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Title: Numerical and artificial neural network modelling of friction stir welding
Author: Wang, Hua
Awarding Body: Cranfield University
Current Institution: Cranfield University
Date of Award: 2011
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This thesis is based on the PhD work of investigating the Friction Stir Welding process (FSW) with numerical and Artificial Neural Network (ANN) modelling methods. FSW was developed at TWI in 1991. As a relatively new technology it has great advantages in welding aluminium alloys which are difficult to weld with traditional welding processes. The aim of this thesis was the development of new modelling techniques to predict the thermal and deformation behaviour. To achieve this aim, a group of Gleeble experiments was conducted on 6082 and 7449 aluminium alloys, to investigate the material constitutive behaviour under high strainrate, near solidus conditions, which are similar to what the material experiences during the FSW process. By numerically processing the experimental data, new material constitutive constants were found for both alloys and used for the subsequent FSW modelling work. Importantly no significant softening was observed prior to the solidus temperature. One of the main problems with numerical modelling is determining the values of adjustable parameters in the model. Two common adjustable parameters are the heat input and the coefficients that describe the heat loss to the backing bar. To predict these coefficients more efficiently a hybrid model was created which involved linking a conventional numerical model to an ANN model. The ANN was trained using data from the numerical model. Then thermal profiles were abstracted (summarised) and used as inputs; and the adjustable parameters were used as outputs. The trained ANN could then use abstracted thermal profiles from welding experiments to predict the adjustable parameters in the model. The first stage involved developing a simplified FE thermal model which represents a typical welding process. It was used to find the coefficients that describe the heat loss to the backing bar, and the amount of power applied in the model. Five different thermal boundary conditions were studied, including both convective and ones that included the backing bar with a contact gap conductance. Three approaches for abstracting the thermal curves and using as inputs to the ANN were compared. In the study, the characteristics of the ANN model, such as the ANN topology and gradient descent method, were evaluated for each boundary condition for understanding of their influences to the prediction. The outcomes of the study showed that the hybrid model technique was able to determine the adjustable parameters in the model effectively, although the accuracy depended on several factors. One of the most significant effects was the complexity of the boundary condition. While a single factor boundary condition (e.g. constant convective heat loss) could be predicted easily, the boundary condition with two factors proved more difficult. The method for inputting the data into the ANN had a significant effect on the hybrid model performance. A small number of inputs could be used for the single factor boundary condition, while two factors boundary conditions needed more inputs. The influences from the characteristics of the ANN model were smaller, but again thermal model with simpler boundary condition required a less complex ANN model to achieve an accurate prediction, while models with more complex boundary conditions would need a more sophisticated ANN model. In the next chapter, the hybrid method was applied to a FSW process model developed for the Flexi-stir FSW machine. This machine has been used to analyse the complex phase changes that occur during FSW with synchrotron radiation. This unique machine had a complex backing bar system involving heat transfer from the aluminium alloy workpiece to the copper and steel backing bars. A temperature dependent contact gap conductance which also depends on the material interface type was used. During the investigation, the ANN model topologies (i.e. GFF and MFF) were studied to find the most effective one. Different abstracting methods for the thermal curves were also compared to explore which factors (e.g. the peak temperature in the curve, cooling slope of a curve) were more important to be used as an input. According to close matching between the simulation and experimental thermal profiles, the hybrid model can predict both the power and thermal boundary condition between the workpiece and backing bar. The hybrid model was applied to six different travel speeds, hence six sets of heat input and boundary condition factors were found. A universal set was calculated from the six outcomes and a link was discovered between the accuracy of the temperature predictions and the plunge depth for the welds. Finally a model with a slip contact condition between the tool and workpiece was used to investigate how the material flow behaviour was affected by the slip boundary condition. This work involved aluminium alloys 6082-T6 and 7449-T7, which have very different mechanical properties. The application of slip boundary condition was found to significantly reduce the strain-rate, compared to a stick condition. The slip condition was applied to the Flexi-stir FSW experiments, and the results indicated that a larger deformation region may form with the slip boundary condition. The thesis successfully demonstrates a new methodology for determining the adjustable parameters in a process model; improved understanding of the effect of slip boundary conditions on the flow behaviour during FSW and insight in to the behaviour of aluminium alloys at temperatures approaching the solidus and high strain-rates.
Supervisor: Colegrove, P. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: modelling friction stir welding ; artificial neural network ; constitutive behaviour of aluminium alloys ; heat transfer numerical modelling ; computational fluid dynamics ; CFD