This study analyzes how the damping characteristics of anisotropic magnetorheological elastomers (MREs) change according to magnetic flux density, the volume fraction of carbonyl iron powder (CIP), and pre-stress. MREs are intelligent materials whose mechanical properties change depending on the magnetic field, and while research on stiffness changes has been actively conducted, analysis of damping characteristics is relatively insufficient. Consequently, the damping characteristics of MREs showed nonlinear responses depending on the interaction among magnetic flux density, CIP volume fraction, and pre-stress, confirming that damping performance can be utilized as a controllable material parameter. These results suggest the possibility that, in the design of MRE-based vibration control systems, not only stiffness but also damping characteristics can be actively controlled, and they provide basic data for the future development of high-performance vibration reduction technologies.

This study proposes a novel methodology for quantitatively evaluating the contribution of input signals in the time domain using Mutual Information (MI). Traditional contribution analysis methods based on Pearson correlation coefficients are limited by their assumption of linearity, making them inadequate for systems with time-varying characteristics or nonlinear transfer paths. To address this, we construct simulation data comprising transient, non-stationary input signals and nonlinear transfer functions, and compute time-local mutual information by adopting the windowing approach commonly used in Short-Time Fourier Transform (STFT). The results demonstrate that the proposed MI-based method outperforms conventional linear techniques in capturing the contributions of inputs under nonlinear and time-varying conditions. Notably, the MI approach provides accurate quantitative assessment even when the system's transfer path responds nonlinearly to input amplitude. This study shows that MI-based contribution analysis is a powerful and effective tool for evaluating input influence in nonlinear, non-stationary, and multi-input systems, and lays a foundation for future applications to experimental data and integration with alternative MI estimation methods.

In this study, contribution evaluation method applying Independent Component Analysis (ICA) was proposed. The necessity of applying ICA to the contribution evaluation was investigated through numerical simulation. The simulation modeled a scenario where the vibration/noise sources were physically overlapped in a small space, and their frequency characteristics were similar. For comparison between the conventional contribution evaluation method and the proposed method, the contribution evaluation was performed using the ordinary and partial contribution evaluation methods. Through this analysis, it was confirmed that the proposed method can identify contributions by restoring the signal when the frequency characteristics of the vibration/noise sources were similar, and their positions overlapped. These results confirm that the contribution evaluation method based on independent component analysis is effective in appropriately analyzing vibration/noise sources when their frequency characteristics are similar, and their positions overlap.

In this study, the pre-stress characteristics of magnetic rheological rubber, an intelligent material widely applied to mechanical systems, are measured. Intelligent materials are substances that change their properties in response to external inputs and are extensively used in mechanical systems. Magnetic rheological rubber is a representative intelligent material that can exhibit variable characteristics depending on the conditions. When measuring the physical properties of magnetic rheological rubber, it is placed in a magnetic field application device, where a magnetic field is applied, and the material is subjected to pre-stress. Similarly, when manufacturing intelligent mechanical systems using magnetic rheological rubber, pre-stress is induced by components used to apply the magnetic field. Generally, when a material is subjected to pre-stress, its properties change. Consequently, the performance of magnetic rheological rubber under pre-stress also varies. If the characteristics of the material under pre-stress change, the expected performance during design may deviate, leading to differences in the mechanical system's performance from the intended design. This variability makes it challenging to design mechanical systems based on intelligent materials, highlighting the importance of experimentally investigating their characteristics. Therefore, this study measures and identifies the pre-stress characteristics of magnetic rheological rubber under pre-stress. These findings can be applied to improve the measurement methods and design approaches for magnetic rheological rubber in pre-stressed conditions.