Contents
Chapter 2 Literature Review
2.4 Signal processing techniques
Literature Review
reported that the proposed methodology is effective and can be extended towards prediction of process parameters for unknown materials.
The survey of available literature provided the information that few works has been carried out for monitoring of FSW process. However, in other manufacturing processes monitoring has already gained significant attention. For instance, in the comprehensive review of turning operation over a decade by Sick (2002) around hundreds of literature on monitoring of the process is presented. In comparison to this the effort made for developing schemes for monitoring of FSW process is quite less. The demand of FSW process is increasing across various automated industrial sectors. In industry low cost, effective and reliable monitoring methods are useful for achieving the desired quality of the product. Monitoring schemes can help in decision making regarding the process outcome for the betterment of the process. The very less attempts made for monitoring of FSW process the current research work is motivated to develop effective methodologies for monitoring weld quality.
Chapter 2
Fourier transform was applied to acoustic emission signals by Subramaniam et al.
(2013) in FSW process. Effects of various pin profiles were investigated through computation of power spectral densities of the signals. Vertical and transverse force signals acquired during FSW process were analyzed in frequency domain by Jene et al.
(2008). Fourier transform and short time Fourier transform were implemented as the signal processing tools. Frequency spectrum of the signals were computed and presented as the feature for monitoring variation of process with tool rotational speed and welding speed. Identification of automatic gap during FSW process was attempted through processing vertical force signal by Yang et al. (2008). Fourier transform was implemented for frequency domain feature extraction in terms of power spectral density of the signals. Fourier transform in gap detection in FSW process was also attempted by Fleming et al. (2008) with vertical force signals. Frequency domain features were computed in order to develop classification methodology for detection of gaps during the process. Processing of acoustic emission signal for monitoring of FSW process with Fourier transform, short time Fourier transform and wavelet transform was attempted by Soundararajan et al. (2006). It was concluded that among the three methods wavelet transform offers more valuable information regarding the process behaviour compared to others. Fourier transform as a signal processing signals was also implemented by Longhurst et al. (2016) for processing main spindle motor current signal. It was commented that inclusion of defects in FSW process show appreciable change in the frequency spectrum of the signal. Boldsaikhan et al. (2011) presented a methodology of defect detection in FSW process using Fourier transform of vertical and transverse force signals. It was commented that occurrence of defect in the welded samples bring notable change in frequency spectrum of the signals. The effect of tool wear on acoustic emission signal during FSW process was investigated by Zeng et al. (2006). The frequency domain analysis leads to the impression that characteristics of tool wear were evidential in acoustic signals. It was concluded that with the increase in the tool wear magnitude of the signals in frequency domain reduces indicating higher tool wear.
Despite the limitation and assumption with Fourier transform and short time Fourier transform many researchers have used these techniques for processing of signals acquired during FSW process. However, these methods results in loss in time information that might be useful in time dependent monitoring of FSW process. As a solution to the limitations with frequency domain techniques, time frequency domain
Literature Review
techniques are evolved with time. Wavelet transform is one of the most robust techniques for signal processing with retaining time and frequency information of the signal.
Wavelet transform was used by Chen et al. (2006) in processing of acoustic emission signal in FSW process. The study revealed that with the inclusion of defects in the welded samples wavelet energy signatures of the signals deviates from that of a defect free signal. In the work presented by Kumari et al (2016) continuous wavelet transform was used as signal processing technique for defect identification in FSW process. Vertical force signals were analyzed using continuous wavelet transform and features were computed for identification of surface level defects. However, the authors did not comment on selection of suitable mother wavelet function which is essential in effective decomposition of signal using wavelet transform. Kumar et al (2015) also attempted identification of surface level defects in FSW process using vertical force signal and discrete wavelet transform. Signal features were computed which later correlated to surface level defect information during the welding. In this work also the authors failed to mention about the selection procedure for suitable mother wavelet function and appropriate level of decomposition in wavelet transform. Although wavelet transform offers rich way of analyzing a signal in both time and frequency domain simultaneously, effort made in the field of FSW process is less. However, in other welding and manufacturing processes wavelet transform has gained significant use compared to FSW process (Liu et al., 2015; Bhat et al., 2015; Droubi et al., 2017; Chen et al., 2009; Praveen et al., 2013; Yang et al., 2016). Exploring the use of wavelet transform in developing monitoring methodology for FSW process would be an appreciable contribution of the current research work.
In signal information based monitoring of various other processes a relatively new time frequency domain technique known as Hilbert-Huang transform has gained significant attention(Lin and Chu, 2011, 2012; Law et al., 2012; Bin et al., 2012; Chen et al., 2012; He et al., 2013; Rado et al., 2014; Taralunga et al., 2015; Bakker et al., 2015;
Liu et al., 2015; Li and Hao et al., 2015).The main advantage of Hilbert-Huang transform over wavelet transform is the ability to estimates subtle change in the frequency.
Estimation of instantaneous frequency from wavelet transform is suboptimal because of frequency smoothening and wavelet transform assumes stationary frequency during the time span of wavelet function. Moreover, wavelet transform convolves a signal with a
Chapter 2
predefined mother wavelet function to decompose a signal. The choice of wavelet function depends on the signal under consideration. On the other hand Hilbert-Huang transform does not require any convolution of the signal with a predefined basis function. These advantages of Hilbert-Huang transform makes it more robust than wavelet transform. Although there is no relevant literature found in FSW domain with Hilbert-Huang transform. Exploring Hilbert-Huang transform for processing of signals is FSW process might result in more useful information for developing effective monitoring scheme for the process.
Apart from Fourier transform and wavelet transform fractal theory is also a prospective technique for processing signals. The idea of extending the use of fractal dimension as an indicator to judge the quality of spot welding was proposed by Zhen et al. (2007). Defects in arc welding process was attempted to detect and characterize by Viera et al. (2008) through computing fractal dimensions from the current signals. Along with signal analysis, fractal dimension found its importance in analyzing images as well.
Krivonosova et al. (2013) and Zhanfeng (2012) used images of welded specimens to compute fractal dimensions and thus proposed methods for the prediction of weld quality. In bio medical applications use of fractal theory as an effective signal processing technique has already been established (Parvinnia et al., 2014; Abhishekh et al., 2013;
Paramanthan et al., 2008; Liu et al., 2005). In other processes fractal theory has found acceptances related to monitoring and control (Shoupeng et al., 2007; Shirong et al., 1999; Zhu et al., 2007; Yang et al., 2007; Kupkova et al., 2005; Tanaka et al., 2008).
However, in FSW process monitoring use of fractal theory as a signal processing tool has not been attempted. Methodologies for monitoring of FSW process through processing signals with fractal theory can be an effective contribution of the present study. Potential of fractal theory as a tool for image processing is also established through various researchers (Ahammer, 2011; Li et al., 2009; Chappard et al., 2003; Risovic et al., 2008;
Chen et al., 1989; Fortin et al., 1992).Similar attempts on monitoring of FSW process with images and fractal theory is not available and research in directive can be a valuable contribution.
The aforementioned survey of available literature fetched that researchers implemented Fourier transform, short time Fourier transform and wavelet transform as signal processing techniques in monitoring of FSW process. Although Fourier transform and short time Fourier transform have their own limitations such as lack of producing
Literature Review
time based information, lack of localization of events etc. Shortcomings with these two techniques were eliminated with the introduction of wavelet transform. However, use of wavelet transform in monitoring of FSW process is less compared to other manufacturing techniques. And the researchers reported to use wavelet transform without providing details on the selection of suitable mother wavelet function which plays influencing role in decomposition. Development of a method for selection of suitable mother wavelet function would be of great importance. The use of fractal theory for signal and image processing for monitoring of FSW process can also be attempted as it is lacking in the research trend.