Surface roughness evaluation of electrical discharge machined surfaces using wavelet transform of speckle line images

Measurement 149 (2020) 107029

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Surface roughness evaluation of electrical discharge machined surfaces using wavelet transform of speckle line images J. Mahashar Ali a,⇑, H. Siddhi Jailani a, M. Murugan b a b

Department of Mechanical Engineering, B S Abdur Rahman Crescent Institute of Science and Technology, Chennai 600 048, India School of Mechanical Engineering, Vellore Institute of Technology, VIT, Vellore Campus, 632 014, India

a r t i c l e

i n f o

Article history: Received 16 June 2019 Received in revised form 6 August 2019 Accepted 3 September 2019 Available online 10 September 2019 Keywords: Surface roughness Image processing Machine vision system Speckle line images Bi-orthogonal wavelet transform

a b s t r a c t The surfaces produced by electrical discharge machine (EDM) were illuminated using a redline laser source and the speckle line images of the specimens were captured using a CMOS camera. Signal vectors were generated from the speckle line images which are 1
2592 matrices, represented the grey scale intensity of the speckle line images. The image signal vectors were then decomposed using a wavelet transform by key local intensity variation method. The fourth and the fifth levels of the six-level decomposition of bi-orthogonal wavelet were expected to bear the details about the surface roughness. The root mean square (RMS) and variance of the 4th and the 5th level decomposition were obtained and were compared with the surface roughness parameters roughness average (Ra), arithmetic mean slope (Rda), and root mean square slope (Rdq). RMS and variance of the 4th level decomposed signals were found to correlate well with surface roughness parameters. Ó 2019 Elsevier Ltd. All rights reserved.

1. Introduction Surface roughness is one of the most important parameters to measure and control the quality in manufacturing. The conventional method of evaluating the surface roughness using stylus instrument is intrusive and off line and hence it is not suitable for online evaluation. So, there is an increasing need for a reliable, non-contact method of surface roughness evaluation. For the past decade, there is a lot of advancement in the image processing techniques in iris and finger print recognition. This has provided the basis for developing image based surface roughness measuring techniques. Specific lay patterns are produced while machining the metal surfaces with processes like turning, planning and milling. But the electrical discharge machining components surface texture is random in nature, which is beneficial in many applications. Furthermore, by taking multiple skimming passes, EDM finish quality can become almost mirror-like. In any machining surfaces, three prominent features can be identified: roughness, waviness between the peaks or valleys and form feature. Stylus instrument is widely used to measure these surface variations with higher reliability. The main limitation of this process is that the stylus tip

⇑ Corresponding author at: 7, Vallaba Agraharam Street, Triplicane, Chennai, Tamilnadu 600005, India. E-mail address: [email protected] (J. Mahashar Ali). https://doi.org/10.1016/j.measurement.2019.107029 0263-2241/Ó 2019 Elsevier Ltd. All rights reserved.

radius prevents it from reaching the real bottoms of the valley, thereby filtering the trace of the profile. Also substantial time is consumed in this offline measurement process. Since it is intrusive and touch type, it is not suitable for online measurement. Hence there is a real need for a system of reliable high-speed and noncontact method for surface measurement. Even though many techniques are available including the optical technique, the reliability was not attained in the real life manufacturing environments. In this study, an attempt is made to develop a vision based surface classification method for surface roughness measurement of electrical discharge machined (EDMed) surfaces. 2. Literature Technological shifts on surface metrology have been documented very well by Jane Jiang et al., [1]. The Nano and micro roughness are formed by fluctuations in the surface characterized by peaks and valleys of varying amplitudes and spacings [2]. 2.1. Roughness parameters The surface texture study is quiet complex. Different machining processes leave different texture. So the characterization of this texture is much significance in engineering. For that a number of surface parameters have to be studied. It can be calculated in either 2D or 3D forms. The surface roughness parameters [3,4] are given

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in the following equations. Arithmetic average height (Ra) is also known as roughness average which is a very common roughness parameter generally used in quality control.

Ra ¼

1 l

Z

l

ð1Þ

jyðxÞjdx 0

‘y(x)’ is the deviation of the roughness irregularities over the mean line and ‘l’ is the sampling length.

Ra ¼

1 XN jy j i¼1 i N

ð2Þ

‘yi’ is the deviation of the profile from the mean line and ‘N’ is the number of samples over the assessment length. Eq. (1) is used for calculating Ra when the profile data is continuous and Eq. (2) is used with a discretized profile data. When a monochromatic coherent beam of laser light is projected on to a machined surface, the mutual interference of scattered light will produce a speckle pattern. This speckle pattern is depends upon the slopes at each points of the surface. So the hybrid parameters arithmetic mean slope (Rda) and root mean square slope (Rdq) is used. The hybrid parameter arithmetic mean slope of the profile is given by the following equation.

Rda; Arithmetic Mean Slope ¼

N X jdi j N i¼1

ð3Þ

dy

where di ¼ dxi are deviations in the slope. i

dyi is the change in the profile height corresponding to a traverse of dxi in the x-direction at point ‘i’. di is the slope at point ‘i’ and ‘N’ is the total number of points. The various mechanical properties like friction, fatigue crack initiation and hydrodynamic lubrication affect this parameter. Another hybrid parameter is termed the root mean square slope over the measurement length. It is given by the following equation

vffiffiffiffiffiffiffiffiffiffiffiffiffiffi u N 2 uX d i Rdq ; Root Mean Square Slope ¼ t N i¼1

ð4Þ

faces. Dhanasekar et al., [9] presented their work on testing the effectiveness of the vision system to analyze the roughness values of the machined surfaces. They used the geometric search technique on the images taken on the machined parts with varying surface roughness obtained by shaping, milling and grinding process and concluded that this approach would be best suited to online measurement of surface roughness. Lee et al., [10] used a polynomial and adaptive modeling method to relate between the feature of the surface image and the original surface roughness using machine vision system. Shahabi et al., [11] used a CCD camera to capture an edge profile of the work piece and the nose area of the cutting tool image intermittently during the turning operations. To remove the noise from the image wiener filtering was used. They used a model called Response Surface Methodology (RSM). The various feed rates, depth of cut and cutting speeds which are extracted from simulation and surface roughness Ra were used as output of the model. The models predicted were verified with real work pieces and by the real machining operations. Rajneesh kumar et al., [12] utilized a vision system for image capturing and used the regression analysis for quantifying the surface roughness of machined specimens. They estimated the parameter arithmetic average of the grey level (Ga) for the magnified and the original images. Finally, they correlated the Ga with the surface roughness. In industrial machine vision systems, process monitoring and quality inspection are in great demand. For in-situ inspection of machined surfaces vision-based techniques are highly suitable. Besmir cuka et al., [13] presented a surface roughness characterisation system for components manufactured by end milling operation using vision-based technique. Mohan Kumar Balasundaram and Mani Maran Ratnam [14] used amplitude, spacing and hybrid parameters to assess in process surface roughness during finish turning using commercial DSLR and CCD camera. Several parametric techniques for surface roughness measurement based on machine vision are described in the above literature but their reliability is yet to be established. However, a few parametric techniques such as those used in iris recognition system [15–17] for human identification, have yielded excellent results.

dy

where di ¼ dxi are deviations in the slope which is shown in Fig. 1. i

2.2. Machine vision system There are many new surface roughness measuring methods are available like, multi parameter modelling and learning [5], parametric anisotropic bi-directional reflectance distribution function (BRDF) [6], color distribution statistical matrix [7], and using fiber optic sensors [8]. Machine vision-based techniques are highly suitable for safer measurement in in-situ inspection of machined sur-

2.3. Image processing with wavelet transform: Iris recognition is one of the well-established areas in image processing [15,16,18–20]. Several commercial techniques are available in image processing for the recognition of fingerprints also [21]. A bi-orthogonal wavelet based iris recognition was studied by Abhyankar et al., [22–24]. Tang [25] studied the surface roughness based on the image processing and image recognition. The literature on iris recognition for human identification reveals that various techniques have been implemented

Fig. 1. Mean slope calculation of the surface profile [3].

J. Mahashar Ali et al. / Measurement 149 (2020) 107029

successfully for real time applications. The recognition involves five important stages, namely preprocessing, localization, segmentation, code generation and matching. Firstly, the image of the eye is preprocessed for variations on lighting and noise disturbance at the time of image acquisition. An edge detection technique is used to locate and localize the iris on the eye image. The centroid of the pupil is selected as a reference point for iris segmentation. The edge of the iris was connected to form a closed contour and stretched as an iris image signal. The polar representation of the signal is normalized to a rectangular block of fixed size. Then, iris feature extraction and code generation are performed using tools like wavelet transform zero-crossings, and a database of iris images is established. In the same way, when a new iris image is captured, it would be processed and matched against the database images for human identification. Soon after capturing the images, they were normalized to deal with lighting variation that affected the image processing. Normalization can be obtained as follows [26]:

 sðp; qÞ ¼



r ðp; qÞ  minðrÞ
255 maxðr Þ  minðrÞ

ð5Þ

The s(p,q) is the normalized image matrix. The r(p,q) is the image matrix each pixel intensity. min(r) is the minimum pixel intensity value and max(r) is the maximum pixel intensity value of image matrix. Since the grey-scale value of 8-bit was used, all values were multiplied by 255 to get the pixel intensity values in the range 0–255. Jeyapoovan et al., [27] adopted the iris recognition methodology in their work for milled surface characterization using the Euclidean distance and Hamming distance as matrices. Wavelets are like a mathematical microscope to obtain and analyze different frequency components at different resolution levels [28]. Wavelet transformation is one of the commonly used mathematical techniques of the time-frequency-transformations. Wavelet can be useful for surface measurement because it could decompose a surface profile into a multi-resolution representation for various surface features [29–32]. The wavelet analysis involves a frequency analysis of the signal into a scalar analysis. It uses time-frequency windows for timefrequency analysis. Long and short windows are used for high frequency and low-frequency analysis respectively [33]. The applications of wavelets such as feature extraction in engineering surfaces, denoising and surface measurement are well documented. Tomkiewicz [34] used a six level decomposed surface image signal for roughness measurement. One-dimensional continuous wavelets are written as [35]:

1 a

Wa;b ðxÞ ¼ pffiffiffi W

  xb a

ð6Þ

where, w(x) is the wavelet function, ‘a’ is the scale parameter for different frequency and ‘b’ is the translation parameter. A multiscale or multiresolution decomposition approach in wavelet transform was proposed by many researchers [29,36,37]. Using the coarse-to-fine approach, the changes in gray-level intensity of images at different decomposed levels were recorded. These contrast levels in the images are more useful for edge detection and roughness measurements. Jiang et al., [38,39] studied the application of lifting wavelet to rough surfaces and its representation for surface characterization. Min Zhong et al., [40] proposed a wavelet transformation based method for analysis of 3D surfaces, for image enhancement and noise suppression. They also elaborated the superiority of the technique over the Fourier Transform based methods. Pour [41] estimated the surface roughness of the ground specimens based wavelet transform. The largest Lyapunov exponent of the image approximation coefficients per surface roughness followed the pre-

3

dicted pattern in the grinding process. Pour [42] also attempted using the combined wavelet and time series analysis to determine the roughness of differently machined surfaces. He used the image entropy to specify the length of the image in machining processes. This method was based on the recognition of the dynamic characteristics of the surface with the dynamic characteristics of contact method. By integrating the time series and wavelet transform all types of image noises were removed. Lingadurai and Shunmugam [43] used a filtering process to separate the roughness component from the three dimensional reference surface consisting of waviness and form error. They established a few machined surfaces and their reference surfaces through wavelet filter from which they got the waviness content and phase matching. Chang et al., [44] used the Daubechies wavelets for non-contact surface roughness assessment of specimens by shaping, grinding and polishing processes. 2.4. Speckle based measurement If a beam of monochromatic coherent laser light is made to fall on a machined surface, the mutual interference of scattered light will produce a speckle pattern. The characteristic of roughness is designated by the contrast of speckle pattern. For the past two decades speckle metrology has been the field focused on for surface roughness measurements. Kayahan et al., [45] studied the surface roughness of machined components using binary speckle image analysis. Nicklawy et al., [46] characterized surface roughness by speckle pattern analysis. Within the domain of vision-based techniques, optical interferometry [47] and speckle metrology are the emerging areas for surface characterization. Using the laser, CCD, CMOS cameras, and high-end computers, the optical and speckle techniques are emerging as one of the best measurement techniques with many applications [47–49]. Schneider [50] used a new set of signal processing algorithms in speckle metrology. Spagnolo [51] used a sensor in the principle of digital speckle correlation for surface roughness diagnostics. He compared the optical measurement of different surfaces with mechanical stylus measurement. Kaplonek and Nadolny [52] presented the results obtained by hard to cut materials after using burnishing process. They processed the images of scattered laser light intensity using ARS method. They used a special software to obtain geometric and photometric parameters to assess the cylindrical surfaces. Dhanasekar and Ramamoorthy [53] attempted a phase shift algorithm to assess the roughness of ground surfaces by a speckle fringe analysis methodology. Advanced filtering to reduce noise in the fringes and a software were used to generate the 3D profiles and it was compared with stylus measured Ra values. Zhao et al., [54] proposed a dynamic speckle method for measuring surface roughness online, while the work piece was under motion. A mathematical model of a dynamic weak speckle image was proposed and its suitability for online roughness measurement was explored. Rodriguez et al., [55] proposed three speckle-based inspection techniques for characterizing the surfaces produced by a laser texturing process. The first one is contrast intensity technique which is the ratio between the quotients of standard deviation with the mean intensity of the speckle image. In the second method called binary image method, the grey scale binary speckle image was converted into a black and white image using an appropriate thrush hold and the area of the bright region was found to correlate well with roughness value of the conventional method. In the third method, the overall size of the speckle spot was found to increase with increasing surface roughness. Dias et al., [56] studied the lacunarity of speckle patterns, which is a measure of the distribution of empty spaces in an image. In speckle images, lacunarity measures the distribution of bright spaces. Through their modelling and experimental study, the authors could establish

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J. Mahashar Ali et al. / Measurement 149 (2020) 107029

the possibility of measuring surface roughness and other surface defects using the lacunarity of speckle images. Spagnolo et al., [57] used two speckle images obtained by using two lasers of different wavelengths and obtained an image based estimate of the three dimensional RMS of the surface roughness (Sq). This estimate was statistically found to be an accurate representation of the stylus based Sq. Though various literature are available on the machine vision system applications, image processing with wavelet transform and speckle metrology, a very little work has been done in the area of evaluation of surfaces machined with EDM using speckle metrology. In this investigation, an attempt has been made to evaluate the surface roughness of flat EDM specimens using speckle line images.

3. Experimental set-up and procedure In this investigation, twelve specimens of varying roughness were machined using EDM, by varying the Spark Current (A), the Time-on (ms) and Time-off (ms) conditions. The EN8 plain carbon steel was used as work piece material. The tool used was a cylindrical copper rod of diameter 20 mm. A typical machined surface is shown in Fig. 2. The roughness was measured using a conventional Taylor and Hobson surface roughness tester (Talysurf) for all the twelve parts machined with EDM. The instrument was having a stylus tip radius

of 2 mm. The cut-off value of 0.8 mm and a surface data length of 8.8 mm were used for measurement. Fig. 3 shows the roughness being measured by Talysurf surface roughness tester and Fig. 4 shows a sample profile along with the roughness parameters Ra, Rda and Rdq. 3.1. Image acquisition A special experimental set-up was designed and fabricated to hold the specimen, light source, and the camera. The design while providing sufficient rigidity for the firm holding of the light source and camera, also facilitated the fine adjustments for the work table for aligning the work-piece. The schematic arrangement of an image acquisition system and the experimental set-up for the image acquisition are shown in Figs. 5 and 6. Respectively. The parts machined with EDM specimen were mounted on the worktable and illuminated using a Quarton Laser Module VLM-635-– 27 LPT-10 Redline laser source. Basler CMOS camera, acA250014gm, having an optical magnification up to 45X was used to capture the image. The CMOS camera was mounted on the adjustable fixture of the set-up and set with its axis normal to the specimen surface. Surface speckle line images of the specimen were captured with the image resolution of 2592
1944 pixels. Fig. 7 shows the image of a specimen taken by Line laser source for different Ra, Rda, and Rdq values. After the image acquisition, the images were normalized to eliminate the variation in lighting conditions or fluctuation in acquisition of image which would very much affect the image processing. This was done to maintain the uniformity in image intensity and for the transformation of the image matrix. 3.2. Signal vector generation For each surface image, the image signal vector was arrived from the image signal matrix. Approximately the central region of the image was identified to derive the image signal vector. By selecting three consecutive pixel rows of the image intensity matrix equivalent to the stylus tip diameter and by taking mean of the pixel intensity of the respective columns, the image signal vector was obtained for a length of 2592 pixels. A MATLAB program was developed and image signals of all surface images were obtained and stored in the database. 3.3. Wavelet transform of the image signal

Fig. 2. Typical Surface machined by EDM.

The one-dimensional signal vector was processed using a wavelet transform. A six-level decomposition of the signal vector was

Fig. 3. Surface roughness tester measuring surface roughness.

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J. Mahashar Ali et al. / Measurement 149 (2020) 107029

Fig. 4. Sample roughness profile corresponding to the Time-on-5 ms, Time-off-5 ms and current 21A.

carried out using the bi-orthogonal wavelet ‘bior 1.1’ wavelet. Biorthogonal wavelets were extensively used in the literature for surface roughness measurement [1,35]. The wavelet transform of the one-dimensional signal vector measures the changes in the pixel intensity of the grey scale at different levels. The changes in the image intensity values contain data for surface roughness measurement. Fig. 8 shows a typical Matlab output of wavelet transformed signal vector. Chen et al., [36] had used wavelets as filters to specifically capture different components of surface texture. They had demonstrated that in a 12 level decomposition, the lower levels (i.e.) up to level 5, represented the error due to machine condition. They observed the level 6, 7 & 8 of the wavelet transform to capture the tool marks. The levels 9, 10 & 11 were found to represent the roughness error. In this paper a six level decomposition is used. Accordingly to capture the data pertaining to the roughness, the detailed coefficients of level 4 & 5 of the decomposed image signal were computed and used for the analysis. So the 4th and the 5th order detail coefficients of the decomposed image signal were found to contain the details about the surface roughness. This is because of the corresponding wavelengths synchronizing with the cut-off values used in the stylus measurements. The 4th and the 5th order detailed coefficients are further processed and the distribution parameters like root mean square (RMS) and the variance of these detailed coefficients were computed and tabulated. The tabulated values are presented in Table 1.

4.1. RMS and Variance of the 4th order detail coefficient vs Ra, Rda, and Rdq The plot between the RMS and Variance of the 4th order detail coefficient of the speckle image signal vector and the stylus parameters Ra, Rda, and Rdq is shown in Fig. 9(a & b), Fig. 10(a & b) and Fig. 11(a & b) It can be observed from the trends that the RMS and the Variance of the 4th order detail coefficient value of the image signal intensity vector correlate well with roughness parameters Ra, Rda, and Rdq. The graphs show a linear relationship between the two with R2 values (0.7742, 0.7804, & 0.7926) for RMS and the R2 values (0.783, 0.8005, & 0.8171) for the variance.

4. Results and discussion: The image-based parameters such as the RMS and the Variance of the 4th and the 5th order detail coefficients of the wavelet transformed one-dimensional signal vectors were obtained. The correlation between these parameters and the various stylus-based roughness parameters is being explored.

Fig. 5. Experimental set-up.

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J. Mahashar Ali et al. / Measurement 149 (2020) 107029

Fig. 6. Image acquisition from the experimental set-up.

Fig. 7. Speckle image of a specimen before normalization.

In this experiment, the surface was illuminated with a laser line source at a 45° angle to the plane of the surface. The axis of the camera was normal to the surface. The light source being monochromatic leads to a speckle image formation. A rough surface results a greater scattering of light. This leads to the formation of larger dark regions resulting the reduction in the RMS value of the image signal. Also a rougher surface results in lar-

ger patches of speckles resulting in the reduction in the variance, within the fixed measurement length. The key aspect of capturing the image based metric is to filter out variations arising out of other surface texture components like waviness and form error. While analyzing surface profiles, the fourth and fifth order detailed coefficients of the Biorthogonal wavelet transformed profiles are found to contain the parameters

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J. Mahashar Ali et al. / Measurement 149 (2020) 107029

Fig. 8. Matlab output of a wavelet transformed signal vector.

Table 1 Statistical parameters of 4th and 5th order detail coefficients for Ra, Rda, and Rdq. S. No

Time ON (ms)

Time OFF (ms)

Current (A)

Ra mm

Rda

Rdq

1 2 3 4 5 6 7 8 9 10 11 12

5 5 6 6 6 7 7 7 7 7 7 7

5 5 5 5 5 6 5 6 5 6 7 6

21 18 15 12 21 12 15 15 12 21 15 18

7.737 8.036 8.574 8.984 9.948 10.820 11.355 11.732 12.655 13.570 13.596 14.427

15.3 15.57 16.4 17.16 18.15 19.17 20.1 19.09 20.87 20.78 20.96 22.1

20.45 21.02 22.02 22.95 24.09 25.74 26.37 26.06 26.81 27.32 27.35 28.33

RMS Vs Ra 8

RMS

VAR

RMS

VAR

7.00335 7.55282 6.49161 7.01857 6.92742 5.11830 4.31362 5.40765 4.54371 3.48619 4.24626 4.91759

49.0659 57.0671 42.1573 49.2793 48.0077 26.2071 18.6145 29.2539 20.6533 12.1582 18.0377 24.1921

8.0290 7.3795 8.3878 10.4179 7.9898 5.6500 3.9263 6.2024 3.2654 4.5112 4.6369 4.4099

64.4898 54.4781 70.3833 108.574 63.8618 31.9353 15.4222 38.4854 10.6674 20.3591 21.5093 19.4549

60

4 2

y = 5.8368x + 96.823 R² = 0.783

50

Variance

RMS

5th order Detail Coefficient

Variance Vs Ra

y = 0.5143x + 11.219 R² = 0.7742

6

4th order Detail Coefficient

40 30 20 10 0

0 6

8

10

12

Ra (a)

14

16

6

8

10

12

14

16

Ra (b)

Fig. 9. (a & b) RMS and Variance of the 4th order detail coefficient of the speckle image signal vector and the stylus parameters Ra.

8

J. Mahashar Ali et al. / Measurement 149 (2020) 107029

representing the roughness [36]. This is analogues to deploying a filter in surface profiling. This enables the metric to capture the variation corresponding to surface roughness glossing over other waviness and form errors. The RMS and the variance values of these detailed coefficients were correlated with the surface roughness parameters Ra, Rda and Rdq obtained using the stylus instrument. The speckle image formulation happens because of the reflection of laser rays by various local surfaces of the profile. This phenomenon is more pronounced by the slope of various points on the specimen surface. Hence the parameters Rda and Rdq, were

expected to provide better correlation with the image based metrics. This is true in the correlation with Rda and Rdq. 4.2. RMS and Variance of the 5th order detail coefficient Vs. Ra, Rda, and Rdq The plot between the RMS and the Variance of the 5th order detail coefficient of the speckle image signal vector and the stylus parameters Ra, Rda, and Rdq is shown in Fig. 12(a & b), Fig. 13(a & b) and Fig. 14(a & b). The RMS and the Variance of the 5th order detail coefficient matrix of the image intensity vector also showed a good

RMS Vs Rda 8

y = 0.5243x + 15.444 R² = 0.7804

60

Variance

6

RMS

Variance Vs Rda

4 2

y = 5.992x + 145.57 R² = 0.8005

40 20

0

0 15

17

19

21

15

23

17

19

Rda (a) Fig. 10. (a & b) RMS and Variance of the 4

order detail coefficient of the speckle image signal vector and the stylus parameters Rda.

Variance Vs Rdq

RMS Vs Rdq 8

RMS

6

Variance

y = 0.4494x + 16.766 R² = 0.7926

4 2 0 22

23

(b) th

20

21

Rda

24

26

28

60 50 40 30 20 10 0

y = 5.1496x + 160.99 R² = 0.8171

20

30

22

24

Rdq

26

28

30

Rdq

(a)

(b)

Fig. 11. (a & b) RMS and Variance of the 4th order detail coefficient of the speckle image signal vector and the stylus parameters Rdq.

12 10 8 6 4 2 0

Variance Vs Ra

y = 0.7669x + 14.634 R² = 0.6535

6

8

10

12

Ra (a)

14

16

Variance

RMS

RMS Vs Ra 120 100 80 60 40 20 0

y = 9.8975x + 151.71 R² = 0.6048

6

8

10

12

14

16

Ra (b)

Fig. 12. (a & b) RMS and Variance of the 5th order detail coefficient of the speckle image signal vector and the stylus parameters Ra.

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J. Mahashar Ali et al. / Measurement 149 (2020) 107029

Variance Vs Rda

RMS Vs Rda 150 y = 0.7934x + 21.153 R² = 0.6786

10 5

Variance

RMS

15

0

y = 10.07x + 232.65 R² = 0.6073

100 50 0

15

17

19

21

23

15

17

19

21

23

Rda

Rda (a)

(b)

Fig. 13. (a & b) RMS and Variance of the 5th order detail coefficient of the speckle image signal vector and the stylus parameters Rda.

RMS Vs Rdq y = 0.6593x + 22.635 R² = 0.6476

10 5

150

Variance

RMS

15

Variance Vs Rdq y = 8.4557x + 253.65 R² = 0.5918

100 50 0

0 20

22

24

26

28

30

20

Rdq

22

24

26

28

30

Rdq

(a)

(b)

Fig. 14. (a & b) RMS and Variance of the 5th order detail coefficient of the speckle image signal vector and the stylus parameters Rdq.

correlation with the roughness parameters Ra, Rda, and Rdq. The graphs show a linear relationship between the two with the high R2 values (0.6535, 0.6786 & 0.6476) for RMS and the R2 values (0.6048, 0.6073 &0.5918) for the variance. It can also be observed that the metrics of the 4th order detail coefficient vector of the image pixel intensity with Ra, Rda and Rdq are having a good correlation compared with the 5th order detail coefficient vector. This is primarily because the wavelength pertaining to the 4th order detail coefficient vectors align with the cut-off value used for calculating the surface roughness values using the stylus instrument.

5. Conclusions An image-based surface roughness parameter for the noncontact and in-situ measurement of surface roughness is proposed.  In the proposed method, the surfaces machined by EDM were illuminated with a red line laser source and the speckle images were obtained. The 4th and 5th order detail coefficients were extracted as image based metric.  The fourth and the fifth level decompositions in its scale matched the cut-off value traditionally used in the stylus instrument.  The RMS and Variance of the 4th order detail coefficient were found to correlate well with the roughness parameters Ra, Rda, and Rdq. The experiments demonstrated a linear relationship between the two with R2 values of 0.7742, 0.7804, and 0.7926 for RMS and the R2 value of 0.783, 0.8005, and 0.8171 for the variance.

 The stylus hybrid parameters Rda and Rdq were found to correlate well with the speckle line image parameters as the slopes at various points on the free surface are responsible for the reflection of light rays.  The robustness of the metrics and its applicability to machining processes like grinding and polishing can be established through further experiments.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.measurement.2019.107029. References [1] X.J. Jiang, D.J. Whitehouse, Technological shifts in surface metrology, CIRP Ann.-Manuf. Technol. 61 (2012) 815–836. [2] D.J. Whitehouse, Handbook of Surface and Nanometrology, CRC Press, 2010. [3] E. Gadelmawla, M. Koura, T. Maksoud, I. Elewa, H. Soliman, Roughness parameters, J. Mater. Process. Technol. 123 (2002) 133–145. [4] T.V. Vorburger, J. Raja, Surface Finish Metrology, American Society for Precision Engineering, 1988. [5] S. Chen, R. Feng, C. Zhang, Y. Zhang, Surface roughness measurement method based on multi-parameter modeling learning, Measurement 129 (2018) 664– 676. [6] H. Kumar, J. Ramkumar, K. Venkatesh, Surface texture evaluation using 3D reconstruction from images by parametric anisotropic BRDF, Measurement 125 (2018) 612–633. [7] J. Liu, E. Lu, H. Yi, M. Wang, P. Ao, A new surface roughness measurement method based on a color distribution statistical matrix, Measurement 103 (2017) 165–178. [8] Z. Nan-Nan, Z. Jun, Surface roughness measurement based on fiber optic sensor, Measurement 86 (2016) 239–245.

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[9] B. Dhanasekar, B. Ramamoorthy, Evaluation of surface roughness using a image processing and machine vision system, J. Metrol. Soc. India 21 (2006) 9– 15. [10] B. Lee, Y. Tarng, Surface roughness inspection by computer vision in turning operations, Int. J. Mach. Tools Manuf. 41 (2001) 1251–1263. [11] H. Shahabi, M. Ratnam, Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach, Int. J. Adv. Manuf. Technol. 48 (2010) 213–226. [12] R. Kumar, P. Kulashekar, B. Dhanasekar, B. Ramamoorthy, Application of digital image magnification for surface roughness evaluation using machine vision, Int. J. Mach. Tools Manuf. 45 (2005) 228–234. [13] B. Cuka, M. Cho, D.-W. Kim, Vision-based surface roughness evaluation system for end milling, Int. J. Comput. Integr. Manuf. 31 (2018) 727–738. [14] M.K. Balasundaram, M.M. Ratnam, In-process measurement of surface roughness using machine vision with sub-pixel edge detection in finish turning, Int. J. Precis. Eng. Manuf. 15 (2014) 2239–2249. [15] W.W. Boles, B. Boashash, A human identification technique using images of the iris and wavelet transform, IEEE Trans. Signal Process. 46 (1998) 1185–1188. [16] D. de Martin-Roche, C. Sanchez-Avila, R. Sanchez-Reillo, Iris recognition for biometric identification using dyadic wavelet transform zero-crossing, Security Technology, 2001 IEEE 35th International Carnahan Conference on, IEEE, 2001, pp. 272-277. [17] L. Ma, T. Tan, Y. Wang, D. Zhang, Efficient iris recognition by characterizing key local variations, IEEE Trans. Image Process. 13 (2004) 739–750. [18] F.M. Alkoot, A review on advances in iris recognition methods, Int. J. Comput. Eng. Res. 3 (2012) 1–9. [19] J. Daugman, How iris recognition works, The essential guide to image processing, Elsevier, 2009, pp. 715–739. [20] J.G. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Mach. Intell. 15 (1993) 1148–1161. [21] C. Sanchez-Avila, R. Sanchez-Reillo, D. de Martin-Roche, Iris-based biometric recognition using dyadic wavelet transform, IEEE Aerosp. Electron. Syst. Mag. 17 (2002) 3–6. [22] A. Abhyankar, L.A. Hornak, S. Schuckers, Biorthogonal-wavelets-based iris recognition, Biometric Technology for Human Identification II, International Society for Optics and Photonics, 2005, pp. 59–68. [23] A. Abhyankar, S. Schuckers, Novel biorthogonal wavelet based iris recognition for robust biometric system, Int. J. Comput. Theory Eng. 2 (2010) 233. [24] A. Abhyankar, S. Schuckers, A novel biorthogonal wavelet network system for off-angle iris recognition, Pattern Recogn. 43 (2010) 987–1007. [25] X. Tang, H. Xiao, H. Ding, J. Liu, Surface roughness measurement based on image processing and image recognition, Comput. Simul. Modern Sci. (2009) 91–96. [26] R.C. Gonzalez, R.E. Woods, Digital Image Processing, Prentice hall Upper Saddle River, NJ, 2002. [27] T. Jeyapoovan, M. Murugan, Surface roughness classification using image processing, Measurement 46 (2013) 2065–2072. [28] W. Sweldens, Wavelets and the lifting scheme: A 5 minute tour, ZAMMZeitschrift fur Angewandte Mathematik und Mechanik 76 (1996) 41–44. [29] S. Mallat, Zero-crossings of a wavelet transform, IEEE Trans. Inf. Theory 37 (1991) 1019–1033. [30] S. Mallat, Wavelets for a vision, Proc. IEEE 84 (1996) 604–614. [31] S.G. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (1989) 674–693. [32] S.G. Mallat, Multifrequency channel decompositions of images and wavelet models, IEEE Trans. Acoust. Speech Signal Process. 37 (1989) 2091–2110. [33] B. Josso, D.R. Burton, M.J. Lalor, Wavelet strategy for surface roughness analysis and characterisation, Comput. Methods Appl. Mech. Eng. 191 (2001) 829–842. [34] A. Zawada-Tomkiewicz, Estimation of surface roughness parameter based on machined surface image, Metrol. Meas. Syst. 17 (2010) 493–503.

[35] X. Jiang, P. Scott, D. Whitehouse, Wavelets and their applications for surface metrology, CIRP Ann.-Manuf. Technol. 57 (2008) 555–558. [36] X. Chen, J. Raja, S. Simanapalli, Multi-scale analysis of engineering surfaces, Int. J. Mach. Tools Manuf. 35 (1995) 231–238. [37] G. Le Goïc, M. Bigerelle, S. Samper, H. Favrelière, M. Pillet, Multiscale roughness analysis of engineering surfaces: a comparison of methods for the investigation of functional correlations, Mech. Syst. Sig. Process. 66 (2016) 437–457. [38] X. Jiang, L. Blunt, K. Stout, Development of a lifting wavelet representation for surface characterization, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, The Royal Society, 2000, pp. 2283-2313. [39] X. Jiang, L. Blunt, K. Stout, Application of the lifting wavelet to rough surfaces, Precis. Eng. 25 (2001) 83–89. [40] M. Zhong, F. Chen, C. Xiao, Y. Wei, 3-D surface profilometry based on modulation measurement by applying wavelet transform method, Opt. Lasers Eng. 88 (2017) 243–254. [41] M. Pour, Simultaneous application of time series analysis and wavelet transform for determining surface roughness of the ground workpieces, Int. J. Adv. Manuf. Technol. 85 (2016) 1793–1805. [42] M. Pour, Determining surface roughness of machining process types using a hybrid algorithm based on time series analysis and wavelet transform, Int. J. Adv. Manuf. Technol. (2018) 1–17. [43] K. Lingadurai, M. Shunmugam, Metrological characteristics of wavelet filter used for engineering surfaces, Measurement 39 (2006) 575–584. [44] S.I. Chang, J.S. Ravathur, Computer vision based non-contact surface roughness assessment using wavelet transform and response surface methodology, Qual. Eng. 17 (2005) 435–451. [45] E. Kayahan, H. Oktem, F. Hacizade, H. Nasibov, O. Gundogdu, Measurement of surface roughness of metals using binary speckle image analysis, Tribol. Int. 43 (2010) 307–311. [46] M. Nicklawy, A. Hassan, M. Bahrawi, N. Farid, A.M. Sanjid, Characterizing surface roughness by speckle pattern, analysis (2009). [47] Y. Wang, F. Xie, S. Ma, L. Dong, Review of surface profile measurement techniques based on optical interferometry, Opt. Lasers Eng. 93 (2017) 164– 170. [48] T. Mathia, P. Pawlus, M. Wieczorowski, Recent trends in surface metrology, Wear 271 (2011) 494–508. [49] U. Persson, Surface roughness measurement on machined surfaces using angular speckle correlation, J. Mater. Process. Technol. 180 (2006) 233–238. [50] S.C. Schneider, S. Rupitsch, B.G. Zagar, A new set of signal processing algorithms in laser speckle metrology, Instrumentation and Measurement Technology Conference, 2004. IMTC 04. Proceedings of the 21st IEEE, IEEE, 2004, pp. 1338-1343. [51] G.S. Spagnolo, D. Paoletti, A. Paoletti, D. Ambrosini, Roughness measurement by electronic speckle correlation and mechanical profilometry, Measurement 20 (1997) 243–249. [52] W. Kapłonek, K. Nadolny, Laser method based on imaging and analysis of scattered light used for assessment of cylindrical surfaces after dynamic burnishing process, Int. J. Surf. Sci. Eng. 10 (2016) 55–72. [53] B. Dhanasekar, B. Ramamoorthy, Digital speckle interferometry for assessment of surface roughness, Opt. Lasers Eng. 46 (2008) 272–280. [54] Z. Gao, X. Zhao, Roughness measurement of moving weak-scattering surface by dynamic speckle image, Opt. Lasers Eng. 50 (2012) 668–677. [55] F. Rodríguez, I. Cotto, S. Dasilva, P. Rey, K. Van der Straeten, Speckle characterization of surface roughness obtained by laser texturing, Procedia Manuf. 13 (2017) 519–525. [56] M. Dias, D. Dornelas, W. Balthazar, J. Huguenin, L. da Silva, Lacunarity study of speckle patterns produced by rough surfaces, Physica A 486 (2017) 328–336. [57] G.S. Spagnolo, L. Cozzella, F. Leccese, Viability of an optoelectronic system for real time roughness measurement, Measurement 58 (2014) 537–543.