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In this study, Artificial Neural Network approach to prediction of diffusion bonding behavior of Ni-Ti alloys, manufactured by powder metallurgy process, were obtained using a back-propagation neural network that uses gradient descent learning algorithm. Ni-Ti composite manufactured with a chemical composition of 51 % Ni-49 % Ti in weight percent as mixture with a average dimension of 45µm. Diffusion welding process have been made under argon atmosphere, with a constant load of 5 MPa, under the temperature of 850, 875, 900 and 925ºC and, in 20, 40 and 60 minutes experiment time. Microstructure examination at bond interface were investigated by optical microscopy, SEM and EDS analysis. Specimens were tested for shear strength and metallographic evaluations. After the completion of experimental process and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. In neural networks training module, different temperatures and welding periods were used as input, shear strength of bonded specimens at interface were used as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of diffusion bonding shear strength and behavior.

In this study, Hardox 400 steel used as substrate material was coated through solid media Thermoreactive Diffusion (TRD) method using Ferro Niobium and Ferro Boron powders from carbide forming element powders. Coating was carried out in three different temperatures ([Formula: see text]C, [Formula: see text]C and [Formula: see text]C) and three different time intervals (1, 2 and 3[Formula: see text]h). Microstructures of the coated specimens were examined by optical microscope, Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDX) and X-ray Diffraction (XRD); and hardness values were measured. The effects of coating parameters on coating thickness and hardness were analyzed by ANOVA. In addition, specimens were subjected to wear tests to determine the effect of hardness and coating parameters on wear. In the wear tests, Taguchi test design setup was used. The obtained results were compared with the Hardox 400 steel used under current conditions. It was seen from optical microscope and SEM images that Hardox 400 steel surface could be coated with TRD method depending on coating parameters. The average thickness of NbC–B coating ranged from 1.797[Formula: see text][Formula: see text]m to 5.596[Formula: see text][Formula: see text]m under different process temperature and time. Rising the coating time and temperature increased the coating thickness by 311.40%. EDX analysis showed that the coating layer was composed of B, C, Fe and Nb elements, and XRD analysis also showed that the phase in the coating layer is NbC–B. The NbC–B phase was determined to be an important factor in increasing the hardness. The coating hardness is enhanced by 320.80% depending on the coating parameters. Optimum coating thickness, hardness and wear results were obtained from high coating temperature and time. Uncoated Hardox 400 steels were worn out more compared to the coated Hardox 400 steels. The contribution of coating temperature and time to wear resistance was 1.46% and 8.02%, respectively. It was observed that the important parameter for wear volume was the applied load.