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[nt J Adv Manuf TechnolTable 1 Machining parametersMachine toolWork pieceCutting toolConventional lathe machine: Model: Pinocho S90/206Steel rod AISI 1035 (0.32--0.38% C, 98.63-99.09% Fe, 0.6-0.9% Mn, 0.04% P (max), 0.05% S(max))Uncoated cemented carbide: TPUN-16-03-04_HI3A, Sandvik Co, Ltd, SwedenFeed rateMachining depthCutting speedCoolantMachining time0.25 mrn/rev0.5 mm134 m/minAir0-40 min0.25 mm/rev0.5 mm190 m/minAir0-40 minFigure 1 (b) shows the gradient!,(x m ) extracted from thesimulated image of a worn cutting tool using p=5. Thepresence of a notch defect causes a change in the gradientare due to the discretization of the image. If the 'defect'gradient profile is fitted with a n-th order polynomialfunctionfnp(x), then the differenceprofile and can be easily visualized from the figure. Thesmall fluctuations of the gradient values along the profile d(x) = f'(xm) - fnp(x) (4)1.521.5_C]----.J -0.5-:: ---------------~-----------------------..~'-- 0.5~
lnt J Adv Manuf Technol1.6.,--------------------1.41.2.::~~ 0.8>:)min with polynomial fitting order nwill reveal the location of the notch defect in the image. Thenotch can be detected by defining a threshold t such thatwhen Id(x)1 > t a notch is considered to exist as illustratedin Fig. 1 (c). The size of the notch defect can be deducedfrom Id(x)l. The step lengthp was varied fromp=l to p=10to investigate the effect ofp on the capability ofthe proposedalgorithm in detecting the presence of the notch defect.3 Algorithm for notch wear detectionThe various stage of determining of notch wear location isshown in Fig. 2 and is described as follows:(a) Image binarizationSince the method of notch detection is based on gradientchanges in a 2-D image, it was sufficient to work withbinary images. The original image that exists in the grayscaleformat is converted to a binary image using a simplethresholding method. This method changes all gray valuesbelow a certain pixel threshold T to 0 and those above T to1. The value of T can be determined from the histogram ofthe original grayscale image.(b) Image rotationThe binarized image is rotated clockwise by 90° becausethe algorithm used to detect the gradient was written suchthat scanning starts at the upper left of the image and endsat the lower left. In each row, the horizontal scanning stopswhen a black (0) pixel is encountered and the gradientcalculation will then proceed.(c) Gradient computationThe measurement procedure, refen-cd to as orthogonalscanning, involves repetitive horizontal scanning to detelminegradient at each pixel on the tool edge (Fig. 3). The horizontalscan line starts from the point of origin [1, I] and ends at apoint on lower left of the image. For simplicity, for the i-thscan line Sdi) the stati point is denoted as Py(i) and the endpoint as Pdi) at the black pixel. The corresponding) valueat the intersection is denoted as j(i). When the end pointPeri) is met, the scanning is repeated at Sdi-p) and Sdi+p)and the locationsj(i-p) andj(i+p) are recorded as)\ andhrespectively. For p=5, the gradient at point Pc;(i) is given by:h - j, h -)1m= =---(i+5)-(i-5) 10(d) Polynomial fIttingIn this stage, the gradient data is fitted with a polynomialfunction ofdegree n using the Matlab function polyfit{-t,J',n).The result of this function is a row vector of length n+ 1containing the polynomial coefficients in descending powers:p(x) = P\X n +p2'Y'-' + .... + PnX + PI7(e) Determination of noteh wear locationIn order to determine the notch wear location, thepolynomial fitted data was subtracted from the gradient(5)(6)1.5-...~c:..0.5 /t~~ I -...~-1-1.5Notdlwearslaltsal520pixalNOlch wear ends 81554 pixel400 11mFig. 7 Notch wear detected on cutting tool having simulated notchDistance along x (pixels)id Springer
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Page 3 and 4: Contents:1. Introduction1.1 Project
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Page 27 and 28: Int J Adv Manuf TechnolFig. 9 Image
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