Contents
Chapter 6 Defect identification in FSW process
6.2 Identification of defects
6.2.3 Defect identification using fractal theory
Successful development of defect identification methodologies from vertical force as well as torque signals acquired during FSW process motivated this study to test tool rotational speed signal as well for defect identification. This signal is analyzed in time domain with fractal theory and the details are given in chapter 5, section 5.9.
Higuchi’s algorithm has been used for computation of FDs for development of index for defect identification. As like the earlier methods for defect identification it is also started with observation of the signals in time domain. Figure 6.8(a) shows the welding period data for tool rotational speed signal against the defective cases (Exp. No. 35 and Exp.
No. 65). The signals show clear deviation when compared with the signal against the defect free welding case as shown in Fig. 6.8(b). The change in the amplitude and trend of the signal for defective cases are clear indication of process anomaly during welding operation. Figure 6.8(b) displays the possibility of detecting three characteristic stages of FSW process for a defect-free weld. The figure is intended to prove that the signal acquired during the welding process is actually reflecting the FSW process and is not an outcome of some redundant data or process. The change in the process is tried to identify quantitatively using the measure of fractal dimension in this research work.
(a)
(b)
Fig. 6.8 Tool rotational speed signals for (a) defective welding cases (welding period data only) (b) different salient stages in friction stir welding process for defect free case
Defect identification in FSW process
Figure 6.9(a) depicts the computed FDs of first two welding stages and there is not much appreciable difference since the plunge depth and dwell time were kept fixed during welding for all the experiments. To correlate the formation of defects, the welding period data is only considered and segmented into several divisions and FDs are computed for each division. For instance defective weld (Exp. No. 35), each division represents 10.4 seconds and it is quite indicative of the self-explanatory characteristic as a part of whole process. The segmentation of whole signals aids to find out any alteration of computed values during scanning through the whole process.
The computed FDs from the segmented data of the welding period are shown in Fig.6.9(b). Very first data point is omitted for all the cases as these are not informative regarding the process behavior since it belongs to very initial stage of welding which may not produce stable signature of the process. It is also very clear that after a certain division i.e., certain point of time, fractal dimension starts decreasing for defective cases.
This phenomenon physically correlates to the point of defect formation during welding which is confirmed through visual inspection of the welded sample by post-weld sectioning in several points. Before this point of depression, the FDs do not show any significant variations and the trend can be compared with the defect free welds. It is indicative that up to the point of depression, the welding process was stable, so as the signals and thus the computed FDs are not showing much variation. Ahead of this point, the FDs for both the defective cases drop considerably. This is physically linked with the formation of defects that was captured well by the dynamic behavior of the signals and hence reflected in the computed fractal dimension. Formation of defect is assumed to introduce instability (Kim et al., 2006) in the process and hence a reduction in fractal dimensions is observed. This decrease continues till the point up to which the process is unstable. Once the process becomes stable with the defect inside the weld, an increase in the fractal dimension is observed (Fig.6.9b). This analysis helps to find a boundary beyond which defects are formed in the welded samples which is indicated in Fig.
6.9(b).
Defects are identified by sectioning the welded samples and the physical measurements confirm the point from which the defects are visible. Figure 6.1 indicates the macrograph of the cross-section of welded sample corresponding to welding conditions of Fig. 6.9. The tunnel defects are formed in the welds that represents defective welds (Exp. No. 35 and 65). However, present methodology cannot describe
Chapter 6
the shape and size of the defect. Figure 6.10 represents the schematic of defect formation for the case of defective weld against Exp. No. 65. This correlates to the point in time from which the defects started in the weld. This calculated time is in good agreement with the time step corresponding to Fig. 6.9(b) in terms of divisions in welding period data where a clear decrease in the fractal dimension of the defective welds observed.This brings the essence of fractal dimension computed from welding period sensor data of tool rotational speed signals may be projected as an independent indicator for the detection of occurrence of defects in the weld.
(a) (b)
Fig.6.9 Variation of fractal dimension (a) plunging and dwell period (b) segmented welding period for 20 equal divisions
Fig 6.10 Schematic of detection of defects in the welds through sectioningfor defective weld against Exp. No. 65
Fractal dimension computed using Higuchi’s method from tool rotational speed signal shows promising direction to identify the formation of defects in FSW process.
The defect free weld produces consistent fractal dimension within the tolerance limit of ±
Defect identification in FSW process
0.025 for all segmented data. Sharp decreasing trend in fractal dimension serves as an indicator to detect the initiation and formation of defects. Appreciable difference in the welding period fractal dimensions (~ 0.13 – 0.15) between the defective cases and defect free welds, reveal that the process outcome can be monitored using the proposed approach. However, the analysis is not tested for characterization of defects for shape and size estimation. The proposed methodology with modification can be adopted for online monitoring of the weld quality in FSW process.