- Email: [email protected]
Machined Surface Monitoring in Turning Using Histogram Analysis by Machine Vision
Available online at www.sciencedirect.com
ScienceDirect Materials Today: Proceedings 5 (2018) 7775–7781
www.materialstoday.com/proceedings
IMME17
Machined Surface Monitoring in Turning Using Histogram Analysis by Machine Vision Y D Chethana*, H V Ravindrab, Y T Krishne gowdaa a
Dept. of Mechanical Engineering, Maharaja Institute of Technology, Mysore, Belawadi, Srirangapatna Tq, Mandya - 571438 Karnataka India b Dept. of Mechanical Engineering, P.E.S. College of Engineering Mandya-571401, Karnataka, India
Abstract Non-contact assessment of surface texture is more crucial than traditional surface assessment using stylus method of which machine vision has got the capability to assess the roughness of machined surface by considering surface area rather than a single line. The study has been carried out to monitor the machined surface in turning Nimonic75 material using coated carbide tool considering different spindle speed and feed rates. The spindle speeds considered are 450 RPM and 710 RPM and feeds are 0.05, 0.06 and 0.07 mm/rev, at constant depth of cut of 0.2mm on high speed, automatic precision lathe. Analysis of the captured data has been processed using histogram analysis, a tool which provides the best frequency of pixels in a dominant gray level of an image. Turned surface images are acquired using machine vision camera to monitor the surface texture. The images were preprocessed to enhance the quality of the image. Nevertheless, image processing is done on real time surface images and some of the limitations observed are non-uniform illumination and image noise. By applying image segmentation and image enhancement techniques, noise and non uniform illumination problems have been reduced. The histogram distributions of an illuminated region of interest (ROI) from turned surface images were analysed to assess changes in the frequency of the histogram, which serve to quantify the surface roughness.The results reveals that, the cutting conditions of turning process would not effect on surface roughness and the obtained results confirmed that histogram analysis is a reliable tool to decide whether the surface analysed is coarser or smooth. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Emerging Trends in Materials and Manufacturing Engineering (IMME17).
Keywords: Machined Surface; Machine Vision; Histogram Analysis; Surface Roughnes;
* Corresponding author. Tel.: 91 9620228113 E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Emerging Trends in Materials and Manufacturing Engineering (IMME17).
7776
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
1. Introduction Assessment of machining quality is made by monitoring the important parameters like surface roughness and machined surface profile. To establish semi automated machining, roughness needs to be monitored without removing job from the machine, so that the deviation of control limits and manufacturing lead time could be reduced. Generally, the surface roughness parameter is considered to evaluate the machined surface texture. Surface roughness is significant parameter, as it might influence frictional resistance, creep life or fatigue strength of machined jobs. As far as turned jobs are concerned, low surface roughness is significant as it can diminish or even completely eliminate the need of auxiliary machining [1] In finish turning, where the tool replacement is defined by either the surface roughness or the tool wear and machining conditions are not destructive enough to fracture the tool, it is very important to monitor the surface roughness to establish the moment to change the tool. Since the increase of surface roughness is caused by the increase of tool wear state. Five different categories such as scene constraints, image acquisition, image pre-processing, processing and systematic considerations have been considered as key points in design and applications of machine vision system by H. Golnabi and A. Asadpour [2]. The condition for optimal design of machine vision system and features of each of the processes has been presented. The existing machine vision system, which has measurement flexibility, high spatial resolution and good accuracy could accomplish, as presented by J. Jurkovic, et al [3], image processing and direct tool wear measurement. The method, as presented, is specially featured by its determination of profile deepness with the help of laser raster lines on a tool surface and hence more advantageous compared with other methods, which can measure only 2D profiles. In their review of machine vision sensors for tool condition monitoring, S. Kurada and C. Bradley [4], have identified the importance gained by Machine vision system in tool condition monitoring over the preceding two decades. Also, recent advances in the field of image processing technology have led to the development of various in-cycle vision sensors. Hence it is possible to provide a direct and indirect estimate of the tool condition, using image processing by machine vision. Besides tool status monitoring, the machine vision system also enables machined surface monitoring without the aid of a contact method. Many researchers have studied the monitoring of surface texture using machine vision. For example, F Luk et al., [5] have developed a method of surface roughness assessment using micro computer based vision system for use in a production environment. This method employs a vision system to analyze pattern of spread light from surface to derive a roughness parameter. A number of toolsteel samples were ground to different roughness to obtain roughness parameters. They have established a correlation curve by plotting roughness parameters against corresponding average roughness (Ra) readings obtained from a stylus instrument. Roughness measurement was also done for specimens immersed in oil, a condition similar to that of a production environment. The proposed method provides a fast and accurate means for measuring surface roughness. S. Palani et. al.,[6] have integrated machine vision system with neural network for, noncontact, and flexible prediction of surface roughness of end milled parts. Machined surface images are acquired by machine vision system and features extracted using developed image processing algorithm. The surface texture features such as major peak frequency (f1), magnitude squared value (f2), and average gray level (ga). The machining parameters speed, feedrate, depth of cut, and response extracted image variables f1, f2, and Ga could be used as input data and response surface roughness Ra measured by traditional stylus method could be used as output data of an ANN. The back-propagation algorithm is used for training network. The result shows that surface roughness of milled parts predicted by machine vision system could be availed with a sensible accuracy compared with those measured by traditional stylus method.The main objective of this research is to develop machined surface monitoring system using image processing by machine vision system. 2. Experimental Setup Experiments were carried out on automatic precision lathe. The material used was a nickel base super alloy, Nimonic75. Today, fuel efficiency and reliability drive modern aircraft engine design. Engineers have long relied upon super alloys, such as INCONEL718 and NIMONIC75, for their unique high temperature and stress-resistance properties. These bars (30 mm in diameter and 300 mm in length) were turned under dry condition using carbide inserts.The experiments were conducted for different cutting speed and feed combinations. The spindle speeds considered are 450 RPM and 710 RPM. Feeds considered are 0.05, 0.06 and 0.07 mm/rev at constant depth of cut of 0.2mm. Each turning was carried out over the entire tool life of cutting tool insert. VBmax of cutting insert was measured with the help of toolmakers microscope. As per standards, (3865:1977 or BS 5623:1977), worn tool is
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7777
considered with VBmax greater than 0.6mm, in turning. Machined surface images were captured by machine vision. Vision feature was extracted by using image processing software, View Flux. Experimental set-up is as shown in the Fig.1
Fig 1: The experimental set up for the acquisition of machined surface image
3. Image Histogram The histogram of an image tells a lot about the distribution of grey levels within the image. Histogram provides a natural bridge between images and a probabilistic description. The histogram is often displayed as a bar graph. Thus, Histogram is more significantly defined as the percentage of pixels within the image at a given gray level. Histogram of an image represents the relative frequency of occurrence of the various gray levels in the image. Histogram is the graphical representation of grey level intensity variations as per the surface roughness of the machined surface. Histogram left side represents smaller intensities and right side represents large value of intensities. 4. Machined surface Status Monitoring During experimentation, turning of nickel base super alloy using coated carbide insert, status of work piece was visualized and monitored using machine vision approach. In terms of images captured during finish turning under various cutting conditions resulted in changes of texture of work piece caused due to increased tool wear. The processed images of the machined surface are analyzed via the images being captured by a digital camera fitted with focal length lens and extension tubes capable of appropriate magnification. Images, lit up using fluorescent light were acquired and at 8-bit grey level intensity resolution (256 level brightness), were processed. It is observed from the machine vision system images that the machined surface consists of irregularities such as micro-particle deposits and feed marks, are seen on the surfaces. as shown in Fig.2 The images, analyzed for grey level intensity distribution, etc, through view flux software indicated of the histogram distributions of illuminated region of interest (ROI) from turned surfaces. ROIs were selected from unturned and final work piece surface images to establish any noticeable differences between the two. While processing machined surface images constant threshold value has been considered. Images with segmentation and threshold is as shown in Figure 3. This data were used to explore the properties of typical image intensity distributions.
7778
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
Fig.2. Machined surface with tool feed marks as seen under machine vision system for Speed=710rpm, Feed=0.06 mm/Rev, Depth of Cut=0.2mm cutting conditions.
Machined surface image having Ra=0.5µm
Machined surface image having Ra=1.062µm
Fig.3:Gray scale of Images Of Surface with the Speed=710rpm, Feed=0.05 mm/Rev,Depth Of Cut=0.2mm
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7779
5. Results and Discussion 5.1 Characterization of Machined Surface Using Image Histogram
Fig 4(a): Histogram of machined surface having Ra = 0.5µm
Fig 4 (b): Histogram of machined surface having Ra = 1.062µm
Machine vision system has got image-processing software that generates intensity histogram, from the captured image. Histogram is the graphical representation of grey level intensity variations as per the surface roughness of the machined surface. Histogram left side represents smaller intensities and right side represents large value of intensities. Since the metal surface possesses high reflectivity, right side of histogram monitoring is preferable. Hence gray level 125 has been considered as reference point to quantify the histogram frequencies. As the machined surface is smooth, reflectivity of the surface increases that result in higher values of frequencies Fig 4 and 5 shows the histogram grey level intensities of machined surface having different Ra values. In fig 4, smooth machined surface having surface roughness Ra = 0.5 µm, gray level 125 approximately having frequency 600. On the other hand in the case of coarse machined surface having surface roughness Ra = 1.075 µm, it is observed from Fig 5 that 125 value of gray level is having 400 frequency. From the histogram analysis of machined surfaces it is possible to judge that whether the given machined surface is comparatively rough or smooth 5.2 Effect of Feed on Machined Surface Histogram Frequency
Fig. 5: Variation of machined surface histogram frequency with machining time for various feed rate at speed of 450 RPM and Depth of Cut 0.2 mm
7780
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
Fig. 6: Variation of machined surface histogram frequency with machining time for various feed rate at speed of 710 RPM and 0.2 mm Depth of Cut
5.3 Effect of Speed on Machined Surface Histogram Frequency
Fig. 7: Variation of machined surface histogram frequency with machining time for various spindle speed at feed rate of 0.05mm/rev and 0.2 mm Depth of Cut
Fig 8: Variation of machined surface histogram frequency with machining time for various speed at feed rate of 0.07mm/rev and Depth of Cut 0.2 mm
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7781
The study has been carried out to monitor the machined surface in turning Nimonic75 material using coated carbide inserts considering different spindle speed and feed combinations. The histogram distributions of an illuminated region of interest (ROI) from turned surface images were analyzed to assess changes in the statistical descriptors of the histogram, which serve to quantify the surface finish. The results reveal that, the cutting conditions of turning process would not effect on surface roughness. Fig 5 and 6 shows the plot of histogram frequency with machining time for various feed and a constant depth of cut 0.2mm and at spindle speed of 450 rpm and 710rpm.To attain an overview associated to the histogram frequency of the machined surface in relation to cutting time, the graphs as shown in Fig 7 to 8 have been plotted. The plots relating only the lowest feed and the highest feed for two different speeds. Based on the graphs, it was found that histogram frequency of the machined surface does not play significant role in controlling the tool status. The machined surface roughness is almost constant for all cutting conditions. Several researchers have studied, found that value of Ra oscillate in constant range [8], similarly machined surface histogram frequency obtained by the machine vision system also oscillates in constant range. The results show that the histogram frequency of the machined surface would not significantly change, in variation of cutting speed and feed. For different feed rate, the changing shape of the feed marks on the machined surface is evident in consistent change in the histogram profile [10] Moreover, the ambient lighting variation and shadowing effects would also give complications in a histogrambased method [7]. 6. Conclusions In the present work, machined surface monitoring is using image processing by machine vision is discussed. For turned surface monitoring with non-contact techniques, can be used for enhancing the automation proficiency. The outcome reveal that, the cutting conditions of turning process would not effect on surface roughness and the obtained results confirmed that histogram analysis is a reliable tool to decide whether the surface analyzed is coarser or smooth. By histogram intensity profile, deteriorated work piece can be determined. Spindle speed increases in the range of 450 to 710RPM and also feed rate changes from 0.05 to 0.07 mm/rev showed no clear effect towards variations in the histogram frequency value obtained. On all cutting conditions, the histogram frequency value oscillates in a narrow value of range. This study also finds that machined surface does not necessarily deteriorate from time to time as the increasing of flank wear. References [1] C. J Rao,D.Nageswara Rao,P. Srihari Procedia Engineering 64 ( 2013 ) 1405 – 1415 [2] H.Golnabi and A.Asadpour“ Robotics and Computer-Integrated Manufacturing 23 (2007) 630–637 [3] J.Jurkovic International Journal of Machine Tools & Manufacture 45 (2005) 1023–1030 [4] S. Kurada and CBradley , Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada, V8W 3P6, accepted 27 August 1996 [5] F Luk, V Huynh and W North, Journal of Physics E: Scientific Instruments, Volume 22, Number 12 [6] S. Palani& U. Natarajan, Int J AdvManufTechnol (2011) 54:1033–1042 [7] Y. D. Chethan, H. V. Ravindra, Prashanth N, Y. T. K. Gowda and T. Gowda, International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, 2015, pp. 48-53. [8] Nik Faizu Kundor, Norazmira Wati Awang and Nawi Berahim, Indian Journal of Science and Technology, Vol 9 May 2016 [9] Y.D. Chethan, H.V. Ravindra, Y.T.Krishne gowda, S. Bharath Kumar ”Materials Today: Proceedings, Volume 2, Issues 4–5, 2015, Pages 1841-1848 [10] C. Bradley and Y. S. Wong , International Journal of Advanced Manufactuing Technology” vol 17,pp 435–443,2001.
ScienceDirect Materials Today: Proceedings 5 (2018) 7775–7781
www.materialstoday.com/proceedings
IMME17
Machined Surface Monitoring in Turning Using Histogram Analysis by Machine Vision Y D Chethana*, H V Ravindrab, Y T Krishne gowdaa a
Dept. of Mechanical Engineering, Maharaja Institute of Technology, Mysore, Belawadi, Srirangapatna Tq, Mandya - 571438 Karnataka India b Dept. of Mechanical Engineering, P.E.S. College of Engineering Mandya-571401, Karnataka, India
Abstract Non-contact assessment of surface texture is more crucial than traditional surface assessment using stylus method of which machine vision has got the capability to assess the roughness of machined surface by considering surface area rather than a single line. The study has been carried out to monitor the machined surface in turning Nimonic75 material using coated carbide tool considering different spindle speed and feed rates. The spindle speeds considered are 450 RPM and 710 RPM and feeds are 0.05, 0.06 and 0.07 mm/rev, at constant depth of cut of 0.2mm on high speed, automatic precision lathe. Analysis of the captured data has been processed using histogram analysis, a tool which provides the best frequency of pixels in a dominant gray level of an image. Turned surface images are acquired using machine vision camera to monitor the surface texture. The images were preprocessed to enhance the quality of the image. Nevertheless, image processing is done on real time surface images and some of the limitations observed are non-uniform illumination and image noise. By applying image segmentation and image enhancement techniques, noise and non uniform illumination problems have been reduced. The histogram distributions of an illuminated region of interest (ROI) from turned surface images were analysed to assess changes in the frequency of the histogram, which serve to quantify the surface roughness.The results reveals that, the cutting conditions of turning process would not effect on surface roughness and the obtained results confirmed that histogram analysis is a reliable tool to decide whether the surface analysed is coarser or smooth. © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Emerging Trends in Materials and Manufacturing Engineering (IMME17).
Keywords: Machined Surface; Machine Vision; Histogram Analysis; Surface Roughnes;
* Corresponding author. Tel.: 91 9620228113 E-mail address: [email protected] 2214-7853 © 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Emerging Trends in Materials and Manufacturing Engineering (IMME17).
7776
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
1. Introduction Assessment of machining quality is made by monitoring the important parameters like surface roughness and machined surface profile. To establish semi automated machining, roughness needs to be monitored without removing job from the machine, so that the deviation of control limits and manufacturing lead time could be reduced. Generally, the surface roughness parameter is considered to evaluate the machined surface texture. Surface roughness is significant parameter, as it might influence frictional resistance, creep life or fatigue strength of machined jobs. As far as turned jobs are concerned, low surface roughness is significant as it can diminish or even completely eliminate the need of auxiliary machining [1] In finish turning, where the tool replacement is defined by either the surface roughness or the tool wear and machining conditions are not destructive enough to fracture the tool, it is very important to monitor the surface roughness to establish the moment to change the tool. Since the increase of surface roughness is caused by the increase of tool wear state. Five different categories such as scene constraints, image acquisition, image pre-processing, processing and systematic considerations have been considered as key points in design and applications of machine vision system by H. Golnabi and A. Asadpour [2]. The condition for optimal design of machine vision system and features of each of the processes has been presented. The existing machine vision system, which has measurement flexibility, high spatial resolution and good accuracy could accomplish, as presented by J. Jurkovic, et al [3], image processing and direct tool wear measurement. The method, as presented, is specially featured by its determination of profile deepness with the help of laser raster lines on a tool surface and hence more advantageous compared with other methods, which can measure only 2D profiles. In their review of machine vision sensors for tool condition monitoring, S. Kurada and C. Bradley [4], have identified the importance gained by Machine vision system in tool condition monitoring over the preceding two decades. Also, recent advances in the field of image processing technology have led to the development of various in-cycle vision sensors. Hence it is possible to provide a direct and indirect estimate of the tool condition, using image processing by machine vision. Besides tool status monitoring, the machine vision system also enables machined surface monitoring without the aid of a contact method. Many researchers have studied the monitoring of surface texture using machine vision. For example, F Luk et al., [5] have developed a method of surface roughness assessment using micro computer based vision system for use in a production environment. This method employs a vision system to analyze pattern of spread light from surface to derive a roughness parameter. A number of toolsteel samples were ground to different roughness to obtain roughness parameters. They have established a correlation curve by plotting roughness parameters against corresponding average roughness (Ra) readings obtained from a stylus instrument. Roughness measurement was also done for specimens immersed in oil, a condition similar to that of a production environment. The proposed method provides a fast and accurate means for measuring surface roughness. S. Palani et. al.,[6] have integrated machine vision system with neural network for, noncontact, and flexible prediction of surface roughness of end milled parts. Machined surface images are acquired by machine vision system and features extracted using developed image processing algorithm. The surface texture features such as major peak frequency (f1), magnitude squared value (f2), and average gray level (ga). The machining parameters speed, feedrate, depth of cut, and response extracted image variables f1, f2, and Ga could be used as input data and response surface roughness Ra measured by traditional stylus method could be used as output data of an ANN. The back-propagation algorithm is used for training network. The result shows that surface roughness of milled parts predicted by machine vision system could be availed with a sensible accuracy compared with those measured by traditional stylus method.The main objective of this research is to develop machined surface monitoring system using image processing by machine vision system. 2. Experimental Setup Experiments were carried out on automatic precision lathe. The material used was a nickel base super alloy, Nimonic75. Today, fuel efficiency and reliability drive modern aircraft engine design. Engineers have long relied upon super alloys, such as INCONEL718 and NIMONIC75, for their unique high temperature and stress-resistance properties. These bars (30 mm in diameter and 300 mm in length) were turned under dry condition using carbide inserts.The experiments were conducted for different cutting speed and feed combinations. The spindle speeds considered are 450 RPM and 710 RPM. Feeds considered are 0.05, 0.06 and 0.07 mm/rev at constant depth of cut of 0.2mm. Each turning was carried out over the entire tool life of cutting tool insert. VBmax of cutting insert was measured with the help of toolmakers microscope. As per standards, (3865:1977 or BS 5623:1977), worn tool is
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7777
considered with VBmax greater than 0.6mm, in turning. Machined surface images were captured by machine vision. Vision feature was extracted by using image processing software, View Flux. Experimental set-up is as shown in the Fig.1
Fig 1: The experimental set up for the acquisition of machined surface image
3. Image Histogram The histogram of an image tells a lot about the distribution of grey levels within the image. Histogram provides a natural bridge between images and a probabilistic description. The histogram is often displayed as a bar graph. Thus, Histogram is more significantly defined as the percentage of pixels within the image at a given gray level. Histogram of an image represents the relative frequency of occurrence of the various gray levels in the image. Histogram is the graphical representation of grey level intensity variations as per the surface roughness of the machined surface. Histogram left side represents smaller intensities and right side represents large value of intensities. 4. Machined surface Status Monitoring During experimentation, turning of nickel base super alloy using coated carbide insert, status of work piece was visualized and monitored using machine vision approach. In terms of images captured during finish turning under various cutting conditions resulted in changes of texture of work piece caused due to increased tool wear. The processed images of the machined surface are analyzed via the images being captured by a digital camera fitted with focal length lens and extension tubes capable of appropriate magnification. Images, lit up using fluorescent light were acquired and at 8-bit grey level intensity resolution (256 level brightness), were processed. It is observed from the machine vision system images that the machined surface consists of irregularities such as micro-particle deposits and feed marks, are seen on the surfaces. as shown in Fig.2 The images, analyzed for grey level intensity distribution, etc, through view flux software indicated of the histogram distributions of illuminated region of interest (ROI) from turned surfaces. ROIs were selected from unturned and final work piece surface images to establish any noticeable differences between the two. While processing machined surface images constant threshold value has been considered. Images with segmentation and threshold is as shown in Figure 3. This data were used to explore the properties of typical image intensity distributions.
7778
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
Fig.2. Machined surface with tool feed marks as seen under machine vision system for Speed=710rpm, Feed=0.06 mm/Rev, Depth of Cut=0.2mm cutting conditions.
Machined surface image having Ra=0.5µm
Machined surface image having Ra=1.062µm
Fig.3:Gray scale of Images Of Surface with the Speed=710rpm, Feed=0.05 mm/Rev,Depth Of Cut=0.2mm
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7779
5. Results and Discussion 5.1 Characterization of Machined Surface Using Image Histogram
Fig 4(a): Histogram of machined surface having Ra = 0.5µm
Fig 4 (b): Histogram of machined surface having Ra = 1.062µm
Machine vision system has got image-processing software that generates intensity histogram, from the captured image. Histogram is the graphical representation of grey level intensity variations as per the surface roughness of the machined surface. Histogram left side represents smaller intensities and right side represents large value of intensities. Since the metal surface possesses high reflectivity, right side of histogram monitoring is preferable. Hence gray level 125 has been considered as reference point to quantify the histogram frequencies. As the machined surface is smooth, reflectivity of the surface increases that result in higher values of frequencies Fig 4 and 5 shows the histogram grey level intensities of machined surface having different Ra values. In fig 4, smooth machined surface having surface roughness Ra = 0.5 µm, gray level 125 approximately having frequency 600. On the other hand in the case of coarse machined surface having surface roughness Ra = 1.075 µm, it is observed from Fig 5 that 125 value of gray level is having 400 frequency. From the histogram analysis of machined surfaces it is possible to judge that whether the given machined surface is comparatively rough or smooth 5.2 Effect of Feed on Machined Surface Histogram Frequency
Fig. 5: Variation of machined surface histogram frequency with machining time for various feed rate at speed of 450 RPM and Depth of Cut 0.2 mm
7780
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
Fig. 6: Variation of machined surface histogram frequency with machining time for various feed rate at speed of 710 RPM and 0.2 mm Depth of Cut
5.3 Effect of Speed on Machined Surface Histogram Frequency
Fig. 7: Variation of machined surface histogram frequency with machining time for various spindle speed at feed rate of 0.05mm/rev and 0.2 mm Depth of Cut
Fig 8: Variation of machined surface histogram frequency with machining time for various speed at feed rate of 0.07mm/rev and Depth of Cut 0.2 mm
Chethan et al., / Materials Today: Proceedings 5 (2018) 7775–7781
7781
The study has been carried out to monitor the machined surface in turning Nimonic75 material using coated carbide inserts considering different spindle speed and feed combinations. The histogram distributions of an illuminated region of interest (ROI) from turned surface images were analyzed to assess changes in the statistical descriptors of the histogram, which serve to quantify the surface finish. The results reveal that, the cutting conditions of turning process would not effect on surface roughness. Fig 5 and 6 shows the plot of histogram frequency with machining time for various feed and a constant depth of cut 0.2mm and at spindle speed of 450 rpm and 710rpm.To attain an overview associated to the histogram frequency of the machined surface in relation to cutting time, the graphs as shown in Fig 7 to 8 have been plotted. The plots relating only the lowest feed and the highest feed for two different speeds. Based on the graphs, it was found that histogram frequency of the machined surface does not play significant role in controlling the tool status. The machined surface roughness is almost constant for all cutting conditions. Several researchers have studied, found that value of Ra oscillate in constant range [8], similarly machined surface histogram frequency obtained by the machine vision system also oscillates in constant range. The results show that the histogram frequency of the machined surface would not significantly change, in variation of cutting speed and feed. For different feed rate, the changing shape of the feed marks on the machined surface is evident in consistent change in the histogram profile [10] Moreover, the ambient lighting variation and shadowing effects would also give complications in a histogrambased method [7]. 6. Conclusions In the present work, machined surface monitoring is using image processing by machine vision is discussed. For turned surface monitoring with non-contact techniques, can be used for enhancing the automation proficiency. The outcome reveal that, the cutting conditions of turning process would not effect on surface roughness and the obtained results confirmed that histogram analysis is a reliable tool to decide whether the surface analyzed is coarser or smooth. By histogram intensity profile, deteriorated work piece can be determined. Spindle speed increases in the range of 450 to 710RPM and also feed rate changes from 0.05 to 0.07 mm/rev showed no clear effect towards variations in the histogram frequency value obtained. On all cutting conditions, the histogram frequency value oscillates in a narrow value of range. This study also finds that machined surface does not necessarily deteriorate from time to time as the increasing of flank wear. References [1] C. J Rao,D.Nageswara Rao,P. Srihari Procedia Engineering 64 ( 2013 ) 1405 – 1415 [2] H.Golnabi and A.Asadpour“ Robotics and Computer-Integrated Manufacturing 23 (2007) 630–637 [3] J.Jurkovic International Journal of Machine Tools & Manufacture 45 (2005) 1023–1030 [4] S. Kurada and CBradley , Department of Mechanical Engineering, University of Victoria, Victoria, BC, Canada, V8W 3P6, accepted 27 August 1996 [5] F Luk, V Huynh and W North, Journal of Physics E: Scientific Instruments, Volume 22, Number 12 [6] S. Palani& U. Natarajan, Int J AdvManufTechnol (2011) 54:1033–1042 [7] Y. D. Chethan, H. V. Ravindra, Prashanth N, Y. T. K. Gowda and T. Gowda, International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT), Mandya, 2015, pp. 48-53. [8] Nik Faizu Kundor, Norazmira Wati Awang and Nawi Berahim, Indian Journal of Science and Technology, Vol 9 May 2016 [9] Y.D. Chethan, H.V. Ravindra, Y.T.Krishne gowda, S. Bharath Kumar ”Materials Today: Proceedings, Volume 2, Issues 4–5, 2015, Pages 1841-1848 [10] C. Bradley and Y. S. Wong , International Journal of Advanced Manufactuing Technology” vol 17,pp 435–443,2001.