Artificial Neural Network (ANN) Approach to Prediction of Diffusion Bonding Behavior (Shear Strength) of Ni-Ti Alloys Manufactured by Powder Metalurgy Method

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.

Estimation and optimization of shear strength for compacted iron powders by means of soft computing paradigms

“…Since the experimental shear strength data set in this study covers quantities up to 75, a testing root mean square error of 3.86 is satisfactory and about 5% of data range. The trained values showed 7.72% mean absolute error which is in good agreement with 6.65% reported by Taskin et al [15].…”

“…For instance, the effect of particle size and iron content on forming of Al-Fe composite preforms was investigated by Selvakumar et al [12], the moldability of feedstocks used in powder injection modeling was determine by Karatas ß et al [13] and prediction of mechanical properties of compacted molybdenum prealloy was studied by Zare and Vahdati Khaki [14]. Tas ßkin et al performed an investigation on the prediction of shear strength of diffusion bonded Ni-Ti alloys manufactured by powder metallurgy utilizing the neural network approach [15]. However, there are a few reports about the ANN application to predict the green properties of PM materials that influence the strength of the final components [16,17].…”

Prediction of 305-day milk yield in Brown Swiss cattle using artificial neural networks

“…In the past few years there has been a constant increase in interest in neural network modelling in various fields of material science (Taskin et al, 2008). These networks consist of many simple units working in parallel with no central control, and learning takes place by modifying the weights between connections.…”

Investigation of the joining characteristics of Al-B4C composites manufactured by friction welding

“…Some researchers investigated the parameters that influence the welding quality, the strength of the joint, and the hardness of the heat-affected zone (HAZ) [14,15] .…”