Artificial Neural Network Modelling for Polyethylene FSSW Parameters

Document Type : Article

Authors

1 Marmara University, School of Applied Sciences, Istanbul 34722, Turkey

2 Industrial Engineering, Engineering Faculty, Industrial Engineering, Sakarya University, Sakarya, Turkey

Abstract

In friction stir spot welding (FSSW) process the welding parameters (the tool rotational speed, the tool plunge depth and the stirring time) effect the nugget formation in high density polyethylene (HDPE) sheets. The size and the microstructure of the nugget determine the resistance of the joint to outer forces. The optimization of these parameters are vital to obtain high quality welds. Feed forward back propagation artificial neural network models are developed for the optimization of the FSSW parameters for HDPE sheets. Input variables of these models are the tool rotation speed (rpm), the plunge depth (mm) and the stirring time (s) affect to lap-shear fracture load (N) output. Prediction performance of 6 models in different specifications are compared. These models differ in terms of the training dataset used (80%-100%) and the number of neurons (5-10-20) in hidden layer. Best prediction performances are obtained with using 20 neurons in hidden layer in both training dataset. There is a good agreement between developed models’ predictions and the experimental data.

Keywords

Main Subjects


References
1. Lathabai, S., Painter, M.J., and Cantin, G.M.D. Friction
stir spot welding of automotive lightweight alloys",
Proceedings of the 7th International Conference
on Trends in Welding Research, May 16-20, Georgia
USA (2005).
2. Lambiase, F., Paoletti, A., and Ilio, D. Mechanical
behaviour of friction stir spot welds of polycarbonate
sheets", The International Journal of Advanced Manufacturing
Technology, 80, pp. 301-314 (2015).
3. Kurtulmus, M. Friction stir spot welding parameters
for polypropylene sheets", Scienti c Research and
Essays, 7(8), pp. 947-956 (2012).
4. Bilici, M.K. and Yukler, A.I. E ects of welding
parameters on friction stir spot welding of high density
polyethylene sheets", Materials and Design, 33, pp.
545-550 (2012).
5. Bilici, M.K. and Yukler, A.I. In
uence of tool geometry
and process parameters on macrostructure and
static strength in friction stir spot welded polyethylene
sheets", Materials and Design, 33, pp. 145-152 (2012).
6. Chandrasekhar, N. and Vasudevan, M. Intelligent
modeling for optimization of A-TIG welding process",
Materials and Manufacturing Processes, 25, pp. 1341-
1350 (2010).
7. Acherjee, B., Mondal, S., Tudu, B., and Misra, D.
Application of arti cial neural network for predicting
weld quality in laser transmission welding of thermoplastics",
Applied Soft Computing, 11(2), pp. 2548-
2553 (2011).
8. Anand, K., Barik, B.K., Tamilmannan, K., and
Sathiya, P. Arti cial neural network modeling studies
to predict the friction welding process parameters of
Incoloy 800H joints", Engineering Science and Technology,
an International Journal, 18, pp. 394-407
(2015).
9. Okuyucu, H., Kurt, A., and Arcaklioglu, E. Arti cial
neural network application to the friction stir welding
of aluminum plates", Materials and Design, 28, pp.
78-84 (2007).
10. Davraz, M., Kilincarslan, S., and Ceylan, H. Predicting
the Poisson ratio of lightweight concretes using
arti cial neural network", Acta Physica Polonica A,
128(2B), p. 184 (2015).
11.  Ozcanli, Y., Kosovali C avus, F., and Beken, M.
Comparison of mechanical properties and arti cial
neural networks modeling of PP/PET blends", ACTA
Physica Polonica A, 130(1), p. 444 (2016).
12. Tekin, H.O., Manici, T., Altunsoy, E.E., Yilancioglu,
K.K., and Yilmaz, B. An arti cial neural networkbased
estimation of Bremsstarahlung photon
ux calculated
by MCNPX", ACTA Physica Polonica A,
132(3), p. 967 (2017).
13. Bilici, M.K., Yukler, A.I., and Kurtulmus, M. The
optimization of welding parameters for friction stir
spot welding of high density polyethylene sheets",
Materials and Design, 32, pp. 4074-4079 (2011).
14. Bilici, M.K. Application of Taguchi approach to
optimize friction stir spot welding parameters of
polypropylene", Materials and Design, 35, pp. 113-119
(2012).
15. Chavan, A. and Shete, M.T. Optimization of friction
stir spot welding process using arti cial neural
network", International Journal of Science Technology
and Engineering, 1, pp. 353-358 (2015).