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

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.

[1]  Livan Fratini,et al.  CDRX modelling in friction stir welding of aluminium alloys , 2005 .

[2]  Livan Fratini,et al.  Design of the friction stir welding tool using the continuum based FEM model , 2006 .

[3]  Modeling mixed microstructures using a multi-level cellular automata finite element framework , 2010 .

[4]  Patrick Ulysse,et al.  Three-dimensional modeling of the friction stir-welding process , 2002 .

[5]  C. Gandin,et al.  A coupled finite element-cellular automaton model for the prediction of dendritic grain structures in solidification processes , 1994 .

[6]  V. Balasubramanian,et al.  Influences of tool pin profile and axial force on the formation of friction stir processing zone in AA6061 aluminium alloy , 2008 .

[7]  Rajiv S. Mishra,et al.  Friction Stir Welding and Processing , 2007 .

[8]  C. Davies.  The effect of neighbourhood on the kinetics of a cellular automaton recrystallisation model , 1995 .

[9]  Lukasz Madej,et al.  Development of the Multi-scale Analysis Model to Simulate Strain Localization Occurring During Material Processing , 2009 .

[10]  V. Balasubramanian,et al.  Influences of tool pin profile and tool shoulder diameter on the formation of friction stir processing zone in AA6061 aluminium alloy , 2008 .

[11]  Sumitesh Das,et al.  Cellular automata finite element (CAFE) model to predict the forming of friction stir welded blanks , 2012 .

[12]  Carter Hamilton,et al.  A thermal model of friction stir welding in aluminum alloys , 2008 .

[13]  John H. Beynon,et al.  Extending the local approach to fracture: methods for direct incorporation of microstructural effects into finite element models of fracture , 2002 .

[14]  I. C. Howard,et al.  A combined neuro fuzzy-cellular automata based material model for finite element simulation of plane strain compression , 2007 .

[15]  Basil M. Darras,et al.  Analytical Modeling of Strain Rate Distribution During Friction Stir Processing , 2008 .

[16]  Pasquale Buonadonna,et al.  Friction-stir welding of AA 2198 butt joints: mechanical characterization of the process and of the welds through DOE analysis , 2011 .

[17]  V. Balasubramanian,et al.  Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints , 2009 .

[18]  L. Fratini,et al.  CDRX modelling in friction stir welding of AA7075-T6 aluminum alloy : Analytical approaches , 2007 .

[19]  Erol Arcaklioğlu,et al.  Artificial neural network application to the friction stir welding of aluminum plates , 2007 .

[20]  C. Muralidharan,et al.  Predicting tensile strength, hardness and corrosion rate of friction stir welded AA6061-T6 aluminium alloy joints , 2011 .

[21]  Amitava De,et al.  Strains and strain rates during friction stir welding , 2009 .

[22]  John H. Beynon,et al.  The combination of cellular automata and finite elements for the study of fracture; the CAFE model of fracture , 2002 .

[23]  Fabrizio Micari,et al.  Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys , 2012 .

[24]  Radovan Kovacevic,et al.  Finite element modeling of friction stir welding—thermal and thermomechanical analysis , 2003 .

[25]  J. T. Chen,et al.  3D modeling of material flow in friction stir welding under different process parameters , 2007 .

[26]  Chengwu Zheng,et al.  Microstructure prediction of the austenite recrystallization during multi-pass steel strip hot rolling: A cellular automaton modeling , 2008 .

[27]  Sumitesh Das,et al.  A general method for coupling microstructural response with structural performance , 2006, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[28]  Ibrahim N. Tansel,et al.  Optimizations of friction stir welding of aluminum alloy by using genetically optimized neural network , 2010 .

[29]  Livan Fratini,et al.  Using a neural network for predicting the average grain size in friction stir welding processes , 2009 .

[30]  Radovan Kovacevic,et al.  Thermal modeling of friction stir welding in a moving coordinate system and its validation , 2003 .

[31]  Livan Fratini,et al.  Numerical procedure for residual stresses prediction in friction stir welding , 2011 .