Artificial Neural Network Modelling on TiN Coating Parameters in Sputtering Process

Sputtering is a Physical Vapor Deposition (PVD) vacuum process used to deposit very thin films onto a substrate for a wide variety of commercial and scientific purposes. Due to this coating process the material performance will be improved. The objective of this study is to evaluate the hardness of titanium nitride thin film layers by utilizing multi layer perceptron (MLP) artificial neural networks (ANN). For determining the influences of the various sputtering parameters (voltage, work pressure, ion bombard time and Sub-layer temperature) on the hardness of TiN thin films, the Taguchi approach has been used. 50 experiments were performed, varying the PVD parameters and the resulting hardness of the film was measured. From these experiments 42 were used for the training process and 8 have been utilized for validation process. A very good agreement between the ANN predictions and the experimental results was achieved, with a 99.754% correlation between the trained ANN result and the experimental measurements

Tool vibration prediction and optimisation in face milling of Al 7075 and St 52 by using neural networks and genetic algorithm

Tool vibration generated under unsuitable cutting conditions is an extremely serious problem during face milling as it causes excessive tool wear, noise, tool breakage, and deterioration of the surface quality. In the current study, an artificial neural network (ANN) was used to predict tool vibration stability during face milling for different materials: Al 7075 and St 52. The testing of the ANN after training had a correlation of 99.206% with experimentally determined results. A generic algorithm (GA) was then used to minimise the vibration experienced during face milling and machining was performed using the GA recommended parameters. Measurement of the vibration during machining showed that the GA had a calculated error of 0.124%

Induced conductivity in sol-gel ZnO films by passivation or elimination of Zn vacancies

Undoped and Ga- and Al- doped ZnO films were synthesized using sol-gel and spin coating methods and characterized by X-ray diffraction, high-resolution scanning electron microscopy (SEM), optical spectroscopy and Hall-effect measurements. SEM measurements reveal an average grain size of 20 nm and distinct individual layer structure. Measurable conductivity was not detected in the unprocessed films; however, annealing in hydrogen or zinc environment induced significant conductivity (∼10−2 Ω.cm) in most films. Positron annihilation spectroscopy measurements provided strong evidence that the significant enhancement in conductivity was due to hydrogen passivation of Zn vacancy related defects or elimination of Zn vacancies by Zn interstitials which suppress their role as deep acceptors. Hydrogen passivation of cation vacancies is shown to play an important role in tuning the electrical conductivity of ZnO, similar to its role in passivation of defects at the Si/SiO2 interface that has been essential for the successful development of complementary metal–oxide–semiconductor (CMOS) devices. By comparison with hydrogen effect on other oxides, we suggest that hydrogen may play a universal role in oxides passivating cation vacancies and modifying their electronic properties