Abstract
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.
Highlights
With the developments in artificial intelligence; researchers have a great deal of attention to the solution of non-linear problems in physical and mechanical properties of metal alloys [1]
The produce of composites, Ni-Ti alloys to have been used with powder metallurgy method and which produce of composites, determine of using field important to present [2]
Diffusion bonding is a solid state coalescence of contacting surfaces occurs at a temperature below the melting point (0,5-0,7 Tm) of the materials to be joined with the loads and the period, below those that would cause macro deformation and a significant properties change at the parent materials
Summary
With the developments in artificial intelligence; researchers have a great deal of attention to the solution of non-linear problems in physical and mechanical properties of metal alloys [1]. Joining of the powder metallurgy products (P/M) by diffusion bonding process is important both to protect the microstructural properties of parent materials and bonding behavior of joining materials [3]. Diffusion bonding is a solid state coalescence of contacting surfaces occurs at a temperature below the melting point (0,5-0,7 Tm) of the materials to be joined with the loads and the period, below those that would cause macro deformation and a significant properties change at the parent materials. Egercioglu et al were investigated prediction of martensite and austenite start temperatures of the Febased shape memory alloys by artificial neural networks [8]. Features of multi layer perceptron architecture with backpropagation learning algorithm were employed to predict the shear strength of diffusion bonding behavior of Ni-Ti alloys manufactured by P/M process
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