Computer Vision and Classification Techniques on

Computer Vision and Classification Techniques on the Surface Finish Control in Machining Processes* Enrique Alegre, Joaquin Barreiro, Manuel Castej6n, and Sir Suarez Unive$idad de Le6n. E.I. Industrial e Inform6tica. 24071.Le6n. Spain es enrique.alegre@uni1eoa.

This work presents a method to perform a surface finish conAbstract, trol using a computer vision system. Test parts used were made of AISI 303 stainless steel and were machined with a MUPEM CNC multi-turret parallel lathe. Using a Pulnix PE2015 B/W camera, a difiuse illumination and a industrial zoom, 140 images were acquired. We have applied a vertical Prewitt filter to all the images obtaining two sets, the original one and the filtered. We have described the images using three difierent methods. The first features vector was composed by the mean, standard deviation, skewness and kurtosis of the image histogram. The second features vectol was made up by four Haralick descriptors - contrast, correlation, ener8y and homogeneity. The last one was composed by 9 Laws descriptors. Using &-nn we have obtained a hit rate around 90 % with filtered images and, the best one, using Laws features vector of 92.14To with unfiltered images. These results show that it is feasible to use texture descriptom to evaluate the rugosity of metallic pads in the context of product quality inspection.

Introduction Diverse properties play an important role in the surface finish of the metallic parts, €.g. the mechanical strength, the wear resistance of the surfaces or the geometrical and dimensional quality of the characteristics of the parts. These properties are directly related to the surface finish level, which is determined by the manufacturing processesand the materials used. Thus, the measurement of the surface finish has been a research matter of special interest during the last fifty years in the machining sectorThe surface finish degree can be objectively estimated by the measures of diverse rugosity parameters established in the international standards [1]. The development of these standards is basically oriented to tactile measuring devices that provide two dimensional records of the part profile. * This work has been partially supported by the research project DPI2006-02550 ftom the Spanish Ministry of Education and Science a,rld the ULE2005-01 from the University of Le6n. A. Campilhoand M- Kamel(Eds.):ICIAR 2008.LNCS5112.pp. 11011110,2008. Berlin Heidelbcrs2008 @ Springer-Verlas

1102

E. Alegreet al.

Nevertheless, during the last decades the sudace measurement technologies have significantly evolved, from the first analogical contact measuring devices to the current digital techniques such as the optical, capacitive or laser devices I2]. Amid the modern techniques, those based on computer vision can be remarked. The advantagesthat this technology brings are many. While tactile techniques characterize a linear track over the surface of the part, the computer vision techniques allow to characterize whole areas of the surface of the part, providing more information [3,4,5]. Besides, computer vision techniques take measuresfaster, as images are captured in almost no time and so they can be implemented in the machine. According to this, it is possible to apply these techniques in the control proccssesin real time on an autonomous manner. Besides, with them it is possible to apply an exhaustive validity check to every single part produced, which would be very difficult to apply with traditioral tactile rugosimeters, slow and delicate as they are. Continuous advances have been made in sensing technologies and, particularly, in the vision sensorsthat have been specially enhanced in capabilities and whose price has decreased. The advances made in the image processing technology provide more reliable conclusions than before. In all, computer vision is a very interesting technology for the industrlal environment. The use of these systems ilr other monitoring operations in machining processeshas proved 16,7] an important reduction in the cycle time and the needed resources. As lar as the traditional contact techniques are concerned) computer vision techniques use other parameters to determine the surface finish level. In the light of this perspective, the current standards, developed for tactile devices, do not completely reflect the cuuent state of the technology. It is necessaryto find procedures that allow to correlate the results obtained by tactile instruments with those obtained with other kind of devices, as those based o! computer vision. In this field, two guidelines should be remarked: the study on the spatial domain and the study in the frequency domain [8,9]. Our work tackles the measurement of the surface quality from the point of view of the spatial domain. Tarng and Lee [10] and Lee et al. [11] analyze the artiflcial vision and image analysis systems so as to quantify the rugosity in diffcrent turling operations. Those methods based on image analysis capture an image of the surface and analyze the pixels so as to obtain a pattern of the diffuse light of the image. Later on, rugosity parameters are calculated by means of statistical descriptors. One of the more frequently used parameters is the standard deviation of the gray levels. Kumar et al. [12] focus on the milling, turning and molding processeszooming the original images and obtaining the Ga parameter (the image gray Ievel average), finding a high correlation amongst the Ga parameter and the rugosity of the surface. AI-Kindi et al. 13]propose a method named intensity topography compatibility (ITC), characterizing the image data by three componerts, namely lightning n(J,y), reflectancer(r,9) and surfacecharacteristicsF'(r,y) and they calculate the value of the traditional rugosity parameters combining statistics such as

Computer Vision and ClassificationTechniques

1103

mean and standard deviatiol. Juan et al. [13] develop a computer vision system that automatically measuresthe value of the rugosity in turning processesintroducing the theory ofpolynomial networks. Gadelmawla [14] usesthe coocurrence matrix and introduces a new parameter, the maximum width of the matrix for calculating the rugosity of the surface. Other authors, [16,15,17] also use the coocurrence matrix and the texture energy. Ikonen and Toivanen [18] propose an algorithm that priorizes a pixel in the tail so as to calculate the minimum distance in a curved space so that it helps in calculating the rugosity in a faster and more eficieut manner, Kassim ct al. 119]use a column projection system, a connectivity algorithm oriented by the Hough transform and run Iength statistics for the analysis of machined surfaces. Ngan [20] proposes a technique to quantifu the polish degree on moulds and matrices surfaces. Other techniques [21] are based on the lightning of the surface by changing the lightning angle so as to correlate two patterns and use them as rugosity parameters. So as to estimate the rugosity of the surface, another important aspect in the cutting palameter is the combination of image analysis with neural network techniques [22,23],support vector machines 124),fuzzy \ogic [25] and genetic algorithms [26]. This work combines the use of digital image descriptors along with classification techniques to estimate the surface finish of metallic parts obtained by a turn process. The initial goal pursued in this work was to design an acceptance conirol strategy, that is to say, to define two classesrelated with two rugosity intervals. Class 1 would contain those parts with low rugosity -acceptableand class 2 would contain those with high rugosity

2 2.1

defective

Design of the Experiment and Image Acquisition Set of Samples Used

Test parts were made of AISI 303 / EN10088 X8CrNiS18 9 stainless steel composition shown in Table 1. This material was chosen due to its extelsive use in the industry ofsmall part mass manufacturing. A MUPEM CNC multi turret parallel lathe ICIAR/1/42 model was used for the machining of palts. The test part is showed in frgure 1. Several part operations were carried out, all of them typical of massive precision mechanization, although only the cylindrical shape was used for surface finish measurcmentThe cutting tools were coated carbide inserts from Sandvik with the following technical specifications: tliargular, tip angle 600, tip radius 0.8 mm, edge length 11 mm, incidence angle 7', rake angle 6", position angle 93", edge radius 0.03 0.05 mm. The machining parameters used for the tests were fixed at the following

Table 1. ComDosition of AISI 303 stainless steel

c < 0.10