Search

This study investigated the influence of the wax layer on the micro-friction behavior of the energetic crystal surface by constructing the lubricating interface between wax and β-HMX crystals. The nano-scratch testing of β-HMX crystals with and without wax coating was conducted under the ramp-loading (0–3.5 ​mN) mode and the constant-loading (0.4 ​mN) mode. The testing results are as follows. Compared with the dry friction of β-HMX crystals without wax coating, wax can significantly improve the contact friction between the energetic crystals and the rigid micro-convex body. The influence of wax on the interface friction highly depends on the external load conditions. Under the ramp-loading mode, the wax layer can greatly reduce the average coefficient of friction (COF) from ∼0.7 (dry friction) to ∼0.2 (lubricating interface) and inhibit COF fluctuation. The inherent mechanism of wax reducing the interface friction works in the following way. Wax can effectively suppress the plowing effect of the rigid micro-convex body on the β-HMX crystal surface and further inhibit the occurrence of brittle fracture and crack defects. The wax layer can also suppress the friction anisotropy of the β-HMX crystal surface, significantly reducing the probability of high COF (>0.4). The distribution probabilities of COF in the range of 0–0.1 and 0–0.3 were 33% and 89%, respectively. The stick-slip effect of the friction behavior was observed under the constant-loading mode, with the COF varying periodically with the sliding distance. These study results can help understand the desensitization mechanisms of wax on the energetic crystal surface at a micro-scale quantitative level and provide a necessary basis for building the friction hot-spot model under dry friction and wax lubrication.

The stick-slip is one of negative phenomena caused by friction in servo systems. It is a consequence of complicated nonlinear friction characteristics, especially the so-called Stribeck effect. Much research has been done on control algorithms suppressing the stick-slip, but no simple solution has been found. In this work, a new approach is proposed based on genetic programming. The genetic programming is a machine learning technique constructing symbolic representation of programs or expressions by evolutionary process. In this way, the servo control algorithm optimally suppressing the stick-slip is discovered. The GP training is conducted on a simulated servo system, as the experiments would last too long in real-time. The feedback for the control algorithm is based on the sensors of position, velocity and acceleration. Variants with full and reduced sensor sets are considered. Ideal and quantized position measurements are also analyzed. The results reveal that the genetic programming can successfully discover a control algorithm effectively suppressing the stick-slip. However, it is not an easy task and relatively large size of population and a big number of generations are required. Real measurement results in worse control quality. Acceleration feedback has no apparent impact on the algorithms performance, while velocity feedback is important.