CAFE modeling,neural network modeling,and experimental investigation of friction stir welding

Abstract

The main aim of the present work is to develop a cellular automata finite element –artificial neural network (CAFE-ANN) hybrid model to predict the evolution of grain size and yield strength during friction stir welding. The CAFE model was developed by linking CA cells with elements in ABAQUS, a finite element code. Then a neural network was developed and trained appropriately by using the data obtained from the validated CAFE model that predicts the grain size and yield strength. The ANN results are validated. Finally it has been demonstrated that ANN can be used as a ‘virtual machine’ to examine the effect of rotational speed, welding speed, axial force and shoulder diameter on the variation in grain size and yield strength. The temperature and strain-rate distribution predicted by the thermal and strain-rate models are consistent with the existing results. The CAFE model grain size predictions are also accurate as compared to experiments. The grain size and yield strength predictions from ANN coincide well with the experimental values and CAFE model predictions. It has been demonstrated that CAFE-ANN hybrid model can be used as a ‘virtual machine’ to predict and analyze the effect of above said parameters on the grain size and yield strength evolution. The predicted influence is found to agree with some of the available results. In few cases, a slight deviation in the trend is witnessed as compared to literature results.

Keywords

Cellular automata finite element neural network friction stir welding grain size

Get full access to this article

View all access options for this article.

References

Mishra

. Friction stir welding and processing. Mater Sci Eng R 2005; 50: 1–78.

Chen

Kovacevic

. Finite element modeling of friction stir welding − thermal and thermomechanical analysis. Int J Mach Tool Manuf 2003; 43: 1319–1326.

Song

Kovacevic

. Thermal modeling of friction stir welding in a moving coordinate system and its validation. Int J Mach Tool Manuf 2003; 43: 605–615.

Ulysse

. Three-dimensional modeling of the friction stir-welding process. Int J Mach Tool Manuf 2002; 42: 1549–1557.

Arora

Zhang

. Strains and strain rates during friction stir welding. Scripta Mater 2009; 61: 863–866.

Darras

Khraisheh

. Analytical modeling of strain rate distribution during friction stir processing. J Mater Eng Perform 2008; 17: 168–177.

Buffa

Ducato

Fratini

. Numerical procedure for residual stresses prediction in friction stir welding. Finite Elem Anal Design 2011; 47: 470–476.

Buffa

Fratini

Shivpuri

. CDRX modeling in friction stir welding of AA7075-T6 aluminum alloy: analytical approaches. J Mater Process Technol 2007; 191: 356–359.

Buffa

Hua

Shivpuri

. Design of the friction stir welding tool using the continuum based FEM model. Mater Sci Eng A 2006; 419: 381–388.

10.

Fratini

Buffa

. CDRX modeling in friction stir welding of aluminium alloys. Int J Mach Tool Manuf 2005; 45: 1188–1194.

11.

Fratini

Buffa

Palmeri

. Using a neural network for predicting the average grain size in friction stir welding processes. Comput Struct 2009; 87: 1166–1174.

12.

Buffa

Fratini

Micari

. Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys. J Manuf Process (In press).

13.

Okuyucu

Kurt

Arcaklioglu

. Artificial neural network application to the friction stir welding of aluminum plates. Mater Des 2007; 28: 78–84.

14.

Tansel

Demetgul

Okuyucu

. Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network. Int J Adv Manuf Technol 2010; 48: 95–101.

15.

Bitondo

Prisco

Squilace

. Friction-stir welding of AA 2198 butt joints: mechanical characterization of the process and of the welds through DOE analysis. Int J Adv Manuf Technol 2011; 53: 505–516.

16.

Rajakumar

Muralidharan

Balasubramanian

. Predicting tensile strength, hardness and corrosion rate of friction stir welded AA6061-T6 aluminium alloy joints. Mater Des 2011; 32: 2878–2890.

17.

Lakshminarayanan

Balasubramanian

. Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans Nonferrous Mat Soc China 2009; 19: 9–18.

18.

Das

Abbod

Zhu

. A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression. Comput Mater Sci 2007; 40: 366–375.

19.

Das

. Modeling mixed microstructures using a multi-level cellular automata finite element framework. Comput Mater Sci 2010; 47: 705–711.

20.

Zheng

Xiao

. Microstructure prediction of the austenite recrystallization during multi-pass steel strip hot rolling: a cellular automaton modeling. Comput Mater Sci 2008; 44: 507–514.

21.

Davies

CHJ

. The effect of neighborhood on the kinetics of a cellular automaton recrystallisation model. Scripta Metall Mater 1995; 33: 1139–1143.

22.

Gandin

ChA

Rappaz

. A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes. Acta Metall Mater 1994; 42: 2233–2246.

23.

Madej

Hodgson

Pietrzyk

. Development of the multi-scale analysis model to simulate strain localization occurring during material processing. Arch Comput Meth Eng 2009; 16: 287–318.

24.

Saluja

Ganesh Narayanan

Das

. Cellular automata finite element (CAFE) model to predict the forming of friction stir welded blanks. Comput Mater Sci 2012; 58: 87–100.

25.

Hamilton

Dymek

Sommers

. A thermal model of friction stir welding in aluminum alloys. Int J Mach Tool Manuf 2008; 48: 1120–1130.

26.

Das

Palmiere

Howard

. CAFE: a potential tool for modeling thermomechanical processes. 2003. Proceedings of International Conference on Thermomechanical Processing: Mechanics, Microstructure and Control. Cambridge, pp. 296–301.

27.

Beynon

Das

Howard

. Extending the local approach to fracture: methods for direct incorporation of microstructural effects into finite element models of fracture. Vol. 438, 2002. ASME 2002 Pressure Vessels and Piping Conference (PVP2002). Canada, pp. 229–237.

28.

Beynon

Das

Howard

. The combination of cellular automata and finite elements for the study of fracture: the CAFE model of fracture. 2002. Proceedings of the 14th European Conference on Fracture. Krakow, pp. 241–248.

29.

Das

Palmiere

Howard

. Modeling recrystallisation during thermomechanical processing using CAFE. Mater Sci Forum 2004; 467–470: 623–628.

30.

Das

Shterenlikht

Howard

. A general method for coupling microstructural response with structural performance. Math Phys Eng Sci: Proc Roy Soc A 2006; 462: 2085–2096.

31.

Zhang

Chen

. 3D modeling of material flow in friction stir welding under different process parameters. J Mater Process Technol 2007; 183: 62–70.

32.

Elangovan

Balasubramanian

Valliappan

. Influences of tool pin profile and axial force on the formation of friction stir processing zone in AA6061 aluminium alloy. Int J Adv Manuf Technol 2008; 38: 285–295.

33.

Elangovan

Balasubramanian

. Influences of tool pin profile and tool shoulder diameter on the formation of friction stir processing zone in AA6061 aluminium alloy. Mater Des 2008; 29: 362–373.