To investigate the effect of magnetic field on the friction and wear characteristics of the transmission surface in magnetorheological fluid, the Archard wear model is modified by introducing the excitation current parameter. Then, surface wear during magnetorheological fluid transmission is simulated using Abaqus. The transmission performance at different excitation currents is tested using a self-developed magnetorheological fluid friction and wear experimental device. After the experiments, a scanning electron microscope and a white light interferometer are used to characterize the wear morphology of the transmission surface. The experimental results show that the wear depth on the outer region of the transmission surface is larger than that on the inner region. In addition, the wear on the transmission surface becomes more severe with higher excitation current. The conventional Archard model and the modified Archard model exhibit average relative errors of 20.61% and 9.56%, respectively. Therefore, the modified Archard wear model provides meaningful guidance for research on surface wear in magnetorheological fluid.
Fatigue cracks in thin metallic structures subjected to bending can nucleate at multiple surface locations and propagate rapidly through the thickness, resulting in a brief interval between damage initiation and final failure. This study experimentally evaluates a piezoresistive sensor comprising indium tin oxide (ITO) nanowires dispersed in a polyvinyl butyral (PVB) matrix for real-time qualitative monitoring of this bending fatigue damage. Six structural steel specimens with a geometric stress concentrator were excited near their first bending resonance at frequencies between 28.5 and 29.5 Hz. Two sensor configurations were examined: two narrow nanocomposite strips, tested on four specimens, and one wider sensor covering the critical region, tested on two specimens. Specimens with narrow sensors failed between approximately 48,000 and 58,000 cycles, whereas the wider-sensor configuration failed near 45,000 cycles. In all four narrow-sensor tests, changes in the electrical response were detected prior to final structural failure. The wider sensors produced more stable electrical signals but showed lower sensitivity to localized damage, with significant changes occurring only shortly before or during specimen failure. This difference is attributed to the fewer parallel conductive pathways in the narrow sensors, which increases their sensitivity to localized disruptions in the ITO percolation network. The results demonstrate the feasibility of PVB/ITO nanocomposite sensors for real-time qualitative detection of rapidly evolving bending fatigue damage and highlight sensor geometry as a key parameter that influences damage sensitivity.
Additive manufacturing (AM) enables the fabrication of complex, multifunctional structures, while integration with shape memory polymers (SMPs) extends this capability to 4D printing through time-dependent, stimulus-responsive behavior. However, commonly used SMPs like PLA are brittle at room temperature, limiting their use in load-bearing or deformable components. This study investigates poly(cyclohexylene dimethylene terephthalate glycol; PCTG), a largely unexplored but mechanically robust alternative, for 4D printing applications using cold programming. Using a Taguchi L27 design, we systematically examine the effects of five key printing parameters, that is, nozzle temperature, layer height, print speed, infill density, and raster angle on the tensile and flexural properties of 3D-printed PCTG. FTIR and DSC analyses confirm PCTG’s amorphous nature and glass transition temperature of ∼84 ° C. Mechanical testing reveals tensile strength up to 43 MPa, flexural modulus values ranging from 1.43 to 1.98 GPa, and high ductility. Cold-programed shape memory behavior is demonstrated via three-point bending at room temperature followed by thermal recovery. Shape recovery ratios reach up to 0.99, and shape fixity ratios up to 0.82 are achieved, depending on process conditions. Results highlight PCTG’s potential as a ductile, tunable SMP for advanced 4D printing, overcoming key limitations of conventional materials like PLA.
This paper proposes a hybrid optimization algorithm combining the adaptive Marine Predators Algorithm (aMPA) and adaptive Differential Evolution (aDE) for parameter identification of the Bouc–Wen hysteresis model in piezoelectric actuators. The proposed aMPA–aDE algorithm improves the balance between global exploration and local exploitation, thereby enhancing convergence performance and identification robustness compared with standalone optimization methods. To evaluate its effectiveness, the proposed algorithm is compared with several optimization approaches, including MPA, aMPA, DE, CMA-ES, and aDE–Jaya. Comparative results show that the proposed aMPA–aDE algorithm achieves competitive convergence speed and identification accuracy. The proposed method is then applied to identify the Bouc–Wen hysteresis model using experimental input–output data obtained from a piezoelectric actuator. Furthermore, the identified hysteresis model is incorporated into a Sliding Mode Controller (SMC) for trajectory tracking and experimental verification under different reference signals. Experimental results demonstrate that the proposed method can effectively characterize the nonlinear hysteresis behavior of the piezoelectric actuator and provide satisfactory tracking performance.
The presence of system time delay, model uncertainty and external excitation in structural vibration systems can lead to system instability. Therefore, a smith predictor with sliding mode controller based on extended state observer (SMCESO) is utilized to compensate for these uncertainties. However, a conventional predictor-based SMCESO (PSMCESO) fails to deal with time-varying delay. An adaptive smith predictor is designed to address the time-varying delay introduced by accelerometers, actuators, computation, and communicational network. In addition, knowing that an accurate nominal model is difficult to build in the smith predictor, an adaptive algorithm based on Lyapunov energy function, hence, is formulated to enable real-time system identification and correction. Finally, the identified parameters are incorporated into the nominal model of the adaptive smith predictor, achieving the whole integration of adaptive model identification and smith predictive control. The proposed adaptive smith predictor for time-varying delay compensation with a sliding mode controller based on an extended state observer(ASP-SMCESO) effectively ensures robust vibration suppression in systems subject to time-varying delay. An experimental platform was developed using the NI PCIe-6343 data acquisition card and the MATLAB/Simulink environment to comparatively validate the proposed ASP-SMCESO against SMCESO and PSMCESO. The vibration tests demonstrate that the control performance is enhanced in comparison with the two classical SMCESO-based approaches.
