The isothermal oxidation kinetics of a Co-40Cr alloy and its yttrium ion-implanted samples were studied at 1000℃ in air by thermal-gravity analysis （TGA）. Scanning electronic microscopy （SEM） was used to examine the Cr203 oxide film＇s morphology after oxidation. An acoustic emission （AE） method was used in situ to monitor the cracking and spalling of oxide films formed on samples during oxidation and subsequent aircooling stages. A theoretical model was proposed relating to the film fracture process and was used to analyze the acoustic emission spectrum on time domain and the AE-event number domain. It was found that yttrium implantation remarkably reduced the isothermal oxidation rate of Co-40Cr and improved the anti-cracking and anti-spalling properties of Cr2O3 oxide film. The reasons for the improvement were mainly that the implanted yttrium reduced the grain size of Cr2O3 oxide, increased the high temperature plasticity of oxide film, and remarkably reduced the number and size of Cr203/Co-40Cr interfacial defects.
Due to the non-linearity behavior of the precision positioning system, an accurate mathematical control model is difficult to set up, a novel control method for ultra-precision alignment is presented. This method relies on neural network and alignment marks that are in the form of 100μm pitch gratings. The 0-th order Moire signals＇ intensity and its intensity rate are chosen as input variables of the neural network. The characteristics of the neural network make it possible to perform self-training and self-adjusting so as to achieve automatic precision alignment. A neural network model for precision positioning is set up. The model is composed of three neural layers, i.e. input layer, hidden layer and output layer. Driving signal is obtained by mapping Moire signals＇ intensity and its intensity rate. The experimental results show that neural network control for precision positioning can effectively improve positioning speed with high accuracy. It has the advantages of fast, stable response and good robustness. The device based on neural network can achieve the positioning accuracy of ± 0. 5μm.
