCRSG with OTB are calculated. Then a multi-state meshing-collision nonlinear dynamic model of HCRSG with OTB is established. The meshing forces of HCRSG with OTB and without OTB are calculated and compared to examine the effect of tooth breakage. The multi-state meshing-collision nonlinear dynamics of HCRSG with OTB are studied via bifurcation diagram, phase portraits, and Poincaré maps by changing the transmission error amplitude. The results show that 3-2-3-2-3 meshing pattern of HCRSG is shifted to 2-1-2-1-2 meshing pattern due to tooth breakage. The effect of tooth breakage on the meshing force and dynamic behavior significantly depends on teeth disengagement or tooth-back collision. Tooth breakage greatly affects the bifurcation and chaos characteristics of multistate meshing-collision behavior of HCRSG. This study creates a framework to predict and assess the dynamics of gear transmission systems with tooth breakage in extreme aviation and aerospace environments.