Wire surface quality index for wire surface erosion using image processing technique
6.3 Estimation of a wire surface quality index for the intensity of wire surface erosion using image processing technique
Extensive amount of erosion causes wire failure, which is one of the major drawbacks in the manufacturing sector. Thus, an attempt was made to determine a surface quality index of the
eroded wires in order to estimate the intensity of wire surface damages and identify a tolerable limit of wire wear to avoid wire failure. The developed methodology shall be useful in establishing a relationship between the wire damages and surface roughness of the machined components. A wire surface quality index was determined from the FESEM images of the wire electrode by plotting an image histogram using the image processing technique. A histogram in image processing is a graphical plot, which denotes the frequency of different pixel intensities. An image histogram is a gray-scale value distribution showing the frequency of occurrence of each gray-level value. It is a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value.
Histograms have many uses in image processing like image analysis, image brightness, adjusting contrast of an image, image equalization, thresholding and computer vision. The horizontal axis of the histogram plot represents the tonal variations, while the vertical axis represents the total number of pixels in that particular tone. The pixel intensity values ranges from 0–255 in the horizontal axis for an 8-bit grayscale image. An image is scanned and a running count of the number of pixels found at each intensity value is kept. This then constructs a suitable histogram for the image. The histogram of a dark image will have most of its data points towards left and centre of the graph. On the other hand, a bright image will have its data points towards right and centre of the graph.
Figure 6.1 depicts the flowchart for estimating the wire surface quality index using the image processing technique. A series of WEDM experiments was carried out on the wire EDM machine (MAKE JK MACHINES, MODEL EC032) with Ti-6Al-4V alloy as the workpiece material and molybdenum wire tool. A total of 36 experiments according to the Taguchi L36 array were carried out and every single set was repeated thrice as explained in chapter 5. Corner cutting experiments were performed on a 60 × 60 × 12 mm3 Ti-6Al-4V plate and wire samples were collected after every three cuts for a single process set. The collected wire samples were examined in FESEM. A histogram was plotted for the FESEM wire images to observe the change in pixel intensity at different locations using the ImageJ software. The region with damages or craters on the wire surface will denote a different pixel intensity and thus can be distinguished from a surface with lesser wear. Thus, an image histogram plot can be considered as a useful tool to estimate the intensity of wire erosion
during the cutting operation. A particular area of the wire sample was considered for analysis keeping the pixel count constant.
Figure 6.2a shows the wire sample for process set: V = 60 V, I = 4 A, ton = 8 μs, toff = 6 μs, v = 6 m/s; and the histogram of the considered region was plotted (Figure 6.2b). The histogram mean value was evaluated as 176.53. A similar technique was applied to Figure 6.2c, which showed critical and substantial amount of wear. The corresponding histogram mean was found to be lower (127.45) i.e. the histogram is shifted towards the darker side.
The reason behind this is because the light intensity reduces in the region where the damage occurs, thus the pixel intensity reduces and the histogram shifts towards the darker side. This shifting of image histogram towards the darker side for wire surfaces with critical damages can be considered as the verification of the image processing technique. It has been observed that an image with a substantial amount of damage on the wire surface will produce a lower mean value of histogram. Damages in the form of craters or pits on the wire surface reduce the light intensity in that particular area which shifts the histogram to the left (darker) side.
This lowers the histogram mean value. Thus, the mean value of the plotted histogram can be considered as a wire surface quality index to denote the extent and intensity of erosion undergone by the electrode. Lower mean values of wire image histogram denote a lower surface quality of the wire samples.
Figure 6.1 Flowchart to estimate the wire surface quality index for an eroded wire surface
Figure 6.2 Analysis of eroded wire samples using image processing technique (a), (c) FESEM wire images after WEDM of Ti-6Al-4V and (b), (d) histogram plots of eroded wire
surfaces at the chosen parameter sets.
Table 6.1 shows the histogram mean values of the FESEM wire images collected at different sets of process conditions. FESEM images were analysed at 36 different sets as discussed in chapter 5. A lower histogram mean value denotes larger intensity of wear on the wire surface. This developed methodology can be useful in understanding the detrimental influence of wire erosion on product surface quality, which is explained in the next section.
Table 6.1 Histogram mean values of wire samples at different process conditions Serial
no.
Discharge voltage (V)
Discharge current (A)
Pulse on- time (μs)
Pulse off- time (μs)
Wire speed
(m/s)
Histogram mean
1 60 4 4 2 3 124.1
2 60 6 8 4 6 145.26
3 60 8 16 6 9 143.89
4 60 4 4 2 6 164.01
5 60 6 8 4 9 146.21
6 60 8 16 6 3 108.3
7 60 4 8 6 3 127.45
8 60 6 16 2 6 108.11
9 60 8 4 4 9 150.06
10 60 4 16 4 3 108.77
11 60 6 4 6 6 157.79
12 60 8 8 2 9 123.85
13 60 6 16 2 9 126.31
14 60 8 4 4 3 113.83
15 60 4 8 6 6 176.53
16 60 6 16 4 3 106.96
17 60 8 4 6 6 146.87
18 60 4 8 2 9 170.01
19 85 6 4 6 9 150.26
20 85 8 8 2 3 104.97
21 85 4 16 4 6 129.88
22 85 6 8 6 9 170.05
23 85 8 16 2 3 104.35
24 85 4 4 4 6 164.57
25 85 8 8 2 6 105.44
26 85 4 16 4 9 148.44
27 85 6 4 6 3 127
28 85 8 8 4 6 110.33
29 85 4 16 6 9 157.02
30 85 6 4 2 3 105.67
31 85 8 16 6 6 122.17
32 85 4 4 2 9 180.45
33 85 6 8 4 3 116.79
34 85 8 4 4 9 130.59
35 85 4 8 6 3 126.04
36 85 6 16 2 6 105.28