[机械模具数控自动化专业毕业设计外文文献及翻译]【期刊】在精车过程中使用视觉机器人亚像素边缘检测测量表面粗糙度-外文文献

INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 11, pp. 2239-2249 NOVEMBER 2014 / 2239 DOI: 10.1007/s12541-014-0587-3 In-Process Measurement of Surface Roughness using Machine Vision with Sub-Pixel Edge Detection in Finish Turning Mohan Kumar Balasundaram1 and Mani Maran Ratnam1,# 1 School of Mechanical Engineering, Engineering Campus, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia # Corresponding Author / E-mail: mmaraneng.usm.my, TEL: +06-4-599-6325, FAX: +06-4-594-1025 KEYWORDS: Surface roughness, Non-contact, Machine vision, In-process Common methods of in-process surface roughness measurement are capable of providing a limited number of amplitude parameters for roughness assessment. This is mainly due to the averaging effect of the sensors used. In this work, we measured the amplitude, spacing, hybrid as well as functional surface roughness parameters during dry turning of AISI 1035 carbon steel using machine vision. A commercial DSLR camera with high shutter speed was used to capture a blur-free image of the workpiece surface profile diametrically opposite the cutting tool. The edge of the surface profile was detected to sub-pixel accuracy using the grey level invariant moment and the roughness parameters were determined from the profile. The tool nose wear and machining time were correlated with amplitude, hybrid, and spacing surface roughness parameters, as well as the bearing area curve parameters. Three new roughness parameters, namely average slope of profile peaks (p), average slope of profile valleys (v), relative length of peaks (Rrl), were introduced to study the effect of changes in tool nose micro geometry due to wear on the surface roughness. Among these new parameters p and Rrl showed better correlation with machining time and nose wear compared to all other parameters. Manuscript received: February 9, 2014 / Revised: June 17, 2014 / Accepted: June 18, 2014 NOMENCLATURE Ra = average roughness Rq = root-mean-square roughness Rt = maximum peak-to-valley roughness Rz = ten point average height Rsk = skewness Rku = kurtosis Rpk = reduce peak height p=average slope of profile peaks v=average slope of profile valley Rrl =relative lengths of peaks 1. Introduction Surface roughness is typically used to describe the surface quality of a machined part. Roughness is usually measured using the conventional stylus instruments. In this method a stylus is drawn across the surface and the minute fluctuations in the vertical direction are amplified and recorded. The roughness parameters are then computed from the vertical fluctuations. The main limitation of this method is that it cannot be applied to a rotating surface, such a workpiece that is being machined on a lathe. To measure the roughness of such a part using the stylus method it is necessary to stop the lathe, thus interrupting the machining process. An in-process surface roughness measurement method is desirable to assess and control the surface quality of the workpiece during the machining process. In-process assessment of surface roughness of a workpiece during machining has been an area of active research in metal cutting over more than a decade. Although the ability to measure the surface roughness during machining has significant potential for manufacturing applications, gaining access to the workpiece surface for roughness measurement is difficult. Due to the high surface speed of the workpiece it is impossible to measure the surface roughness using the stylus instruments. Therefore, most of the techniques proposed in the KSPE and Springer 2014 2240 / NOVEMBER 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 11 past are non-contacting. These include optical, capacitive, ultrasonic and pneumatic methods. The optical methods can be subdivided further into light scattering, speckle contrast and machine vision methods. Among these the light scattering and speckle contrast methods are the most widely studied methods for in-process roughness measurement. Digital images acquired by a vision system have also been processed to extract the roughness of a moving surface but not on a surface rotating at a high speed. Some of the related researches on in-process roughness measurement are briefly reviewed. Gupta and Raman1 measured the surface roughness of a turned workpiece using images of the scattered spectrum of laser light reflected from the workpiece rotating at spindle speeds ranging from 140 rpm to 285 rpm. First order statistical texture descriptors were extracted based on the grey level histogram of the images captured using a CCD camera. Although it was concluded that the speed of rotation of the work piece and ambient lighting did not affect the extracted surface roughness, the measurement was carried out on pre-turned workpiece. The effects of other parameters such as presence of coolant, reflectivity of the surface, chips, vibration etc. that occur in real machining were not studied. Rong-Sheng et al.2 used the light scattering method for on-line surface roughness measurement. The surface roughness was extracted from the laser light scattering intensity distribution in the direction parallel to the surface micro machining marks. The intensity distribution was found to follow a Gaussian distribution whose parameters depended on the root-mean-square surface roughness. Wong and Li3 used the combined effect of interference and light scattering to measure the roughness of a surface moving at a speed of up to 3.7 m/s in a cylindrical grinding process. The bright and dark intensity ratio in the image was related to the average roughness Ra. Persson4 used the angular speckle correlation technique to measure the surface roughness parameters (Ra, and Rq) of machined surfaces. A speckle pattern was formed on the machined surface with the help of a coherent He-Ne laser and captured at diverse angle of lighting. Although this method was intended for in-process application their results were limited to off-line measurement. Dhanasekar and Ramamoorthy5 captured the images of moving machined surfaces (milling and grinding) using machine vision technique and then deblurred the images using RichardsonLucy restoration algorithm. The deblurred images were pre-processed to compensate the inhomogeneous illumination. The spatial frequency, arithmetic mean value and standard deviations were extracted as texture features. The results from vision system and stylus method were compared to show a good correlation. However, this method can only be used for the machined surfaces moving at low speeds. Wang et al.6 developed an optical technique, termed as dark bright ratio method, which utilises the size of dark or bright area on an image of reflectance to interpret the roughness of a ground surface. The reliability of their technique in obtaining roughness data of surfaces was proven only up to the linear speed of 0.107 m/s (60 rpm). Sarma et al.7 measured the arithmetic average roughness parameter Ra at different cutting conditions using the surface image of glass fibre reinforced plastic (GFRP) composite hollow bar turned by polycrystalline tools. The average grey scale value of the image was correlated with the surface roughness. A normalized power spectrum was obtained from the experimental images. The effect of cutting speed, feed, and depth of cut and fiber orientation angle on surface roughness was studied. However, this method is prone to errors caused by blurring effect due to the capture of low resolution (768574 pixel) images. Several researchers have studied the effect of tool wear on surface roughness when machining different types of materials. Penalva et al.8 estimated tool wear by simple roughness measurement when machining hardened steels using a shop floor instrument. Seeman et al.9 developed a strategy to optimize tool wear and surface roughness in machining of particulate metal matrix composite using response surface methodology. The authors measured the average roughness Ra off-line using a stylus instrument. Shahabi and Ratnam10 measured the surface roughness by stopping the lathe and correlated the roughness with tool nose wear. Saini et al.11 also measured the roughness off-line using a commercial surface roughness tester. In most of the published literature involving study on surface roughness and tool wear the authors either removed the part for roughness measurement or measured the roughness in-situ, i.e. without removing the part. In either case it is necessary to stop the machine, thus interrupting the machining process. A truly in-process roughness measurement method that is capable of providing the numerous parameters similar to those determined using the stylus instrument has yet to be developed. The difficulties in measuring surface roughness during machining are caused by the presence of cutting fluids, cutting chips, vibration, deflection of workpiece, dust and the heat generated. Therefore, several indirect methods of measuring surface roughness in-process have been proposed in the past. In the method developed by Chen and Savage12 a fuzzy-net based model was used to predict surface roughness in-process by extrapolating indirectly from vibration and cutting conditions. The data were analyzed and a model was constructed using the neural fuzzy system. Their system was shown to be able to prediction average roughness (Ra) up to 90% accuracy. Although several authors have successfully implemented the light scattering, speckle contrast, pneumatic and capacitive sensor methods for in-process roughness measurement,1,2,4,13-19 these methods are capable of providing only the basic amplitude roughness parameters, such as average roughness (Ra) and root-mean-square roughness (Rq). This is due to the ability of some of the sensors, such as a photodiode, to detect only the root-mean-square (RMS) value of the signal. Moreover, the information from the sensors is indirectly correlated with the surface roughness thus making it impossible to separate waviness from roughness using various filters. None of the non-contacting in-process methods developed in the past, including the commercially available Lasercheck roughness measurement gage developed by Schmitt Industries Ltd.20 is capable of measuring other roughness parameters, including the numerous amplitude, spacing, hybrid parameters as well as functional parameters, such as the bearing area curve. Measurement of these parameters currently can only be done by using the stylus instrument. However, in-process measurement of these parameters during machining will be important to understand how the surface roughness evolves during machining, to control the surface quality and to detect the on-set of tool failure. Machine vision methods of roughness measurement coupled with image processing are capable of providing many more surface INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 11 NOVEMBER 2014 / 2241 parameters compared to other non-contact methods. For instance, Kamguem et al.21 measured five amplitude parameters, namely Ra, Rq, Rv, Rt and Rz, using image texture gradient factor. However, the images were acquired off-line and the method was limited to only the amplitude parameters. Al Kindi and Shirinzadeh22 extracted vision data using two light reflection models and related the parameters with nine amplitude and spacing parameters. Although good agreement with stylus instruments results were reported for most the parameters the image were not acquired in-process. In this study a non-contact direct method using machine vision has been developed to measure the surface roughness of workpiece during dry turning operation. This method is based on the machine vision technique developed by Shahabi and Ratnam23 for non-contact roughness measurement for off-line roughness measurement. The method was also used to study the effect of nose wear on the surface roughness of workpiece during turning of AISI 1035 steel workpiece. In addition to the established amplitude, spacing, hybrid and functional roughness Fig. 1 Schematic of in-process roughness measurement system parameters three new parameters that are unique to the turning process have been introduced. 2. Methodology 2.1 System configuration A schematic diagram of the experimental set-up is shown in Fig. 1. The basic components of the system setup comprises a positioning mechanism, a 10.2 megapixel digital single-lens-reflex (DSLR) camera (SONY -230) having a picture resolution of 38722592 pixels and a high-resolution (13921040 pixels) CCD camera (JAI CV-A1). The DSLR camera was fitted with a 4X close-up lens and connected via USB cable to a personal computer for surface roughness measurement. The CCD camera was fitted with a 50 mm lens for tool wear measurement. Backlight illumination was obtained by using a high-frequency fluorescent source to capture the contour of the cutting tool and work piece. Both cameras were mounted on an X-Y axis motion camera mount. The X-Y axes were adjusted to obtain the correct focus distance during image capture as shown in Fig. 1. The actual setup of the in-process roughness and tool wear measurement is shown in Fig 2. 2.2 System calibration 2.2.1 Calibration of DSLR camera A commercially available multi-frequency grid distortion target (Edmund Optics Ltd.) was used to check for the presence of distortion in the image captured by the DLSR camera. The grid target was placed in orthogonal directions in the object plane and captured using the DSLR camera. The distances between the dot centers were measured to check for distortions caused by the camera lens. The maximum difference in distances between the consecutive points was found to be only two pixels (0.2%). 2.2.2 Calibration of CCD camera A high precision Ronchi ruling (200 lines/inch) was used to check for the presence of distortion in the image captured by the CCD camera. Separate images of the rulings placed in the horizontal and vertical positions were captured using the 50 mm lens. The images were contrast enhanced and scanned at various points. The maximum Fig. 2 Actual setup of in-process roughness measurement system difference in distance between these points is 3 pixels (0.24%). 2.2.3 Determination of scaling factor The horizontal and vertical scaling factors to convert the image dimensions in pixels to micrometres were obtained by using a standard Mitotuyo pin gage of 1.35 mm diameter. The horizontal and vertical scaling factors for the DSLR camera are 8.35 m/pixel and 8.33 m/ pixel respectively, while the corresponding scaling factors for the CCD camera are 1.87 m/pixel and 2.12 m/pixel respectively. 2.3 Machining condition Dry turning tests were performed on the Optimum Vario D320630 conventional lathe. The work piece material is AISI 1035 steel rod (diameter=30 mm, length=250 mm). The work piece was machined continuously using uncoated carbide triangular inserts (TCMT110208) with the same cutting tool under dry turning with the following parameters: feed rate=0.3 mm/rev, depth-of-cut=0.1 mm, cutting speed = 60 mm/min and machining duration up to 140 minutes. 2242 / NOVEMBER 2014 INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING Vol. 15, No. 11 2.4 Image acquisition of workpiece surface using DSLR camera The shutter speed of the DSLR camera was set to 2.5 ms. The focusing ring of the camera was adjusted so that the edge of the work piece is sharply in focus. Images of the workpiece surface were captured diametrically opposite the cutting side during machining (Fig. 3). A sequence of ten images of the rotating workpiece surface was captured at a time interval of 2 s. The shutter speed of 2.5 ms causes the helically shaped surface formed on the workpiece to rotate by 1.05, which is equivalent to a linear shift of only 6.1 m, i.e., 3% of the wavelength (feed mark distance) of the surface undulations. This causes minimum blurring in the workpiece image. A cropped out image of the region of interest (ROI) is shown in Fig. 4(a). The edge images were captured at different intervals of machining time ranging from 5 minutes to 140 minutes. 2.5 Image acquisition of cutting tool insert using CCD camera Back lighting was used to capture the contour of the cutting tool using the CCD camera in-between the cutting cycles without removing the cutting tool from the machine. Fig. 4(b) shows the image of a new cutting insert captured using the CCD camera. 2.6 Roughness measurement using 2-D image of workpiece Fig. 3 Image showing the cutting tool, work piece and the region of interest (ROI) on the workpiece surface 2.6.1 Edge detection to sub-pixel resolution using grey level invariant moment The precision of edge detection in the image influences the accuracy of the roughness measurement. The step edge model applying the concept of moment invariance method proposed by Tabatabai and Mitchell25 was adopted to detect the edge of workpiece image to sub-piexel accuracy. The gray level moment edge operator for an image based on the first three moments m1, m2, m3 of the input data sequence are given by a threshold independent method based on grey level moment equations. Moments are defined as a sum of pixel intensity powers. The i th moment of the input data sequence xj are given by 1 n i mi = - j=1 ( xj) , i = 1, 2, 3 (1) n where x1, x2, , xn are pixel intensities. Assuming that ph is the number of pixels with gray level intensity values h, if we the define densities p1 and p2 as Fig. 4 (a) Region of interest (ROI), (b) image of new cutting tool insert nose region 1 1 p2 = - 1 s - (7) 2 2 4 + s p2 h = m1 - (8) p1 p1 k = 2 - (9) p2 where m 3 + 2 m 13 3 m 1 m 2 s = - (10) 3 2