چکیده مقاله
Friction Stir Processing FSP of AZ31 with nano sized TiN particulates was employed to produce AZ31/TiN nano composite, and hardness values were measured using Vichers indenter An Artificial Neural Network ANN was applied to acquire the relationships between hardness and the processing parameters of FSP In establishing of those relationships, tool rotation & substrate advancing speeds and number of FSP passes were taken as the inputs, whilst the hardness was presented as the output The network was trained using the data from experimental work It was found that the trained neural network could predict the hardness quite reliably and the optimum processing parameters can be quickly selected to achieve the desired hardness using the prediction based on the ANN model
کلیدواژهها
نویسندگان
M Mokhtari
School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran
S. F. Kashani-Bozorg
School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran
M Sharififar
School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran, Tehran, Iran
شیوه ارجاع
Mokhtari, M and Kashani-Bozorg, S. F. and Sharififar, M,1390,Prediction of Mechanical Properties of Friction Stir Processed AZ31 Using Neural Network,12th Iranian Conference on Manufacturing Engineering (ICME 2010),Tehran
ارائهشده در
مجموعه مقالات دوازدهمین کنفرانس ملی مهندسی ساخت و تولید ایران6 دی 1390 · تهران