Lefei Shao

The suspension system of a commercial vehicle cab plays a crucial role in enhancing ride comfort by mitigating vibrations. However, conventional rubber suspension systems have relatively fixed stiffness and damping properties, rendering them inflexible to load variations and resulting in suboptimal ride comfort under extreme road conditions. Shape memory alloys (SMAs) represent an innovative class of intelligent materials characterized by superelasticity, shape memory effects, and high damping properties. Recent advancements in materials science and engineering technology have focused on rubber-based SMA composite dampers due to their adjustable stiffness and damping through temperature or strain rate. This paper investigates how various structural parameters affect the stiffness and damping characteristics of sleeve-type rubber-based SMA composite vibration dampers. We developed a six-degree-of-freedom vibration differential equation and an Adams multi-body dynamics model for the rubber-based SMA suspension system in commercial vehicle cabins. We validated the model’s reliability through theoretical analysis and simulation comparisons. To achieve a 45% increase in stiffness and a 64.5% increase in damping, we optimized the suspension system’s z-axis stiffness and damping parameters under different operating conditions. This optimization aimed to minimize the z-axis vibration acceleration at the driver’s seat. We employed response surface methodology to design the composite shock absorber structure and then conducted a comparative analysis of the vibration reduction performance of the optimized front and rear suspension systems. This study provides significant theoretical foundations and practical guidelines for enhancing the performance of commercial vehicle cab suspension systems.

Shock absorbers are essential in enhancing vehicle ride comfort by mitigating vibrations. However, traditional rubber shock absorbers are constrained by their fixed stiffness and damping properties, limiting their adaptability to varying loads and thus affecting the ride comfort, especially under extreme road conditions. Shape Memory Alloys (SMAs), known for their intelligent material properties, offer a unique solution by adjusting stiffness and damping in response to temperature changes or strain rates, making them ideal for advanced vibration control applications. This study builds upon the Auricchio constitutive model to propose an enhanced SMA hyper-elastic constitutive model that accounts for different loading rates. This new model elucidates the impact of loading rates on the stiffness and damping characteristics of SMAs. Additionally, we introduce an innovative circular rubber-based SMA composite vibration reduction structure. Through a parameterized model and finite element simulation, we comprehensively analyze the stiffness and damping properties of the composite damper under various loading rates and harmonic excitations. Our findings suggest a novel approach to improving the vehicle ride comfort, offering significant potential for engineering applications and practical value.

Prediction of rock fracturing capacity demands particular requirements for the exploitation of mineral resources, especially for the parameter design of conical pick performance for hard rock fragmentation, which must take into account differences in rock mechanical properties. Among these parameters, the peak cutting force (PCF) is important in designing, selecting, and optimizing the cutting head of mining equipment and a cutability index of rocks. Taking high lithological tolerance as demand traction, this study proposes a theoretical model for estimating the peak cutting force of conical picks based on the improved projection profile method for which the influence of alloy head, pick body structure, and installation parameters are taken into consideration. Besides, experimental results corresponding to different numbers of rock samples are used to verify the accuracy and stability of the theoretical model. Meanwhile, the comparison of performance in cutting force estimation between this model and four other existing theoretical models is conducted. The results found that the new method has the highest correlation coefficient with the experimental results and the lowest root mean square error comparing with other models, i.e., the estimation performance of this method has high lithological tolerance when the rock type increases and the lithology changes. Consequently, the proposed peak cutting force estimation of improved projection profile method will provide a more valid and accurate prediction for rock fracturing capacity with large differences in rock mechanical properties.