No abstract

Bulletproof ceramics are usually hard and brittle with high elastic modulus, high compressive strength, and low tensile strength. While machining bulletproof ceramics, severe tool wear makes it difficult to obtain desired machining quality and efficiency, especially in hole drilling. In this work, an intensive experimental study on the overall wear rate of the sintering diamond thin-wall core bit during the hole drilling of Al2O3 bulletproof ceramics (99 wt.%) has been carried out. The quality loss of the bit after each hole drilled was selected for representing the overall wear rate of the bit. Based on experimental data, the influences of the main bit performance and machining process parameters on the overall wear rate of the bit have been analyzed. According to the results discussed, under the test conditions, finer diamond grit, higher diamond concentration, lower number of water gaps, thinner wall thickness, or lower bit load all can decrease the wear rate of the bit. However, within a certain range, the spindle speed has little influence on the overall wear resistance of the bit, but when the spindle speed increases, the machining efficiency can be significantly improved. The results obtained in this work can offer a valuable reference for the use of sintering diamond thin-wall core bits in the hole drilling of bulletproof ceramics.

Surface electromyographic (sEMG) signals always change with the external and internal conditions of human beings. Such a time-varying characteristic leads to decreasing classification accuracy of fixed-parameter classifiers for EMG patterns with time. To design a control system for EMG-based artificial limbs with stable performance, it is necessary to introduce the adaptive mechanism in the classifiers for EMG patterns. In addition, there are many uncertainties in the process of EMG signal acquisition and grasp model recognition. In this paper, on the basis of a distance classifier based on probabilistic fuzzy set, we attempted to introduce the adaptive scheme to the classifiers for EMG patterns and then verified the application of the scheme in the classification of EMG patterns through experiments. The study shows that a self-enhancement distance classifier based on probabilistic fuzzy set can improve recognition accuracy.