Machine Vision for Correlating Tool Status and Machined Surface in Turning Nickel-base super alloy Y D Chethan, H V Ravindra, Prashanth N
Y T Krishne Gowda,Thejesh gowda
Research scholar Dept. of Mechanical Engineering, P.E.S. College of Engineering Mandya-571401, Karnataka, India
[email protected],
[email protected]
Dept. of Mechanical Engineering, Maharaja Institute Of Technology, Mysore, Belawadi, Srirangapatna Tq, Mandya - 571438 Karanataka India
[email protected]
Abstract— Substantial amount of research has been performed on automated tool status monitoring systems. The research has tended to focus on the development of Tool and work Surface Texture Monitoring. However, there has been relatively less effort to monitor surface texture. This paper presents machine vision system, capable of providing surface texture information in Turning Inconel 718 material. Machine vision based Surface texture sensors, that provide information on the wear state of the tool employed in turning the surface. Images of the turned surface specimens were acquired using the machine vision system. The images were pre-processed to eliminate noise present in the image. An image histogram quantifies the distribution of all image pixels against the grey level and is a measure of the reflectance properties of the surface under monitoring. The histogram shape changes as the wear state of tool increases. From the analysis of the intensity distribution in the region of interest of the tool and surface, a good correlation between the tool image and corresponding surface image was found. It is expected that these results would support further to establish a criteria for tool replacement in turning operation. Keywords— Machine vision; Texture analysis; Nickel-base super alloy; surface roughness;
I. INTRODUCTION Every surface has some form of texture that takes the form of peaks and valleys. These peaks and valleys vary in height and spacing and have properties inherent in the way the surface was produced or utilized. Blunt tool affects the surface roughness dramatically. There is a very close correspondence between the geometrical features imposed on the tool by wear and micro-fracture and the geometry imparted by the tool on to the workpiece surface. Since a machined surface is the negative copy of the shape of the cutting tool, and reflects the volumetric changes in cutting-edge shape, it is more suitable to monitor the machined surface than look at a certain portion of the cutting tool. 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. Besides tool status monitoring, the machine vision system also enables machined surface monitoring without the aid of a contact method. Many authors have studied the monitoring of surface texture using machine vision. For example,Ghassan et al. [1] proposed a methodology for using machine vision data to acquire reliable surface roughness parameter measurement. Their results showed that the ITC (Intensity-Topography Compatible) model gives more superior results compared to the Light-Diffuse model with remarkable close values to those acquired by the traditional stylus-based data of all roughness parameters. In the case of the Skewness parameter (Rsk), however, it was shown to provide highly different values from those acquired by stylus technique. Although it has been revealed that the surface roughness is characterized by the Machined surface image, practical surface roughness measurements based on machine vision are quiet difficult. The machine vision technique for inspecting surface roughness adopted by Lee and Tarng[2] is briefly described as follows. The surface image of the work piece is first acquired using a digital camera and then the feature of the surface image is extracted. A polynomial network using self-organizing adaptive modeling method is applied to constructing the relationships between the feature of the surface image and the actual surface roughness under a variation of turning operations. As a result, the surface roughness of the turned part can be predicted with reasonable accuracy if the image of the turned surface. Conventional roughness measurement procedures depend on stylus method which has limited flexibility. Moreover, the procedure is a post-process approach, which is not amenable for automation. In some literatures, the modeling and prediction of surface roughness of a machined surface by machine vision in turning operations have received a great deal of attention. Rajneesh Kumar, P.[3] has utilized, a machine vision system to capture the images and
then the quantification of the surface roughness of machined surfaces (ground, milled and shaped) is done by the application of regression analysis. Subsequently, original images have been magnified using Cubic Convolution interpolation technique and improved (edge enhancement) through Linear Edge Crispening algorithm.They have estimated surface image features, a parameter called Ga using regression analysis, for the original and magnified quality improved images. Finally, a comparison has been carried to establish correlation between magnification index, Ga and surface roughness.tool wear state is estimated by extracting parameters from images of the machined surface.by considering three image-processing algorithms, in estimating the tool condition: analysis of the intensity histogram; image frequency domain content; and spatial domain surface texture.[5] Analysing the texture of machined surfaces has been shown to be promising for tool status monitoring. However, the geometric features of machined surface depend on the machining operation, and where image quality is affected by illumination and other factors. Problems of non-uniform illumination and image noise can be reduced by applying image segmentation and image enhancement techniques. Also, they have discussed on statistical and structural approaches for analysing machined surfaces and investigate the correlation between tool status and quantities characterizing machined surfaces. The results indicated that tool status monitoring, which is defined as the ability to distinguish between a sharp, a semi-dull, or a dull tool can be successfully accomplished by analysis of statistical and structural information extracted from the machined surface.[12] This paper discusses research work that analyzes images of work piece surfaces that have been subjected to turning operations and investigates the correlation between tool status and quantities characterizing machined surfaces. The distribution image histogram pattern from a work piece image is used to characterize the topography of the surface finish and to set criteria for cutting tool replacement. Results clearly indicate that tool status monitoring (the distinction between a sharp, semi-dull, or a dull tool) can be successfully accomplished by analyzing surface image data. II. EXPERIMENTAL CONDITIONS
TABLE I
THE CHEMICAL COMPOSITION OF WORK MATERIAL IN PERCENTAGE Work material Nickel Chromium Iron Niobium Molybdenum Aluminium Carbon
Inconel 718 53% 19% 18.5% 5.1% 3% 0.5% 0.08%
A. Surface roughness The surface roughness parameter used in this study is the average surface roughness (Ra) as it is the most commonly used surface finish parameter by researchers and in industry as well. It is the arithmetic average of the absolute value of the heights of roughness irregularities from the mean value measured, that is (1) Where yj is the height of roughness irregularities from the mean value and n is the number of sampling data. Surfcom FLEX 50-A (Fig.1) is a compact, hand-held surface tester. There is no easier way of measuring, evaluating and documenting surface roughness. Surfcom flex 50-A measures not only flat, horizontal, but also vertical, overhead surfaces and simple measurement to waviness. In addition, 30 complete data records can be stored in the built-in memory and recalled at any time. Additionally USB memory can be connected in Surfcom FLEX to save more data and Mini USB connector is equipped with Surfcom FLEX and able to connect with PC. The data can be sent to PC. It has the capability to measure roughness average (Ra), average maximum height of the profile (Rz) and maximum roughness depth (Rmax),etc.,It is easy to carry by compact design, it can be use anywhere, it has built in printer so we can take the print out directly just by inserting the print paper. Fig. 2. Shows Surface profile obtained from Perthometer
Nickel-base super alloy Inconel 718 is a high-strength, thermal-resistant. Because of its excellent mechanical properties, it plays an important part in recent years in aerospace, petroleum and nuclear energy industries. Due to the extreme toughness and work hardening characteristic of the alloy, the problem of machining Inconel 718 is one of everincreasing magnitude. The experiments were conducted for different cutting speed and feed combinations using titanium coated carbide tool. Table 1 shows the chemical composition of work material in percentage by weight
Fig. 1. Perthometer :Surfcom FLEX 50-A
Fig. 2. Surface profile obtained fom Perthometer III. SURFACE TEXTURE MONITORING USING IMAGE PROCESSING BY MACHINE VISION Images are important sources of information for interpretation and analysis. These might be images of a planet’s surface transmitted from a spacecraft, plant cells magnified with a microscope, or electronic circuitry, automated inspection and building undergoing renovations, Human analysis of these images or objects has inherent difficulties: the visual inspection process is time-consuming and is subject to inconsistent interpretations and assessments. Computers, on the other hand, are ideal for performing some of these tasks. For computers to process images, the images must be numerically represented. This process is known as image digitization. The digitization process divides an image into a two-dimensional grid of small units called pixels (picture elements). Each pixel has a value that corresponds to the intensity or colour at that location in the image. Each pixel in the image is identified by its position in the grid and is referenced by its row and column number. Once images are represented digitally, computers can reliably automate the extraction of useful information using digital image processing. Digital image processing performs various types of image enhancements, distortion corrections, and measurements. A. Steps Involved In Image Processing
IMAGE ACQUISITION AND DIGITIZATION
Image acquisition and digitization function involve the input of vision data by means of a camera focused on the scene of interest. Special lighting techniques are frequently used to obtain an image of sufficient contrast for later processing. The elements of the matrix are called picture elements or pixels, in which each element has a value that is proportional to the light intensity of that portion of the scene. A single pixel is the projection of a small portion of the scene, which reduces that portion to a single value. The value is a measure of the light intensity for that element of the scene. Each pixel intensity is converted in to a digital value.
IMAGE ENHANCEMENT
Adjust the brightness and contrast of an image. Use geometrical transformations on an image. Use window leveling on an image. Use filters to improve image quality and extract objects
Improving image quality This segment describes how to improve the quality of machine vision images by increasing contrast, removing noise, and compensating for aspect ratio distortions. There are various methods available to enhance the edges after magnification. Some are Linear Edge Crispening, Statistical Differencing, etc. Again Linear Edge Crispening can be performed by discrete convolution, masking and Fourier domain filtering depending upon the nature of image. There are a few filter,for example 3x3, 5x5 and 7x7 These masks possess the property that the sum of all elements of the filter should be 1 if the resulting image to have the same brightness as the original. If the sum of the elements is larger than 1, the result will be a brighter image, and if it's smaller than 1, a darker image. If the sum is 0, the resulting image isn't necessarily completely black, but it'll be very dark. To sharpen the image is very similar to finding edges, add the original image, and the image after the edge detection to each other, and the result will be a new image where the edges are enhanced, making it look sharper. Adding those two images is done by taking the edge detection filter, and incrementing the centre value of it with 1. Now the sum of the filter elements is 1 and the result will be an image with the same brightness as the original, but sharper.Out of several masks the one, which has been used in this work, is given below:Edge detection filter that detects edges in all directionsDouble filter [filter Width] [filter Height]
=
[
]
(2)
The values '8' were chosen for no particular reason at all, just makes sure the sum of the values is 0. Histogram equalization mapping Histogram equalization operations change the intensity distribution (histogram) of an image. With Matrox Inspector, it is possible to perform uniform histogram equalization, as well as exponential and Rayleigh equalization. The uniform histogram equalization results in a more uniform distribution of your image’s pixel values. The table below lists the density functions used for uniform histogram equalization. Pgg
(3)
gmin g gmax Transfer functions g = [gmax – gmin] Pf(f) + gmin The cumulative probability distribution, Pf(f), of the input image is approximated by its cumulative histogram IMAGE PROCESSING AND ANALYSIS The amount of data that must be processed and analyzed is significant. The data for each frame must be analyzed within time required to complete one scan. A number of techniques
have been developed for analyzing the image data in a machine vision system Blob analysis is a branch of image analysis that allows identifying groups of pixels (known as blobs) within a grayscale image. Once these regions are identified, it is possible to carry out different types of analyses. In blob analysis it is assumed that an un calibrated image contains pixels that are 1 unit by 1 unit in size. Therefore, the area of any pixel is 1 unit squared The processing of the images of the tool insert will be done using view flex image processing software and feature extraction from the images are carried out to obtain feature value wear area. Wear Area =Area of the tool wear region, the number of pixels within the tool wear region The machined surface images acquired by the machine vision system is shown in Figure 3
This section describes how the conventional surface roughness parameter (Ra) is employed for correlating with different tool status using machine vision. The geometry of blunt tool causes a change in surface roughness as machining time elapses. Groove and flank wear are the two kinds of wear that most influence this change in surface roughness. The groove wear changes the tool nose curvature and this is reflected in the work piece surface. It also increases the chip side flow. The above graph shows that there is an increased magnitude of the surface roughness at the beginning of cut, a decreased tendency in the middle region and again an increased tendency at the end of wear. Figure.4 shows the surface roughness increase as the cutting time elapses.The conventional roughness measures (i.e.Ra), when plotted as a function of cutting time, undergoes a complex evolution. The evidence shows that Ra increases steadily with cutting time but then drops markedly as the tool shows marked wear. The drop in Ra at long distances is due to heating and ductile surface deformation by the worn tool [5]Therefore, Ra is not a reliable tool wear indicator, since a single profile measured on a workpiece location will not reveal the textural changes, caused by tool wear state. As the cutting time elapses, the curves have a different behavior owing to the different influence that the cutting condition has on the wear area growth. The small influence of cutting speed on surface roughness was expected since this is often cited in the literature, but the small influence of feed was not expected since there is a strong geometrical contribution of feed to surface roughness C. Work piece status visualization and monitoring
Fig.3 The Experimental Set Up For The Acquisition Of An Image Using Machine Vision
B. Correlating Tool Status And Surface Roughness
Wear Area (in pixels)
2.5
20000
2.0
15000
1.5
10000
1.0
5000
0.5
0 0
200
400
600
800
1000
0.0 1200
Machining Time(in seconds)
Fig.4 Ra and wear area (pixels), as a function of machining time
Ra in microns
wear areain pixles Ra in microns
25000
Work piece status visualization and monitoring of turning Inconel 718 using coated carbide insert was done using machine vision approach. Experiments have been conducted to monitor the changing of workpiece surface texture caused by the increase of tool wear state, through the variation of images of work piece in finish turning, under different cutting conditions. The image-processing analysis was done on the work piece image, Images were taken using a digital camera fitted with focal length lens with extension tubes to give a good magnification. The work piece surfaces were illuminated with white light from a fluorescent light; images were acquired and processed at 8-bit gray level intensity resolution (256 levels of brightness). The image analysis was performed using a commercial software package, View flux. The histogram distributions of an illuminated region of interest (ROI) from turned surface images were analysed for gery level intensity distribution etc. They are recognized as descriptors of image properties such as brightness, contrast, etc. Initially, ROIs were selected from initial work piece surface and final turned workpiece surface images, in order to establish noticeable differences between these textures. This data was used to explore the properties of typical image intensity distributions. Texture is a commonly used parameter in machine vision, which allows images to be segmented into regions based on the structural appearance of the intensity data. [4]
Fig.5 a) Unworn Cutting Tool
b) Worn Cutting Tool
(Flank wear=0.076mm)
(Flank wear=0.32mm)
progressively worn between each sample by turning Inconel 718. The tool condition spanned the range from a new cutting tool to a worn tool. Tool condition was determined by comparing histogram intensity of tool with histogram of work piece texture, from the Fig.5and 6 it can be seen that histogram profiles are distributed for worn tool and its corresponding surface texture. Similarly, histogram profiles are concentrated for fresh tool and its surface texture generated from the fresh tool, while the peak intensity decreases. The changing surface texture produces a corresponding change in the reflected light pattern, or contrast pattern, evident in the image.
Speed=710 rpm, feed= 0.05 mm/rev, depth of cut=0.4mm
Fig.6 Images Of Surface Profile Of The Work Piece And Tool With Histogram: a) Machined With Unworn Cutting Tool, b) Machined With Worn Cutting Tool[speed=280rpm,feed=0.05mm/rev,depth of cut=0.4mm]
This research has tended to focus on quantify the cutting tool status: monitoring of specific machine tool parameters in order to infer tool condition, direct observations made on the cutting tool insert; the development of surface texture vision system that provide information on the condition of the tool employed in turning the surface. The aim of this work is to examine the efficacy of a machine vision system for analyzing the machined surface texture and then assessing the tool condition from the textural information. Furthermore, machine vision system has got image-processing software that generates intensity histogram, from the captured image, that is indicative of tool wear state. The sensor input used is the textural information present in 2-D intensity images captured from the machined surfaces by a digital camera. The surfaces were produced by a turning operation employing a cutting tool insert. The tool was
Speed=710 rpm, feed= 0.06 mm/rev,depth of cut=0.4mm
REFERENCES [1] Ghassan A. Al-Kindia, Bijan Shirinzadehb ― An evaluation of surface roughness parameters measurement using vision-based data‖ International Journal of Machine Tools & Manufacture vol. 47, PP. 697–708, 2007.
[2] B.Y. Lee a, Y.S. Tarng ―Surface roughness inspection by computer vision in turning Operations‖International Journal of Machine Tools & Manufacture vol. 41,pp. 1251–1263, 2001. [3] Rajneesh Kumar, P. Kulashekar, B. Dhanasekar, B. Ramamoorthy ―Application of digital image magnification for surface roughness evaluation using machine vision‖ International Journal of Machine Tools & Manufacture vol.45, pp.228–234, 2005. [4] David Kerr · James Pengilley · Robert Garwood‖Assessment and visualisation of machine tool wear using computer vision‖ International Journal of Advanced Manufactuing Technology vol.28, pp.781–791,2006.
[5] C. Bradley and Y. S. Wong ―Surface Texture Indicators of Tool Wear – Speed=710 rpm, feed= 0.07 mm/rev,depth of cut=0.4mm
Fig.7. Images of machined surfaces at different machining conditions
From the surface image vector, a rough surface can be implicit as an image with the grey levels corresponding to the surface and deeper a valley, the darker the corresponding pixel. Similarly, the higher a peak is the brighter the Corresponding area in the image. This technique that can be considered to characterize a texture using machine vision system.also,The changing shape of the feed marks on the surface with different feed rate is evident in the gradual but consistent change in the histogram shape[5]; i.e. a larger number of higher intensity grey level pixels for the worn tool as shown in Fig.7. Moreover, variation in the ambient lighting circumstances and shadowing effects would also give complications in a histogram-based method. IV CONCLUSION
In the present work, experiments have been conducted on automatic precision lathe with different cutting conditions Surface roughness of work piece were measured using stylus method. The variation of roughness parameter with machining time, for which trend has been generated In this study, the application of machine vision system for surface texture monitoring using image processing is discussed. For real time surface texture condition monitoring with noncontact techniques, the image processing algorithms can be used for enhancing the automation proficiency in unmanned machine tool. The results obtained in each case shows that, the procedure explained in this work, is able to Correlates tool status and surface roughness by monitoring machine vision images in turning with the help of histogram profile
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