A heating phenomenon exists in the service process of magneto-rheological devices. For magnetorheological devices using magnetorheological grease (MRG) as the working medium, changes in the medium’s rheological behaviors caused by temperature increase will generate challenges in the reliable service of the devices. With laboratory-prepared MRG as the research object, MRG rheological property laws under different temperatures and magnetic field intensities are investigated, and the magnetic properties of carbonyl iron (CI) powder under different temperatures are analyzed. The relationship between the evolution of the MRG structural system and changes in rheological properties under the action of thermomagnetic coupling is discussed. The results show that the MRG structural system is composed of base carrier liquid soap fibers and magnetic chains under the action of a magnetic field, and the soap fibers can strengthen the structural intensity of the MRG. A decrease in the entanglement degree of the soap fiber structure caused by temperature increase will obviously weaken the intensity of the entire MRG system and narrow the regulation and control scope of the MRG rheological properties. The structural evolution of soap fibers under thermomagnetic coupling will affect the chain formation of magnetic particles and ultimately their rheological properties. At low temperature and magnetic field intensity, the entanglement degree of soap fibers is relatively high, and the magnetic force on magnetic particles is weak, which cannot completely overcome the binding of soap fibers. The influence of soap fibers is the dominant factor affecting the MRG rheological properties. As temperature and magnetic field intensity increase, the entanglement degree of soap fibers decreases, magnetic particles gradually disperse from the soap fibers under the action of a large magnetic field force, and the magnetic chain structure gradually becomes the dominant factor affecting the MRG rheological properties. Moreover, the weakening degree of the temperature increase effect on the MRG rheological properties decreases, and the rheological law changes gradually.

Magnetorheological brake (MRB) provide a potential alternative to traditional mechanical friction brakes in automobile applications owing to their technical advantages in terms of being compact yet powerful, having superior control performance, and wire-control features. However, the temperature effect has been an important issue that should be considered in the design and precise control of MRB. This paper presents the multi-objective optimal design and stability control of an automotive MRB considering the temperature effect. First, a description of the configuration design, magnetostatic field simulation, and mathematical modelling of the automotive MRB is presented in detail. Subsequently, design optimization of the MRB is carried out. The design of experiment method was adopted to screen out the major design variables, and the optimal solution was obtained using a multi-objective genetic algorithm (GA) NSGA-II. Thereafter, the torque output and response performance, as well as the temperature characteristics of the optimal MRB prototype are experimentally evaluated. The results indicate that the optimal design of the MRB was reasonable and effective. Finally, a GA-based back propagation neural network proportion integration differentiation controller is proposed for the stability control of the MRB during automobile braking. Its performance was verified to be satisfactory through both simulations and experiments.

Lubricating grease has increased thermorheological properties during heating, which may affect the lubrication of the friction pair. And a friction pair usually heats up in the working process. This study explored the effect of surface temperature of the friction pair on the lubrication performance under lubrication conditions. The thermorheological properties of lubricating grease were analyzed using a rotational rheometer, and the variations and mechanisms of the thermo-rheological properties were explored. The friction-wear test on lubrication was conducted at different temperatures to examine the effects of thermorheological properties on the tribological behaviors of lubricating grease. Wear scar morphology, composition change, and friction-lubrication mechanisms at different temperatures were probed through SEM and X-ray spectrometer analysis. The results showed that lubricating grease has significant thermorheological properties. Moreover, its soap fiber entanglement decreases with rising temperature, and the entanglement properties are slowly lost at high temperature. The soap fiber structure of lubricating grease plays a vital role in lubrication. As temperature rises, the soap fiber entanglement of lubricating grease decreases and the base oil is more easily released under shear, exhibiting a trend of friction coefficient decreasing with the rising temperature. High temperatures weaken the soap fiber entanglement of lubricating grease, the film-forming property, and the surface friction-abrasion resistance of the friction pair and even cause oxidative wear.

Magnetorheological grease (MRG) is a new type of field-response intelligent material with controllable performance and excellent settlement stability, which is feasible to replace traditional materials. The heating phenomenon of magnetorheological (MR) devices is more common during operation, while the MRG as a medium has more significant thermal rheological characteristics in the heating process. In the process of MRG modeling, a model is established to study the effect of thermal-magnetic coupling on its performance and to save experimental time and reduce costs. Hence, an improved and reliable artificial neural network (ANN) prediction model is established to characterize and predict the relationship among temperature, aging time, magnetic field strength and thermal-rheological properties of MRG. The training data of neural network were obtained from the experiments under the condition of thermomagnetic coupling with rotational rheometer. After the neural network was trained and substituted into the test set data, the predicted results were compared with the experimental results, the correlation coefficient R reached and exceeded 0.95. The results show that the model has excellent prediction accuracy and can provide theoretical reference for the thermal aging behavior of MRG.