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Acoustic characterization and prediction of surface roughness
Journal of Materials Processing Technology 152 (2004) 127–130
Acoustic characterization and prediction of surface roughness Sanjay Kumar Singh, K. Srinivasan, D. Chakraborty∗ Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, North Guwahati, Guwahati 781039, India Received 8 January 2003; accepted 26 March 2004
Abstract The present paper deals with a new methodology for predicting surface roughness of engineering surfaces. A contactor has been rubbed against a surface, and the friction noise thus generated is related with the roughness of the surface. The amplitude of friction noise and the magnitude of contact force have been utilized to train a back propagation neural network for future prediction. It has been observed that the neural network can learn the correlation between the friction noise, contact force and the surface roughness and could effectively be used in practical applications to predict surface roughness. © 2004 Elsevier B.V. All rights reserved. Keywords: Acoustic characterization; Surface roughness; Neural network
1. Introduction The importance of control of surface roughness together with dimensional accuracy has been realized in the recent past. It is a generally accepted fact that surface finish greatly influences the functioning of machine parts. It also affects the resistance to wear, load carrying capacity, tool life, resistance to corrosion, fatigue resistance and ability to hold pressure and noise reduction in case of gears. Even the extremely smooth surfaces generated using sophisticated techniques suffer from surface imperfections beyond certain level of microscopic observations. The imperfections assume the form of a succession of hills and valleys, which may vary both in height and in spacing, and result in a kind of roughness which, in appearance or feel, is often a characteristic of the machining process and its accompanying defects. Therefore, the most important parameter describing surface integrity is surface roughness. In the manufacturing industry, surfaces must be within certain limits of roughness. Therefore, measuring surface roughness is vital to quality control of the machining work piece. There have been a lot of work reported in the literature, where acoustic emission (AE) techniques have been used for fault/damage detection in components (refer Dunegan [1] and Huang et al. [2]). However not many works have been reported in acoustic characterization of surface rough-
∗ Corresponding author. Tel.:+91-0361-2582666; fax: +91-0361-2690762. E-mail address: [email protected] (D. Chakraborty).
0924-0136/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2004.03.023
ness. Dunegan [3] used AE to detect the surface roughness by attaching a transducer to a plate for detecting the signals created by moving contact of the human finger along the test surface. He found that by splitting the received signal into two frequency bands and taking the ratio of the high frequency to low frequency components of the voltage amplitude (HF/LF ratio) it is possible to relate the ratio to the surface roughness. While work has been reported in the direction of acoustic characterization of surface roughness, not much work is available on the prediction of surface roughness using pertinent parameters. In the present work, an attempt has been made to use acoustic emission in conjunction with artificial neural network for online prediction of surface roughness of engineering surfaces. Therefore, the present paper aims at correlating the friction noise and contact force between two surfaces with the surface roughness and then using artificial neural network to predict the surface roughness from the friction noise. The methodology adopted and the experimental setup used in the present work are discussed in detail in the following sections.
2. Experimental procedure Experimental setup for the present work has been shown in Fig. 1. The method used is based on the principle that two surfaces rubbing against each other lead to the generation of noise, whose characteristics depend on the nature of the two rubbing surfaces. Apart from noise, contact force between the rubbing surfaces is another important parameter, whose
128
S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
Fig. 1. Experimental setup for recording noise signal and contact force.
magnitude depends on the friction coefficient, which in turn, is dictated by the degree of surface roughness. Therefore, in the present work, the test surface is rubbed against a testing rod, and both the friction noise characteristics as well as the contact force have been measured to correlate with the surface roughness. Mild steel plates of different thickness (2, 3 and 8.5 mm) were used in the present work. The total area for each plate was 150 mm × 100 mm and the test area in each plate was restricted to 30 mm × 20 mm. An electronic surface-measuring instrument called “Pocket Surf” manufactured by Mahr, Germany, has been used to measure the surface roughness of the plate. The range of measurement of the device is 0.5–6.35 m as specified by the manufacturer. It measures roughness by centerline average method providing output in micrometers. In each plate, the measurement of surface roughness has been done in two directions—length and width. A force transducer along with an impact hammer has been used to measure the force. The contacting device (henceforth referred to as “contactor”) is made of mild steel rod having a pointed nose at one end as shown in Fig. 2. The rod was hardened by heat treatment. This contactor was con-
Fig. 2. Schematic diagram of the mild-steel contactor.
nected to the detachable end of the impact hammer. On rubbing of the contactor against the steel plate, a contact force as well as friction noise is generated. The force transducer has been used to measure the contact force, thus produced. The friction noise produced due to rubbing of the contactor on the plate is measured by the microphone (B&K 4138) with frequency range (10 Hz–140 kHz). The signals obtained in both the cases are stored as frequency domain signals. Since signals obtained during rubbing also contained background noise, the signals of both friction as well as the background noise were recorded. The maximum amplitude of force signals spectrum has been recorded, which gives maximum force produced during rubbing of the contactor against the surface. To remove the effect of background noise from the microphone signals, the spectrum of background noise was subtracted from the spectrum of the signals generated during rubbing. From the subtracted spectrum of frictional noise, the maximum amplitude of noise and the corresponding frequency were recorded. Apart from the peak amplitude of the microphone signal, the standard deviation of all amplitudes is taken as another parameter. Therefore, for each case of rubbing, the maximum amplitude, corresponding frequency, standard deviation of amplitude of microphone signals and the maximum force recorded by the force transducer were stored to characterize the noise signal and the contact force. For each such case, the corresponding surface roughness was measured by the pocket surf.
S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
3. Artificial neural network for roughness prediction Artificial neural network architecture comprises mainly parallel adaptive processing elements with hierarchical structured interconnected networks. Each processing unit of an ANN has multiple input slots and a single output slot (Fig. 2). The relationship between the input and the output signals are usually formulated as follows: Oi = f(yi ) = yi =
l
1 1 + exp(yi )
wij Ii − θi
(1)
(2)
i=1
where Oj is the output signal of the jth unit, yj the potential of the jth unit, f( ) the activation function that is a sigmoidal function in this case, wij the connection weights between ith and jth units, θ j the threshold value of the jth unit and l the number of input signals. Fig. 3 shows a three-layer neural network. All the units are formed into a multiple layers, i.e. an input layer, a hidden layer and an output layer. The basic idea of training a neural network is as follows. First, the square error of the pth training pattern Ep is defined as Ep =
1 2
m
(Tpk − Opk )
2
(3)
k=1
where Tpk is the teacher signal (desired output) to the kth output unit for pth training pattern, Opk the output signal to the kth output unit for pth training pattern and m the number of output units. In the training process, wji and θ j are modified repeatedly based on gradient descent method to minimize the above error. This modification proceeds downward. Through such an iterative process, the network attains the
129
ability to promptly output the similar signal to the teacher’s one. This training algorithm is called back propagation neural network (BPNN). Since the friction noise and contact force contain information about the surface roughness, these two parameters have been used for training the neural network for subsequent prediction. This information may be extremely difficult to extract by traditional methods as the rules governing the cause and effect relationship must be established explicitly and a methodology for using these rules must be developed a priori. So, in the proposed approach, the training process is used to extract the cause and effect relationships, which are then stored in the connection strengths of an appropriate BPNN. Thus the learning capability of neural network eliminates the need for explicitly extracting the cause–effect relationships. These relationships exist in a distributed way within the connection strengths of the neural network but are not obvious or externally available to the user. In a typical application of BPNN, the following steps are involved: (i) training the network; (ii) deciding the appropriate architecture of the network; (iii) testing the network to verify how well it has learnt the training cases. These steps are discussed in detail with respect to the present work in the subsequent paragraphs. As stated earlier, BPNN architecture was used in this investigation. After trying many combinations of parameters, it was decided to use the following input parameters for training the neural network: • • • •
maximum amplitude of the friction noise; frequency corresponding to maximum amplitude; standard deviation of acoustic signals; maximum contact force recorded by force transducer.
Fig. 3. Layout of the three-layer back propagation neural network.
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S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
Table 1 Training and testing error for different combinations of number of nodes in hidden layer, η and α Number of nodes on hidden layer
η
α
Number of iterations
Mean-square training error
Mean-square testing error
4 4 4 4 4 3 5 6 7
0.3 0.4 0.5 0.5 0.7 0.3 0.3 0.3 0.3
0.4 0.5 0.5 0.7 0.9 0.4 0.4 0.4 0.4
3267 3267 3267 3267 3267 3267 3267 25618 11991
0.019586 0.019384 0.019219 0.019455 0.023850 0.019505 0.018805 0.018793 0.019228
0.020781 0.020933 0.020993 0.020962 0.021382 0.020846 0.021006 0.021026 0.020619
The surface roughness measured by the pocket surf was used as the desired output of the BPNN. The network thus consists of four input nodes and one output node. The contactor has been rubbed on machined mild steel surfaces and around 300 data sets were generated. Each data set contained all the aforementioned input and output parameters. These data sets are then normalized between 0.1 and 0.9 using the following equation: x − xmin y = 0.1 + 0.8 (4) xmax − xmin where xmax and xmin are maximum and minimum values of a particular parameter in the entire data set, respectively. These normalized data sets are then used to train the BPNN. For better training of the network, the data sets were shuffled (randomly re-arranged).
Fig. 4. Training and testing error vs. number of iterations.
4. Results and discussion
5. Conclusions
From the data set 70% were selected at random and are used to train the network and the remaining 30% were used to test the performance of the network. It was found by trial and error that one hidden layer was optimum for the present neural network architecture. The number of nodes in the hidden layer was decided by trial and error based on comparative performance of large number of network architectures (few of them have been shown in Table 1). Based on the trial runs, the network architecture decided in the present case was 4-4-1. Various combinations of the learning rate (η), the momentum parameter (α) and the number of hidden nodes have been tried to optimize the learning, and based on their relative performances (Table 1), the values η = 0.3 and α = 0.3 have been found to be most suitable. For this network, variation of training and testing error with number of iterations is shown in Fig. 4. It could be observed that the network could learn the pattern reasonably well. Thus, BPNN could be used for prediction of surface roughness of machined components by monitoring the contact force between the contactor and the surface and the friction noise generated during rubbing.
A methodology has been proposed for predicting surface roughness using noise signal and contact force generated during sliding of two surfaces in conjunction with artificial neural network. The data obtained from the experiments demonstrate that the neural network could learn the pattern for future prediction of surface roughness. It is expected that in practical applications where data volumes could be large will lead to a better training and hence a better performance of the system developed in the present work.
References [1] H.L. Dunegan, Use of plate wave analysis in acoustic emission testing to detect and measure crack growth in noisy environments, in: Proceedings of the International Conference on Structural Materials Technology and NDT, San Diego, California, 20–23 February 1996. [2] M. Huang, L. Jiang, P.K. Liaw, C.R. Brooks, R. Seeley, D.L. Kiarstrom, Using acoustic emission in fatigue and fracture materials research, JOM 50 (11) (1998) (Web edition). [3] H.L. Dunegan, An Acoustic Emission Technique for Measuring Surface Roughness, The DECI Report, Publication of Dunegan Engineering Consultants Inc., December 1998.
Acoustic characterization and prediction of surface roughness Sanjay Kumar Singh, K. Srinivasan, D. Chakraborty∗ Department of Mechanical Engineering, Indian Institute of Technology, Guwahati, North Guwahati, Guwahati 781039, India Received 8 January 2003; accepted 26 March 2004
Abstract The present paper deals with a new methodology for predicting surface roughness of engineering surfaces. A contactor has been rubbed against a surface, and the friction noise thus generated is related with the roughness of the surface. The amplitude of friction noise and the magnitude of contact force have been utilized to train a back propagation neural network for future prediction. It has been observed that the neural network can learn the correlation between the friction noise, contact force and the surface roughness and could effectively be used in practical applications to predict surface roughness. © 2004 Elsevier B.V. All rights reserved. Keywords: Acoustic characterization; Surface roughness; Neural network
1. Introduction The importance of control of surface roughness together with dimensional accuracy has been realized in the recent past. It is a generally accepted fact that surface finish greatly influences the functioning of machine parts. It also affects the resistance to wear, load carrying capacity, tool life, resistance to corrosion, fatigue resistance and ability to hold pressure and noise reduction in case of gears. Even the extremely smooth surfaces generated using sophisticated techniques suffer from surface imperfections beyond certain level of microscopic observations. The imperfections assume the form of a succession of hills and valleys, which may vary both in height and in spacing, and result in a kind of roughness which, in appearance or feel, is often a characteristic of the machining process and its accompanying defects. Therefore, the most important parameter describing surface integrity is surface roughness. In the manufacturing industry, surfaces must be within certain limits of roughness. Therefore, measuring surface roughness is vital to quality control of the machining work piece. There have been a lot of work reported in the literature, where acoustic emission (AE) techniques have been used for fault/damage detection in components (refer Dunegan [1] and Huang et al. [2]). However not many works have been reported in acoustic characterization of surface rough-
∗ Corresponding author. Tel.:+91-0361-2582666; fax: +91-0361-2690762. E-mail address: [email protected] (D. Chakraborty).
0924-0136/$ – see front matter © 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.jmatprotec.2004.03.023
ness. Dunegan [3] used AE to detect the surface roughness by attaching a transducer to a plate for detecting the signals created by moving contact of the human finger along the test surface. He found that by splitting the received signal into two frequency bands and taking the ratio of the high frequency to low frequency components of the voltage amplitude (HF/LF ratio) it is possible to relate the ratio to the surface roughness. While work has been reported in the direction of acoustic characterization of surface roughness, not much work is available on the prediction of surface roughness using pertinent parameters. In the present work, an attempt has been made to use acoustic emission in conjunction with artificial neural network for online prediction of surface roughness of engineering surfaces. Therefore, the present paper aims at correlating the friction noise and contact force between two surfaces with the surface roughness and then using artificial neural network to predict the surface roughness from the friction noise. The methodology adopted and the experimental setup used in the present work are discussed in detail in the following sections.
2. Experimental procedure Experimental setup for the present work has been shown in Fig. 1. The method used is based on the principle that two surfaces rubbing against each other lead to the generation of noise, whose characteristics depend on the nature of the two rubbing surfaces. Apart from noise, contact force between the rubbing surfaces is another important parameter, whose
128
S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
Fig. 1. Experimental setup for recording noise signal and contact force.
magnitude depends on the friction coefficient, which in turn, is dictated by the degree of surface roughness. Therefore, in the present work, the test surface is rubbed against a testing rod, and both the friction noise characteristics as well as the contact force have been measured to correlate with the surface roughness. Mild steel plates of different thickness (2, 3 and 8.5 mm) were used in the present work. The total area for each plate was 150 mm × 100 mm and the test area in each plate was restricted to 30 mm × 20 mm. An electronic surface-measuring instrument called “Pocket Surf” manufactured by Mahr, Germany, has been used to measure the surface roughness of the plate. The range of measurement of the device is 0.5–6.35 m as specified by the manufacturer. It measures roughness by centerline average method providing output in micrometers. In each plate, the measurement of surface roughness has been done in two directions—length and width. A force transducer along with an impact hammer has been used to measure the force. The contacting device (henceforth referred to as “contactor”) is made of mild steel rod having a pointed nose at one end as shown in Fig. 2. The rod was hardened by heat treatment. This contactor was con-
Fig. 2. Schematic diagram of the mild-steel contactor.
nected to the detachable end of the impact hammer. On rubbing of the contactor against the steel plate, a contact force as well as friction noise is generated. The force transducer has been used to measure the contact force, thus produced. The friction noise produced due to rubbing of the contactor on the plate is measured by the microphone (B&K 4138) with frequency range (10 Hz–140 kHz). The signals obtained in both the cases are stored as frequency domain signals. Since signals obtained during rubbing also contained background noise, the signals of both friction as well as the background noise were recorded. The maximum amplitude of force signals spectrum has been recorded, which gives maximum force produced during rubbing of the contactor against the surface. To remove the effect of background noise from the microphone signals, the spectrum of background noise was subtracted from the spectrum of the signals generated during rubbing. From the subtracted spectrum of frictional noise, the maximum amplitude of noise and the corresponding frequency were recorded. Apart from the peak amplitude of the microphone signal, the standard deviation of all amplitudes is taken as another parameter. Therefore, for each case of rubbing, the maximum amplitude, corresponding frequency, standard deviation of amplitude of microphone signals and the maximum force recorded by the force transducer were stored to characterize the noise signal and the contact force. For each such case, the corresponding surface roughness was measured by the pocket surf.
S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
3. Artificial neural network for roughness prediction Artificial neural network architecture comprises mainly parallel adaptive processing elements with hierarchical structured interconnected networks. Each processing unit of an ANN has multiple input slots and a single output slot (Fig. 2). The relationship between the input and the output signals are usually formulated as follows: Oi = f(yi ) = yi =
l
1 1 + exp(yi )
wij Ii − θi
(1)
(2)
i=1
where Oj is the output signal of the jth unit, yj the potential of the jth unit, f( ) the activation function that is a sigmoidal function in this case, wij the connection weights between ith and jth units, θ j the threshold value of the jth unit and l the number of input signals. Fig. 3 shows a three-layer neural network. All the units are formed into a multiple layers, i.e. an input layer, a hidden layer and an output layer. The basic idea of training a neural network is as follows. First, the square error of the pth training pattern Ep is defined as Ep =
1 2
m
(Tpk − Opk )
2
(3)
k=1
where Tpk is the teacher signal (desired output) to the kth output unit for pth training pattern, Opk the output signal to the kth output unit for pth training pattern and m the number of output units. In the training process, wji and θ j are modified repeatedly based on gradient descent method to minimize the above error. This modification proceeds downward. Through such an iterative process, the network attains the
129
ability to promptly output the similar signal to the teacher’s one. This training algorithm is called back propagation neural network (BPNN). Since the friction noise and contact force contain information about the surface roughness, these two parameters have been used for training the neural network for subsequent prediction. This information may be extremely difficult to extract by traditional methods as the rules governing the cause and effect relationship must be established explicitly and a methodology for using these rules must be developed a priori. So, in the proposed approach, the training process is used to extract the cause and effect relationships, which are then stored in the connection strengths of an appropriate BPNN. Thus the learning capability of neural network eliminates the need for explicitly extracting the cause–effect relationships. These relationships exist in a distributed way within the connection strengths of the neural network but are not obvious or externally available to the user. In a typical application of BPNN, the following steps are involved: (i) training the network; (ii) deciding the appropriate architecture of the network; (iii) testing the network to verify how well it has learnt the training cases. These steps are discussed in detail with respect to the present work in the subsequent paragraphs. As stated earlier, BPNN architecture was used in this investigation. After trying many combinations of parameters, it was decided to use the following input parameters for training the neural network: • • • •
maximum amplitude of the friction noise; frequency corresponding to maximum amplitude; standard deviation of acoustic signals; maximum contact force recorded by force transducer.
Fig. 3. Layout of the three-layer back propagation neural network.
130
S.K. Singh et al. / Journal of Materials Processing Technology 152 (2004) 127–130
Table 1 Training and testing error for different combinations of number of nodes in hidden layer, η and α Number of nodes on hidden layer
η
α
Number of iterations
Mean-square training error
Mean-square testing error
4 4 4 4 4 3 5 6 7
0.3 0.4 0.5 0.5 0.7 0.3 0.3 0.3 0.3
0.4 0.5 0.5 0.7 0.9 0.4 0.4 0.4 0.4
3267 3267 3267 3267 3267 3267 3267 25618 11991
0.019586 0.019384 0.019219 0.019455 0.023850 0.019505 0.018805 0.018793 0.019228
0.020781 0.020933 0.020993 0.020962 0.021382 0.020846 0.021006 0.021026 0.020619
The surface roughness measured by the pocket surf was used as the desired output of the BPNN. The network thus consists of four input nodes and one output node. The contactor has been rubbed on machined mild steel surfaces and around 300 data sets were generated. Each data set contained all the aforementioned input and output parameters. These data sets are then normalized between 0.1 and 0.9 using the following equation: x − xmin y = 0.1 + 0.8 (4) xmax − xmin where xmax and xmin are maximum and minimum values of a particular parameter in the entire data set, respectively. These normalized data sets are then used to train the BPNN. For better training of the network, the data sets were shuffled (randomly re-arranged).
Fig. 4. Training and testing error vs. number of iterations.
4. Results and discussion
5. Conclusions
From the data set 70% were selected at random and are used to train the network and the remaining 30% were used to test the performance of the network. It was found by trial and error that one hidden layer was optimum for the present neural network architecture. The number of nodes in the hidden layer was decided by trial and error based on comparative performance of large number of network architectures (few of them have been shown in Table 1). Based on the trial runs, the network architecture decided in the present case was 4-4-1. Various combinations of the learning rate (η), the momentum parameter (α) and the number of hidden nodes have been tried to optimize the learning, and based on their relative performances (Table 1), the values η = 0.3 and α = 0.3 have been found to be most suitable. For this network, variation of training and testing error with number of iterations is shown in Fig. 4. It could be observed that the network could learn the pattern reasonably well. Thus, BPNN could be used for prediction of surface roughness of machined components by monitoring the contact force between the contactor and the surface and the friction noise generated during rubbing.
A methodology has been proposed for predicting surface roughness using noise signal and contact force generated during sliding of two surfaces in conjunction with artificial neural network. The data obtained from the experiments demonstrate that the neural network could learn the pattern for future prediction of surface roughness. It is expected that in practical applications where data volumes could be large will lead to a better training and hence a better performance of the system developed in the present work.
References [1] H.L. Dunegan, Use of plate wave analysis in acoustic emission testing to detect and measure crack growth in noisy environments, in: Proceedings of the International Conference on Structural Materials Technology and NDT, San Diego, California, 20–23 February 1996. [2] M. Huang, L. Jiang, P.K. Liaw, C.R. Brooks, R. Seeley, D.L. Kiarstrom, Using acoustic emission in fatigue and fracture materials research, JOM 50 (11) (1998) (Web edition). [3] H.L. Dunegan, An Acoustic Emission Technique for Measuring Surface Roughness, The DECI Report, Publication of Dunegan Engineering Consultants Inc., December 1998.