원문정보
Machine Learning Approach for Predicting Shear modulus of Natural Rubber Magnetorheological Elastomers
초록
영어
This study compares the shear behavior of anisotropic magnetorheological elastomers (MREs) using natural rubber (NR) and silicone rubber (Si) as matrices. The effects of magnetic flux density and compressive pre-stress on the shear modulus were experimentally investigated. Results showed that silicone-based MREs exhibited a 10–20% higher magnetorheological effect than NR-based ones due to stronger particle–matrix bonding and stable chain alignment under magnetic fields. In contrast, NR-based MREs showed greater stiffness variation under compressive stress, attributed to strain-hardening and volumetric constraint effects. These findings indicate that matrix selection significantly governs the magneto-mechanical response: silicone MREs are suitable for precision control and sensing, while NR MREs perform better in high-stress damping systems. This study provides fundamental insight for tailoring MREs according to design requirements.
목차
1. 서론
2. 이론적 배경(Theoretical Background)
2.1 기존 모델링 접근법
2.2 데이터 기반 접근법
3. 실험 데이터 및 전처리(Experimental Data and Preprocessing)
3.1 실험 구성 및 데이터 수집
3.2 데이터 전처리 및 가공
3.3 데이터 특성 분석
4. 머신러닝 기반 예측 결과
4.1 Random Forest 모델 및 학습 결과
5. 결론
References