Effects of HfB2 and HfN Additions on the Microstructures and Mechanical Properties of TiB2-Based Ceramic Tool Materials
The effects of HfB2 and HfN additions on the microstructures and mechanical properties of TiB2-based ceramic tool materials were investigated. The results showed that the HfB2 additive not only can inhibit the TiB2 grain growth but can also change the morphology of some TiB2 grains from bigger polygons to smaller polygons or longer ovals that are advantageous for forming a relatively fine microstructure, and that the HfN additive had a tendency toward agglomeration. The improvement of flexural strength and Vickers hardness of the TiB2-HfB2 ceramics was due to the relatively fine microstructure; the decrease of fracture toughness was ascribed to the formation of a weaker grain boundary strength due to the brittle rim phase and the poor wettability between HfB2 and Ni. The decrease of the flexural strength and Vickers hardness of the TiB2-HfN ceramics was due to the increase of defects such as TiB2 coarse grains and HfN agglomeration; the enhancement of fracture toughness was mainly attributed to the decrease of the pore number and the increase of the rim phase and TiB2 coarse grains. The toughening mechanisms of TiB2-HfB2 ceramics mainly included crack bridging and transgranular fracture, while the toughening mechanisms of TiB2-HfN ceramics mainly included crack deflection, crack bridging, transgranular fracture, and the core-rim structure.
Synthesis and characteristic of SiBCN/HfN ceramics with high temperature oxidation resistance
Abstract SiBCN/HfN ceramics were prepared by crosslinking and pyrolysis of Hf-modified polyborosilazanes (PBSZHf) at 1100 °C. High temperature oxidation resistance of SiBCN/HfN ceramics was evaluated by heat-treatment at 1100–1500 °C in air. The chemical nature of SiBCN/HfN was investigated by Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR). It was shown in X-ray diffraction (XRD) patterns that HfN was obtained in the SiBCN matrix after pyrolysis at 1100 °C under argon. Transmittance electron microscopy (TEM) analysis indicated that HfN nano-crystals with diameter range of 10–50 nm were well dispersed in SiBCN matrix. Thermal gravimetric analysis (TGA) showed that the weight loss of SiBCN/HfN was 2.8 wt % at 1500 °C in air which was lower than that of pure SiBCN (5.4 wt %), indicating the fabricated SiBCN/HfN ceramics exhibited high temperature oxidation resistance. The mechanism could be analyzed as follows: i) The nitrogen consumption as well as HfN formation can improve high temperature stability; ii) The composite oxidative products SiCOx- HfO2– HfSiO4 generated at 1500 °C in air acted as an oxide layer on the surface and prevented the SiBCN/HfN ceramics from oxidation further.
A hybrid algorithm to optimize cutting parameter for machining GFRP composite using alumina cutting tools
In this paper, two different evolutionary algorithm-based neural network models were developed to optimise the unit production cost. The hybrid neural network models are, namely, genetic algorithm-based neural network (GA-NN) model and particle swarm optimization-based neural network (PSO-NN) model. These hybrid neural network models were used to find the optimal cutting conditions of Ti[C,N] mixed alumina-based ceramic cutting tool (CC650) and SiC whisker-reinforced alumina-based ceramic cutting tool (CC670) on machining glass fibre-reinforced plastic (GFRP) composite. The objective considered was the minimization of unit production cost subjected to various machine constraints. An orthogonal design and analysis of variance was employed to determine the effective cutting parameters on the tool life. Neural network helps obtain a fairly accurate prediction, even when enough and adequate information is not available. The GA-NN and PSO-NN models were compared for their performance. Optimal cutting conditions obtained with the PSO-NN model are the best possible compromise compared with the GA-NN model during machining GFRP composite using alumina cutting tool. This model also proved that neural networks are capable of reducing uncertainties related to the optimization and estimation of unit production cost.