art-3A10.1007-2Fs12541-016-0088-7

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 17, No. 6, pp. 709-715 JUNE 2016 / 709

© KSPE and Springer 2016

Prediction of Surface Roughness using Spectral Analysisand Image Comparison of Audio Signals

R. Panneer1,#, S. P. Harisubramanyabalaji1, C. A. Sribalaji1, A. Vivek1, and G. Vigneshwaran1

1 School of Mechanical Engineering, SASTRA University, Thirumalasamudram, Thanjavur, Tamilandu, 613401, India# Corresponding Author / E-mail: [email protected], TEL: +91-9566730200

KEYWORDS: Audio signals, Histogram, MATLAB, Microphone, Spectrogram, Surface roughness

The aim of this work is to design an off-line system, method and experimental set-up for predicting surface roughness (Ra) of metal

surfaces with the help of audio signals. The frictional contact between a metal surface and sharp pencil like scratching tool will

produce audio signals which vary based on the roughness of the surface. The samples considered to design and validate the concept

are work pieces machined with metal cutting processes such as Turning and Grinding. Several audio signals are generated from

various types of metal surfaces produced by these processes after the completion of the machining process away from the machining

area in an enclosed chamber. The audio waves are captured with the help of a microphone fixed inside the chamber. These audio

signals are processed to generate the surface pattern of the relevant surface. The audio signals are then converted to spectrogram

and normalized histogram plots with the help of MATLAB, based on which the roughness of the surfaces is predicted. An experimental

set-up is designed which provides a sound-proof environment to capture and record the audio signals. The proposed system, method

and set-up are validated with the actual surface roughness of the chosen surfaces measured with the help of a surface roughness

measurement instrument.

Manuscript received: December 10, 2015 / Revised: February 1, 2016 / Accepted: February 11, 2016

1. Introduction

Quality of any surface is specified with the help of three major

parameters such as surface roughness, waviness, and lay. Surface

Roughness is the very small deviation of the surface from the intended

flat ideal plane. The most important aim of manufacturing industry is

to produce surfaces with relevant surface finish suitable to the function

of the surfaces because apart from dimensions and geometry, surface

roughness plays a major role in realizing the function of the surface.

Therefore both online and offline measurement of surface finish is

required in the manufacturing industries. There are many parameters

available for the measurement of surface finish among which the Average

Roughness (Ra) is broadly used by the Industry.1 For measurement of

Ra both contact and non-contact methods are used. Nano indenters,

Rutherford Backscattering Spectroscopy, Dektak Talysurf, are various

methods/devices which employ the contact method.2-4 Generally the

contact type methods use probes which glide over the concerned metal

surface to measure the Ra value.5,6 The limitation of the contact method

is that the probe used for measurement is very petite, fragile and of

high cost. It wears out quickly if it is continuously used on a metallic

surface of high surface roughness. Optical Microscopy, Interferometry,

Confocal and Atomic Force Microscopy, Surface Topography, Image

Processing, Holographic Contouring, and Ultrasound Reflectometry are

various methods/devices which employ the non-contact method.7-16

Non-Contact methods are considered to be the nondestructive type of

measurements in which, mostly a light source like laser or sound is

made to fall on the surface and the backscattered waves and light are

absorbed and analyzed to get the Ra value of the surface. It is observed

that the cheapest available contact type surface roughness tester in the

market is Mitutoyo 178-561-02A Surftest SJ-210 costs around INR 0.14

million. The cheapest available non-contact type Microscope that can

measure surface roughness costs a minimum of INR 0.20 million which

NOMENCLATURE

Ra = Arithmetic Mean Deviation of the Profile

µm = micrometer

DOI: 10.1007/s12541-016-0088-7 ISSN 2234-7593 (Print) / ISSN 2005-4602 (Online)

710 / JUNE 2016 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 17, No. 6

may not be economical to small scale industries for offline measurement

of non-critical surfaces. The proposed device will cost only around INR

0.02 million for a single device. In case of mass production, the cost of

the device will further get reduced. In the proposed method, sound is

generated by the dry friction contact between the finished work piece

and a sharp pencil like tool in an experimental set-up outside the

machine that encloses the surface to be measured and the microphone

in a sound-proof chamber. The sound can be captured with the help of

microphone without the noise interruption and analyzed using

MATLAB.17 Though the proposed method is a contact type

measurement, it can be deployed in the areas of surfaces which are not

functionally critical. Hence this method will reduce the cost of

measurement without compromising quality.

2. Methodology

The method used is based on the theory that when two surfaces are

rubbed against each other, audio signals are generated whose

characteristics depend on the roughness of the two rubbing surfaces.18

Since this method uses audio signals, a microphone is used to acquire

the sound waves generated from the dry friction contact between a tool

and the sample surface considered. The microphone is placed at a

distance of 3 cm from the contact point of the tool and the sample. The

dry friction contact is made for a sampling length of 10 cm. The

microphone is selected with less attenuation value so that the sound

that is to be captured is not attenuated. The noise from the surrounding

is removed with the help of microphone parameter adjuster so that

there will not be any mixing of external noise. The force applied on the

tool and speed of movement of the tool should be uniform to get a good

quality audio signal. Moving the tool over the surface manually may

lead to the non-uniform speed and force which may lead to deep cuts

(cuts > 1 mm) and damage to the surface. The intensity of pressure of

the tool on the surface at various points from beginning to the end of

the tool-metal contact should not vary. Hence the metallic surface is

moved under the tool with constant velocity and applied force for a pre-

determined distance without any deep cuts using a motor. Once the audio

signals are generated because of the frictional contact between the

sample surface and the tool, they are transferred to MATLAB software.

To know the intensity range of the audio signals, spectrogram plot is

generated using MATLAB. Since different surface roughness give rise to

sounds of different intensities, spectrogram plot is made to show that

difference. To get the surface roughness values of the surfaces, a histogram

plot is generated. Histogram plot gives a plot in the form of number of

pixels vs. intensity of the color values, from the spectrogram plot. This

method uses image comparison technique to give the error value between

the known roughness value of a sample and unknown roughness value

of a sample. The roughness value of the surface for which the error is

least is predicted as the roughness of the unknown sample.

3. Experimental Set-Up

Fig. 1 shows a hollow rectangular base which is raised to a height

to avoid external vibrations and to provide rigid support. This base

contains two parallel groves which holds set of steel balls. These steel

balls provide smooth movement of the movable base plate which is

placed over them. As shown in Fig. 2, a lead screw is fixed in between

the parallel groves and bottom of the base plate. The lead screw is

operated by a motor fixed at one end of the lead screw. When the lead

screw is rotated, the movable base plate translates along the length of

the lead screw. The sample is fixed using a clamping arrangement fixed

to the movable base plate. The clamping arrangement is provided with

fixed and adjustable jaws that enables the mounting of samples of

various lengths. Fig. 3 shows a semi sound-proof chamber which

remains stationary around the testing area. Fig. 4 shows the upper

sound-proof chamber which provides insulation from external sound.

This upper sound proof chamber is attached to the raised supports on

the base plate. It is capable of moving in the vertical direction. The

upper soundproof chamber when brought down encloses the lower

stationary semi sound-proof chamber and provides double insulation

from external sound at the base level and enhances the quality of the

sound captured by the microphone. A microphone and a tool attached

to the upper sound-proof chamber as shown in Fig. 5 reaches the base

level when the chamber is lowered. The tool and the microphone

remain stationary while the movable base plate along with the metallic

sample moves and creates the sliding friction contact between the tool

and sample.

Fig. 1 Sliding mechanism

Fig. 2 Movable base plate attached to the sliding mechanism

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 17, No. 6 JUNE 2016 / 711

4. Experimentation

For this work, sample Mild Steel Cylindrical Pieces of Ø 25 mm ×

150 mm are machined using cylindrical grinding and turning operation.

Totally 10 samples are made, 5 pieces ground in Cylindrical Grinding

Machine and 5 pieces Turned in Conventional Lathe. Each sample is

kept in the prototype of the device and sound is generated by making

dry friction contact with the pencil like sharp tool as explained below.

Each sample is placed in the clamping mechanism inside the semi-sound

proof chamber and clamped. After the upper sound proof chamber is

brought down to cover the sample and semi-sound proof chamber, the

motor is operated which rotates the lead screw and thereby moves the

sample against the bottom of the tool. The tool is placed perpendicular

to the sample’s axis to generate a sound of superior quality. The sound

produced by the movement of the sample surface under the tool due to

friction is recorded by the microphone. This procedure is repeated for

all the 10 samples and the audio signals are recorded, which is then

processed in MATLAB. The major parameters of measurement are the

tool sharpness and speed of movement. As the measurement is made

only for 10 samples, the sharpness is assumed to be constant and linear

speed is 0.01 m/sec. The sound is generated three times for each sample

for a sampling length of 10 cm on the same spot and audio signals are

generated. It is observed that the signals are almost same.

The surface roughness values of all the ten samples are determined

using a portable “Touch” type surface finish measuring instrument

manufactured by “Mitutoyo, Japan”. The roughness values are determined

on the same spots where audio signals are generated and the average

value of three measurements is considered. One sample each from turning

and grinding is assumed as unknown sample but with knowledge that

the value of its roughness lies between the ranges of roughness values

of other four known samples. A spectrogram is plotted for each of the

predetermined eight known samples (four each from each operation) and

also for the unknown samples. The spectrogram of the unknown sample’s

signal is compared with the spectrogram of the known sample’s signal

by comparing their normalized histogram values. The Euclidian distance

is calculated for each case and the known sample with the least error

is predicted to have the roughness of the unknown sample.

5. Results and Discussions

5.1 Spectrogram plots

The Spectrogram plot19 is made taking frequency along Y-axis and

Time along X-axis. The portion which is Red in colour indicates the

area where the intensity of sound is higher and the portion which is

Yellow in colour indicates the area where the intensity of sound is

comparatively less. The Spectrogram plots of audio signals obtained

during dry friction of Tool on ground surfaces are presented in Figs. 6

to 10. (10 represents the plot for which Roughness value is assumed

unknown). Similarly the Spectrogram plots of the audio signals

obtained during dry friction of tool on turned surfaces are presented in

Figs. 11 to 15. (15 represents the plot for which Roughness value is

assumed unknown).

5.2 Histogram plots

Depending on the density of the Red color in the Spectrogram plot,

the final normalized histogram plot is generated. The comparison of the

unknown sample’s histogram plot with the known sample’s histogram

plot gives the difference in intensity of the plot as the error value. The

comparison of the pair in which a least error value obtained is taken as

the nearest value of the Surface Roughness of the unknown sample. The

Fig. 3 Semi Sound-proof arrangement remains stationary around the

testing area

Fig. 4 Upper sound-proof chamber and microphone attached to it

Fig. 5 Emery tool attached to the upper sound-proof chamber beside

the microphone


Fig. 6 Spectrogram plot for ground surface - sample 1




Fig. 10 Spectrogram plot for ground surface - unknown sample

Fig. 11 Spectrogram plot for turned surface - sample 1

Fig 12 Spectrogram plot for turned surface - sample 2



Fig. 15 Spectrogram plot for turned surface - unknown sample


Figs. 16 to 19 represents the Histogram plot obtained by comparison of

the Unknown ground sample with the known ground Samples.

5.3 Prediction of surface finish

Table 1 represents the values of Surface Roughness of the ground

samples obtained from the Roughness Measurement Instrument and the

error values as compared to the unknown sample obtained from the

Normalized Histogram Plot. From Table 1, it is observed that the least

error value (1.2446×10-4) is for Sample 1 and hence the Surface

Roughness Value of the unknown ground sample is predicted as 0.23

µm.

Table 2 represents the values of Surface Roughness of the turned

samples, obtained from the Roughness Measurement Instrument and

the error values as compared to the unknown sample obtained from the

Normalized Histogram Plot. From Table 2, it is observed that the least

error value (0.49776×10-4) is for Sample 2 and hence the Surface

Roughness Value of the unknown turned sample is predicted as, 2.98

µm.

Fig. 16 Comparison of histogram plots of the unknown sample with

sample 1 (ground surface)



Fig.18 Comparison of histogram plots of the unknown sample with





sample 1 (turned surface)

Fig 21 Comparison of histogram plots of the unknown sample with







6. Validation

The unknown ground sample’s surface roughness is measured using

a surface roughness measurement instrument and observed that the

roughness value is 0.25 µm. From Table 1, it is predicted that the

Roughness Value of the unknown ground sample is 0.23 µm which is

nearly equal to the actual Roughness of sample 1. The unknown turned

sample’s surface roughness is measured using a surface roughness

measurement instrument and observed that the roughness value is 3.14

µm. From Table 2, it is predicted that the Roughness Value of the

unknown turned sample is 2.98 µm which is nearly equal to the actual

Roughness of sample 2. Table 3 presents the actual and predicted

roughness values of the unknown samples.

There is a good correlation between the measured surface roughness

and signals frequency. This correlation scientifically proves that the

undulations in the surface caused by the type of manufacturing process

will vary the frequency of the audio signals generated. Based on this

scientific principle it can be established that using the audio signals for

predicting surface roughness is a reliable method within an accuracy

level of ±8%, which can be further improved by increasing the number

of samples.

7. Conclusions

The audio signals produced by a pair of tool and metallic surface

vary in frequency and intensity based on the surface roughness which

can be used to generate spectrograms and histograms. The histograms

of surfaces with known roughness values can be compared with

Histograms of unknown roughness values. Based on the errors observed,

the surface roughness value of the unknown surface can be predicted.

Even though this method does not measure the surface roughness like

a measuring instrument, it is very useful to predict/compare the surface

roughness during the manufacturing process before the final machining

is done, or as an in process measurement. It can be also used to predict

roughness of surfaces which are not functionally critical where

inaccuracy up to ±10% can be tolerated. This method is a simple and a

economical method to predict surface roughness. The major limitation

is that it cannot be used inside the machine shop as it needs sound proof

environment. Further, if we make the system perfectly sound proof,

accuracy of the output signal can be increased and thereby the accuracy

of roughness measurement can be increased. The accuracy of this

method also increases with more number of known samples.

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Sample

No.

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finish (Ra)

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histogram pair ×10-4

1 Grinding 0.23 µm 1.2446




Table 2 Error values from histogram pair-turned samples

Sample

No.

Machining

process

Measured surface

finish (Ra)

Obtained error values from

histogram pair ×10-4

1 Turning 5.1 µm 7.3936

2 Turning 2.98 µm 0.49776

3 Turning 2.60 µm 2.44310

4 Turning 2.85 µm 0.72949

Table 3 Comparison of measured and predicted Ra

SampleMeasured/actual

surface finish (Ra)

Predicted surface

roughness (Ra)

Ground sample 0.25 µm 0.23 µm

Turned sample 3.14 µm 2.98 µm


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art-3A10.1007-2Fs12541-016-0088-7