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

2012 ◽  
Vol 227 (6) ◽  
pp. 1164-1176 ◽  
Author(s):  
C Patel ◽  
Sumitesh Das ◽  
R Ganesh Narayanan
Keyword(s):  
Neural Network ◽  
Grain Size ◽  
Finite Element ◽  
Friction Stir Welding ◽  
Yield Strength ◽  
Strain Rate ◽  
Virtual Machine ◽  
Hybrid Model ◽  
Neural Network Modeling ◽  
Friction Stir

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.

Prediction of Temperature Evolution During Self-Pierced Riveting of Sheets

2017 ◽  
pp. 381-398 ◽  
Author(s):  
Deepak Mylavarapu ◽  
Manas Das ◽  
Ganesh Narayanan R
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Friction Stir Welding ◽  
Dissimilar Materials ◽  
Temperature Evolution ◽  
Ann Model ◽  
Temperature Estimation ◽  
Friction Stir ◽  
Automotive Components ◽  
The Relationship

Weight reduction of automotive components by tailoring materials is the state of the art. This basically has resulted in the development of advanced joining methods like clinching, friction stir welding, self-pierced riveting etc. to assemble similar or dissimilar materials, with significant change in sheet properties. In the present work, the main aim is to predict the temperature evolution during Self-Pierced Riveting (SPR) of sheets by Finite Element (FE) analyses. Load evolution is also predicted. Generally temperature estimation during SPR is not attempted. The influence of a few selected SPR parameters has been studied on the temperature and load evolution through FE simulations. The relationship between these parameters and the temperature and the load evolution are revealed. Later a neural network model is developed to predict the temperature rise during SPR. The same has been validated at 20 intermediate levels and the predictions are accurate. Thus a hybrid FEM-ANN model for SPR has been developed to predict the SPR outputs efficiently.

scholarly journals Optimum Rotation Speed for the Friction Stir Welding of Pure Copper

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Masoud Jabbari ◽  
Cem C. Tutum
Keyword(s):  
Grain Size ◽  
Friction Stir Welding ◽  
Yield Strength ◽  
Rotation Speed ◽  
Pure Copper ◽  
Traverse Speed ◽  
Tensile Tests ◽  
Friction Stir ◽  
Hardness Tests ◽  
Metallographic Images

The friction stir welding (FSW) was conducted in the pure copper plates with the thickness of 4 mm in the constant traverse speed of 25 mm/min and five different rotation speeds. Analysis of metallographic images showed that the increasing of the rotation speed results in the increase of grain size in the nugget zone. Vickers hardness tests were conducted on the weld samples and the maximum hardness obtained in rotation speed of 900 rpm. Results of the tensile tests and their comparison with that of the base metal showed that the maximum strength and the minimum elongation are achieved again in this rotation speed. Yield strength and ultimate tensile strength increased with the decrease in grain size in the nugget region, and the yield strength obeyed Hall-Petch relationship. Hence, the hardness values do not follow the relationship.

Finite Element and Neural Network Modeling of Extreme Overbalance Perforating

1999 ◽  
Author(s):  
Mehdi Azari ◽  
Mahmoud Asadi ◽  
Roger Schultz ◽  
Ali Ghalambor
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Network Modeling ◽  
Neural Network Modeling

The Use of Bobbin Tools for Friction Stir Welding of Aluminium Alloys

2010 ◽  
Vol 638-642 ◽  
pp. 1179-1184 ◽  
Author(s):  
Philip L. Threadgill ◽  
M.M.Z. Ahmed ◽  
Jonathan P. Martin ◽  
Jonathan G. Perrett ◽  
Bradley P. Wynne
Keyword(s):  
Mechanical Properties ◽  
Grain Size ◽  
Friction Stir Welding ◽  
Cross Section ◽  
Axial Force ◽  
Aluminium Alloys ◽  
Processing Parameters ◽  
Machine Design ◽  
Friction Stir ◽  
Triangular Zone

The use of a double sided friction stir welding tool (known as a bobbin tool) has the advantage of giving a processed zone in the workpiece which is more or less rectangular in cross section, as opposed the triangular zone which is more typically found when conventional friction stir welding tool designs are used. In addition, the net axial force on the workpiece is almost zero, which has significant beneficial implications in machine design and cost. However, the response of these tools in generating fine microstructures in the nugget area has not been established. The paper presents detailed metallographic analyses of microstructures produced in 25mm AA6082-T6 aluminium wrought alloy, and examines grain size, texture and mechanical properties as a function of processing parameters and tool design, and offers comparison with data from welds made with conventional tools.

A Finite Element Model for the Prediction of Chip Formation and Surface Morphology in Friction Stir Welding Process

2021 ◽  
pp. 1-21
Author(s):  
Debtanay Das ◽  
Swarup Bag ◽  
Sukhomay Pal ◽  
M. Ruhul Amin
Keyword(s):  
Finite Element ◽  
Friction Stir Welding ◽  
Surface Morphology ◽  
Chip Formation ◽  
Material Flow ◽  
Welding Process ◽  
Measured Temperature ◽  
Material Loss ◽  
Friction Stir ◽  
Mass Scaling

Abstract Friction stir welding (FSW) is widely recognized green manufacturing process capable of producing good quality welded joints at temperature lower than the melting point. However, most of the works is focused on to the establishment of the process parameters for a defect-free joint. There is a lack to understand the formation of defects from physical basis and visualization of the same, which is otherwise difficult to predict by means of simple experiments. The conventional models do not predict chip formation and surface morphology by accounting the material loss during the process. Hence, a 3D finite element based thermo-mechanical model is developed following Coupled Eulerian-Lagrangian (CEL) approach to understand surface morphology by triggering material flow associated with tool-material interaction. In the present quasi-static analysis, the mass scaling factor is explored to make the model computationally feasible by varying the FSW parameter of plunge depth. The simulated results are validated with experimentally measured temperature and surface morphology. In CEL approach, the material flow out of the workpiece enables the visualization of the chip formation, whereas small deformation predict the surface quality of the joint.

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