Weld quality monitoring and defect identification in friction stir welding process

Using the computed signal features, artificial neural network (ANN) and logistic regression (SVR) models are developed for predicting the ultimate tensile strength (UTS) of joints. The features are further used to predict the UTS of nodes using ANN and SVR models.

List of Tables

After completion of the experiments, test samples are prepared for mechanical and microstructural examination. The mechanical properties include UTS, yield strength, and percent elongation of the joints produced in each welding experiment. The respective figures show that none of the weld quality characteristics correlated in an understandable way with process parameters.

Test specimens for measuring microhardness of welded specimens are prepared by standard procedures. This increase in microhardness can be attributed to the finer grains in the NZ of the as-welded samples. The primary objective of this research work is to develop methodologies for monitoring the FSW process.

Microstructural examination of the welds showed that the grains in NZ are finer compared to the base material. The microhardness distribution of the welded samples showed that the hardness of the weld zone is higher compared to base materials.

Design and development of force and torque

Introduction
Design and construction of the developed setup
Strain gauge installation and orientation of ring member
Data acquisition, amplification circuit and natural frequency of the setup
Calibration and comparison of developed setup
Real time welding results using the developed setup
Summary

Keeping these features of the ring member, the material for the members used is mild steel. These four faces of the ring member are therefore chosen for fixing the strain gauges responsible for measuring vertical force during welding. This level is chosen for the installation of the strain gauges responsible for measuring transverse force.

The schematic of the developed amplifier circuit for the present case is shown in Fig. The developed setup should have a natural frequency that is at least four times the frequency of the machine tool (Korkut, 2003; Karabay, 2007b). Thus, this equation can be used for real-time measurement of the force during actual welding operation.

This is quite acceptable for implementing the developed setup in real-time force measurement during the FSW process. Low cross-sensitivity of the developed setup indicates the reliability in measuring individual forces and moment simultaneously.

Signal features based monitoring of UTS

Introduction
Time domain analysis
Frequency domain analysis

Fourier transform
Short time Fourier transform

Time frequency domain analysis

Wavelet transform
Hilbert-Huang transform

Fractal theory

Higuchi’s algorithm
Katz’s algorithm
Validation of fractal dimension codes

Prediction of UTS with current signal features
Prediction of UTS with vertical force signal features
Prediction of UTS with torque signal features
Prediction of UTS with tool rotational speed signal features
Summary

Frequency domain analysis of the signals does not provide any comprehensible information regarding the process behavior. Two SVR models are developed in this study for modeling UTS of the joints. These characteristics are used together with process parameters for the development of data-driven models for prediction of UTS of the joints.

This indicates that SVR model can be effectively used for prediction of UTS of the joints. The calculated signal features were fused with SVR and BPNN models for prediction of UTS of the joints. This model consists only of signal features in the input space of the SVR model.

The performance of SVR model developed for predicting UTS of the joints with signal features in the input space (Model I) is compared with model developed using BPNN model. In this section, a methodology is developed for predicting UTS of the joints with SVR and BPNN models with signal features. In the present study, it is decided to use BPNN and SVR for the prediction of UTS of the joints with process parameters and FDs.

The prediction of UTS of the joints is attempted with characteristics of tool rotation speed signal.

Defect identification in FSW process

Introduction
Identification of defects

Analysis of vertical force signal features
Analysis of torque signal features
Defect identification using fractal theory
Analysis of temperature signal

Summary

In addition to the instantaneous frequency, the instantaneous phase angles obtained by the Hilbert transform of the IMF also give a remarkable insight into the detection of defects in welded specimens. The remaining welded samples show no signs of the presence of internal defects. The calculated FDs from the segmented welding period data are shown in Figure 6.9(b).

The proposed method with modification can be used for online monitoring of welding quality in the FSW process. In the FSW process, local temperature change rate greatly affects the mechanical properties of the welded parts (Fuller et al., 2007). Moreover, the temperature distribution in the welds is the cumulative result of the combination of different input process parameters.

The proposed method leads to an effective notion that the proposed indicator can be an effective alternative for detecting defects in friction tube welded specimens. The reason for the low values for defective welds can be derived from the physics involved in defect formation in the FSW process. In this work, the threshold value is calculated to differentiate the defective weld zone from the defect-free weld zone based on Euclidean distance calculation from p. The method for threshold calculation is given in Eq. 6.6) where, represents the threshold limit for is the Euclidean distance, represents s for defect-free weld cluster, represents s for.

Monitoring with weld image information

Introduction
Algorithms for estimation of fractal dimension
Experimental investigation
Image acquisition and pre-processing
Image rendering using fractal theory

Method I
Method II
Scale invariance of fractal dimension

Wavelet analysis of weld images
Summary

The calculated FDs from images are presented as an indicator for monitoring the FSW process through correlation of the UTS of the joints. At different process parameters, the semicircular rings on the upper surface of the welds are different, and this is clearly seen from the recorded images. Different semicircular rings on the top surface of the welds are the representation of the effects of different combinations of process parameters.

The semicircular rings formed are the result of this typical movement of the tool (Krishnan, 2002). At low tool rotation speed, the irregularities on the top surface of the weld are high and therefore the calculated FDs are also high. Both methods of estimating FDs of images deliver a certain remarkable trend with ultimate tensile strength of the joints.

Moreover, it is also demonstrated that the calculated FDs are independent of the scaling of the images. As the FDs of the images increased, the ultimate tensile strength of the welds was found to follow a decreasing trend.

Conclusions and future scopes

Conclusions of the present work

Installation of the developed setup with an existing FSW machine is relatively easy compared to other force or torque measurement systems. Comparison of the developed method with already published methods showed that the proposed method is free of process dependencies. The method used the relationship between the signal energy and the entropy of the wavelet packets to determine the appropriate function of the mother wavelet.

The characteristics of main spindle motor and welding motor current signals, vertical force and torque signals, and tool rotation speed signals are evaluated using WPT, WPT-HHT, DWT, and fractal theory, respectively. The features are combined with BPNN, RBFNN, and SVR models to develop UTS joint prediction models. The identification of internal defects in the welded samples was successfully achieved with the functions of vertical force signal, torque signal, tool rotation speed signal and temperature signal.

The present study also develops a new method based on images of the top surface of the welds for monitoring the UTS of the joints. The proposed work provides a simple yet effective approach for monitoring UTS of the joints with significantly less post-processing time.

Future scopes of the present work

Along with these indicators, instantaneous phase and frequency of vertical force signals calculated using WPT-HHT are also presented as effective features for identifying internal defects. Fractal dimensions calculated from the tool rotation speed signal using Higuchi's algorithm also provided insight into the identification of internal defects. Two methods have been proposed for processing images to extract appropriate information using fractal theory.

The image processing method developed in the present research work can be extended to test thermographic images of welded samples for welding quality monitoring and defect identification in the welded samples. Suitable hardware and software integration can be developed so that developed methods can be extended to hardware realization for actual industrial implementation.

BPNN Model development steps

Definition
Presentation of the training patterns
Normalization of the data set
Forward Pass
Error calculation
Setting the stopping criteria for training
Backward pass
Iteration

In the case of the logistic sigmoid function, the same is presented as follows. where is a constant and is the input to the activation function. The range of each input is linearly scaled over the range of the activation function. Therefore, the input and output parameters for the entire data set are normalized to the range from 0.1 to 0.9 as follows. where and are the maximum and minimum values of each parameter in the data set under consideration, is the actual value of the parameter, and are the normalized values of the parameters.

After applying the activation function to the weighted sum from a neuron, the output from it can be calculated and this becomes the input to the next layer. The output of the hidden neuron and output neuron for pattern are calculated as follows. By calculating the output of the output neuron, with the target output of the pattern at the iterations, the error value can be calculated as follows.

The goal of training the BPNN model is to reach where the mean square error (MSE) for all training patterns decreases to a sufficiently small value. Training is stopped when the test dataset continues to increase while the training dataset follows a decreasing trend.

RBFNN model development steps

Forward pass calculations
Training algorithms
Setting the stopping criteria for training
Iteration

Output of output for input pattern at the iteration was calculated using the sigmoid activation function. Where, is the output of the output neuron with input pattern in the iteration, is the weighted sum of the output neuron with input patterns at the iteration, is the connection weight between the hidden neuron and the . The network training for RBFNN is done by updating the RBF, width of the Gaussian function and the weight between hidden and output neurons of the network.

In the present full training algorithm is used where all the above parameters were updated through back propagation algorithm and on the same time scale.

Weld quality monitoring and defect identification in friction stir welding process

Contents

Introduction

Literature Review

Experimental Investigation

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