Artificial neural networks and response surface methodology as a tool for analysis the spindle torque in FSP process

by

Artificial neural networks and response surface methodology as a tool for analysis the spindle torque in FSP process

Marek S. Węglowski

Institute of Welding, Bl. Czesława Str. 16-18, 44-100 Gliwice.

DOI:

https://doi.org/10.7494/cmms.2015.1.0504

Abstract:

The article presents the effect of rotational and travelling speeds and down force on the spindle torque acting on the tool in friction stir processing (FSP) process. To find a dependence combining the spindle torque acting on the tool with the rotational speed, travelling speed and the down force, the artificial neural networks (ANN) and response surface methodology (RSM) were applied. Good correlation between experimental set and model was achieved. The best results were gained for the multilayer perceptron type 3-9-1. The results obtained in artificial neural network were compared with those through response surface methodology. Based on achieved results ANN, quadratic and linear models can be recommended to predict the value of spindle torque acting on the tool during FSP process carry out on alloy AlSi9Mg.

Cite as:

Węglowski, M. (2015). Artificial neural networks and response surface methodology as a tool for analysis the spindle torque in FSP process. Computer Methods in Materials Science, 15(1), 65-70. https://doi.org/10.7494/cmms.2015.1.0504

Article (PDF):

Keywords:

Friction stir processing, Artificial neural networks, Response surface methodology, Cast aluminium alloy

Publication dates:

Received: 05.10.2014, accepted: 12.11.2014, published: 28.12.2014

Publication type:

Original scientific paper

References:

Asadi, P., Besharati Givi, M.K., Rastgoo, A., Akbari, M., Zakeri, V., Rasouli, S., 2012, Predicting the Grain Size and Hardness of AZ91/SiC Nanocomposite by Artificial Neural Networks, Int J Adv Manuf Tech, 63, 1095-1107.

Buffa, G., Fratini, L., Micari, F., 2012, Mechanical and Microstructural Properties Prediction by Artificial Neural Networks in FSW Process of Dual Phase Titanium Alloys, Journal of Manufacturing Processes, 14, 289-296.

Charit, I., Mishra, R.S., 2005, Low Temperature Superplasticity in a Friction Stir Processed Ultrafine Grained Al-Zn-Mg-Sc Alloy, Acta Mater, 53, 4211-4223.

Darras, B.M., 2005, Experimental and analytical study of friction stir processing. PhD thesis University of Kentucky, Lexington.

Ebnonnasir, A., Karimzadeh, F., Enayati, M.H., 2011, Novel Artificial Neural Network Model for Evaluating Hardness of Stir Zone of Submerge Friction Stir Processed Al 6061-T6 Plate, J Mater Sci Technol, 27, 990-995.

Elangovan, K., Balasubramanian, V., Babu, S., 2008, Developing an Empirical Relationship to Predict Tensile Strength of Friction Stir Welded AA2219 Aluminum Alloy, J Mater Eng Perform, 16, 820-830.

Kovacevic, R., Mijajlovic, M., Milcic, D., 2012, Analytical Model for Estimating the Amount of Heat Generated during Friction Stir Welding: Application on Plates Made of Aluminium Alloy 2024 T351, Welding Processes, InTech, Rijeka, Croatia, 247-274.

Ma, Z.Y., 2008, Friction Stir Processing Technology: a Review, Metall Mater Trans A, 39a, 642-658.
Neto, D.M., Neto, P., 2013, Numerical Modeling of Friction Stir Welding Process: a Literature Review, Int J Adv Manuf Tech, 65, 115-126.

Okuyucu, H., Kurt, A., 2007, Artificial neural network application to the friction stir welding of aluminum plates, Mater Design, 28, 78-84.

Palanivel, R., Koshy Mathews, P., Murugan, N., 2011, Development of mathematical model to predict the mechanical properties of friction stir welded AA6351 aluminum alloy, Journal of Engineering Science and Technology Review, 4, 25-31.

Palanivel, R., Koshy Mathews, P., Murugan, N., Dinaharan, I., 2012, Predict and optimization of wear resistance of friction stir welded dissimilar aluminum alloy, Procedia Engineering, 38, 578-584.

Tansel, I.N., Demetgul, M., Okuyucu, H., Yapici, A., 2010, Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural networks, Int J Adv Manuf Tech, 48, 95-101.

Venkateswarlu, G., Davidson, M.J., Tagore, G.R.N., 2012, Analysis of sheet metal formability studies of friction stir processes Mg AZ31B alloy using response surface methodology, Procedia Engineering, 38, 2228-2236.

Węglowski, M.St., 2011, Technologia Friction Stir Processing – nowe możliwości, Biuletyn Instytutu Spawalnictwa, 55, 25-31 (in Polish).

Węglowski, M.St., Dymek, S., 2012, Microstructural modification of cast aluminium alloy AlSi9Mg via Friction Modified Processing. Arch Metall Mater, 57, 71-78.

Węglowski, M.St., Dymek, S., Hamilton, C., 2013, Experimental investigation and modelling of Friction Stir Processing of cast aluminium alloy AlSi9Mg, Bulletin of the Polish Academy of Sciences-Technical Sciences, 61, 893-904.

Węglowski, M.St., 2013, An experimental study on the Friction Stir Processing process of aluminium alloy, Key Eng Mat, 554-557, 1787-1792.

Węglowski, M.St., Dymek, S., 2013, Relationship between Friction Stir Processing parameters and torque, temperature and the penetration depth of the tool, Arch Civ Mech Eng, 13, 186-191.