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Evaluation of Honed Cylinder Bores F.Puente Leon Design of Systems on Silicon(DS2),Parque Tecnologico de Valencia, C./Charles Robert Darwin 2,E-46980 Paterna(Valencia),Spain Submitted by G.Spur(1),Berlin,Germany Abstract The quality of the honing texture on cylinder bores of combustion engines plays an important role with respect to oil consumption,noxious emissions,and running performance.To evaluate honed surfaces objectively,features describing the surface texture are extracted from 2-D data of the surface.The paper focuses on two crucial stages of the data analysis:the preprocessing,which aims at suppressing irrele-vant components and enhancing the information of interest,and the feature extraction,which yieldsreliable numerical estimates of the surface characteristics of interest,like the honing angle,groove pa-rameters,surface defects etc.The assessment results can easily be adapted to user-specific ratings. Keywords:Honing,Surface texture,Automated visual inspection 1 INTRODUCTION Cylinder bores of combustion engines are finished by honing.The resulting surface texture mainly consists of two bands of helical grooves placed stochastically and appearing at different angles to the cylinder axis.The texture quality is highly important for dry operation properties,oil consumption,noxious emissions,and running performance.Up to now,experts are still rating honed surfaces visually based on microscopic images.This method is tedious,subjective,and time consuming.To get objective and reproducible results,an automated method of inspection is necessary. 2 INSPECTION APPROACH 2.1 Surface data There are basically different ways to measure the texture of a honed surface;see Table 1.Typically,a mechanical stylus only performs a I-D measurement of the surface profile.In contrast to this,grey level images and optical profilometers provide 2-D data in a reasonable amount of time. Because the lateral-geometric features of honing textures can only be analysed with 2-D data,in the following we will concentrate on such kinds of data.Other characteris-tics related to the different measurement principles investigated are also included in this table. A signal model describing the essential characteristics of a honing texture constitutes the basis of the evaluation approach presented in this paper.Based on this model,clear and mathematically well-defined features are introduced,which enable a reproducible and objective assessment of the texture.This strategy differs from many popular methods-such as those relying on neural networks-,which are often treated as ablack boxI.The features chosen are inspired by the Honing Atlas2,by many opinions of experts,and have also been ex-tended by adding new volumetric parameters for the case of analysing profile data.This results in an extensive set of features that can be customized to match the needs of individual users. 2.2 Properties of honing textures Figure 1 shows some of the properties of honing tex-tures,based upon which features are to be ntsdefined.The most popular ones are the roughness parameters,such as those based on the Bearing Ratio Curve(Abbott Curve)3,and R,R,and R,4.However,dealing with honed surfaces,it is important to define features that take the lateral geometry into account.This way,most relevant texture peculiarities can be described,such as the honing angle,material smearings,groove interrupts, stray grooves,holes,foreign bodies,and flakes,as shown in Figure 1.In addition,features describing the balance of grooves,presence of plateaus,shape of grooves,cracks,residual turning grooves,and chatter marks are also needed. 2.3 Automated inspection Figure 2 shows an overview of the abilities and aims of automated inspection in quality control applied to the honing process.A 2-D or 3-D sensor provides data g(x) of the honed surface,where x=(,yE )R2 denotes the lateral spatial coordinates.The grey coloured blocks of the diagram are part of the sensor data processing.The outputs of the system can be used simply as a statement about surface quality,to give alarms causing an interrupt of the machining process,or it can be fed back via a controller to regulate the honing process,because the honing texture contains information about both functional-ity and also machining procesdndependently of the fact whether a post-honing brushing is performed or not.In the following sections,we will focus on two crucial stages of the automated inspection:the preprocessing of the sensor data and the feature extraction,and we will give some examples to these steps. 3 PREPROCESSING The goal of the preprocessing is to suppress irrelevant components,namely the inhomogeneities i(x)and the disturbances b(x),while enhancing the information of interest,i.e.the texture t(x).In the case of acquisition of image data,the inhomogeneities i(x)could be due to Table 1:Comparison between mechanical stylus devices,grey level images,and optical profilometers grey iron cylinder ntsFigure 2:Automated inspection of honed surfaces. Figure 1:Honing textures showing lateral features and defects:(a)material smearings,groove interrupts; (b)stray grooves;(c)holes or foreign bodies;(d)flakes. signal of interest inverse transform separation transform Components Figure 3:Principle of the preprocessing.spatial variations of surface illumination.Other compo-nents assigned to the disturbances b(x)include e.g.deviations from the ideal course of the grooves and defects,such as material smearings,flakes etc.We use a signal model that describes the sensor data g(x)as a combination of the texture t(x)and the irrele-vant components i(x)and b(x): To be able to recover the information of interest t(x),an assumption is necessary:the different components have to be mathematically distinguishable. As shown in Figure 3,a transform maps the raw data g(x)such that a strict separation of their components is obtained.Then,the undesired components are sup-pressed,and finally an inverse transform is performed that yields the results of the preprocessing. The benefits of this procedure include a simplification of the feature extraction,and a more robust image process-ing,as shown in the following examples. 3.1 Homogenization When a groove texture is degraded by an intensity inhomogeneity i(x)due to the data acquisition process,e.g.due to an inhomogeneous lighting,a homogenization can be performed to suppress this unwanted component 6.Figure 4 shows an example of this operation for a planing texture.On the left side of the figure,the original texture is shown.The central image represents the result of a standard homogenization method-the homomorphic nts Figure 4:Homogenization:(left)planning texture;(centre) homomorphic filtering;(right)homogenization result. Figure 5:Texture decomposition:(left)honing texture; (centre)groove texture;(right)background texture. Figure 6:Reference surface:problems with conventional low-pass filters. filtering,which assumes a multiplicative combination of texture and inhomogeneity.Especially in the upper left corner,this image shows a very poor contrast.The image on the right results from the model-based approach according to Figure 3.In this case,a homogenization of the local mean value and the local contrast has been performed based on a model that considers a mixed additive and multiplicative combination of both signal of interest and disturbing inhomogeneity6.The result is clearly more homogeneous than the former one and enables a more robust analysis of the texture. 3.2 Texture decomposition The next example concerns the decomposition of the honing texture to ease the feature extraction.Due to the complexity of the honing texture,the extraction of rele-vant features needed for the inspection task could be simplified considerably,if the partial textures constituting the signal g(x)according to Eq.(1)were available.Thus, it would be advantageous to develop a method to sepa-rate the texture g(x)into a component t(x)containing the straight structures(i.e.the grooves)and another one b(x) showing the isotropic components(i.e.the background, including defects and objects).In this case,a homogene-ous texture will be assumed. Fortunately,a very efficient algorithm to perform this separation already exists7.The left side of Figure 5 shows an original honing texture;the other two images represent the results of the adaptive texture decomposi-tion computed with this algorithm.In the groove texture,only the ideal grooves can be seen,whereas the back-ground image contains all deviations from the ideal groove course as well as defects and other objects.For a more comprehensive discussion of the separation algo-rithm,interested readers are referred to7 nts Figure 7:Original honed surface and reference surface. 3.3 Reference surface Finally,the definition of a reference surface to eliminate the shape component will be presented.The graph in Figure 6 represents a trace through the profile of a honed surface.The smooth line describes the shape component to be suppressed.However,conventional low-pass filters lead to distortions in the area of the grooves,as shown in the case of the dashed line.We have faced this problem by developing an iterative 2-D filter-a modified Gaus-sian filter-hich behaves robustly even in case of deep grooves8.The 3-D plot depicted in Figure 7 shows a section of a honed surface as well as the resulting reference surface computed with this method. 4 FEATURE EXTRACTION 4.1 Honing angle The first example of the feature extraction is the estima-tion of the honing angle.To this end,the periodogram (PG)is computed,which is proportional to the squared magnitude of the Fourier transform of the texture g(x): The PG is an estimator of the power spectral density(PSD)function,which specifies the spectral properties of the stochastic process generating the texture9.Then, the PG is projected radially;see Figure 8. Since honing textures consist of two bands of grooves, the projection function also shows two pronounced maxima.The estimate of the honing angle results as the difference between the locations of both maxima: Despite the variance of the PG,due to the averaging performed,the radial projection is a very smooth curve. Thus,this procedure yields a fast and statistically reliable estimate for the honing ntsangle. Figure 9:Illustration of the Radon transform. Figure 10:Detection of defects:(a)groove image; (b)Radon transform of(a);(c)multiplication of(b)and (e);(d)background image;(e)Radon transform of(d); (f)defective grooves detected. Figure 11:Algorithm to detect defective grooves. 4.2 Groove parameters The next example concerns the extraction of the groove parameters.This is accomplished based on the Radon transform,which maps each line of a 2-D image onto a point of the transformation domain;as demonstrated in Figure 9lo.Following,all distinct peaks of the Radon transform,which correspond with grooves,are detected by means of morphological filters.Finally,for each detected groove,the corresponding parameters(ampli-tude,width,location,and angle)are estimated based on the output of the morphological filters9. 4.3 Detection of defects In Section 3.2,an algorithm enabling a decomposition of honing textures has been presented.This section fo-cuses on the background texture obtained,which con-tains the main information concerning defects and objects,and discusses a robust approach allowing a detection of defects based on this image.It represents a refinement of the detection of grooves presented in the last ntssubsection;see Figure 1111.In this case,a Radon transform of the groove image Figure l0(a)obtained after decomposition is performed to concentrate the information concerning grooves;see Figure 10(b). Furthermore,collinear defects distributed along grooves are also concentrated by means of a Radon transform of the background image onto peaks in the Radon domain; see Figures 10(d)and(e).By combining both groove texture and background texture in the Radon domain multiplicatively,only the peaks representing defective grooves remain;see Figure l0(c).The most pronounced peaks in this image correspond with the locations of the three grooves sketched in Figure IO(f),which are indeed the most salient defective grooves of the original image; see Figure 5(a). 5 SUMMARY AND CONCLUSIONS This paper has shown how signal processing methods can be used to automatically evaluate relevant properties of the honing texture of grey iron cylinders with regard to different quality aspects.A preprocessing strategy has been presented that enables a robust automated as-sessment.Moreover,a feature-oriented approach has been proposed,in which the features are clear and mathematically well defined.By incorporating depth data, new function-relevant parameters can be computed. In previous approaches,only roughness parameters from first-order statistics have been used to quantify the features of interest.The presented strategy,however,is based on an analysis of the essential lateral-geometric characteristics of the texture,including those related to higher-order statistics.This enables to automate and objectify the assessment proposed by experts and standards used in different companies. IMalburg,M.C.,Raja,J.,1993,Characterization of surface texture generated by plateau honing proc-ess,ClRP Annals,42/1:637840. 2AE Goetze GmbH,Burscheid,Germany,1993,AE Goetze Honing Guide-Rating Criteria for the Hon-ing of Cylinder Running Surfaces. 3DIN EN I S 0 13565-2,1996,Geometrical Product Specification(GPS)-Surface texture:Profile method;Surfaces having stratified functional prop-erties-Part 2:Height characterization using the linear material ratio curve. 4DIN 4768,1990,Determination of roughness parameters R,R,R,by means of stylus instru- ments;terminology;measuring conditions. 5Pfeifer,T.,Wiegers,L.,1998,Adaptive control for the optimized adjustment of imaging parameters for surface inspection using machine vision,ClRP An-nals,47/1:487490. 6Beyerer,J.,Puente Leon,F.,1997,Suppression of Inhomogeneities in Images of Textured Surfaces,Optical Engineering,36/1:85-93. 7Beyerer,J.,Puente Leon,F.,1998,Adaptive Separation of Random Lines and Background,Op- tical Engineering,37/10:2733-2741. 8Krahe,D.,2000,Zerstorungsfreie Prufung der Tex-tur gehonter und geschliffener Gegenlaufflachen, VDI Verlag,Dusseldorf. 9Beyerer,J.,Krahe,D.,Puente Leon,F.,2001,Characterization of Cylinder Bores,In:Metrology and Properties of Engineering Surfaces,E.Main-sah,J.A.Greenwood,and D.G.Chetwynd(eds.),Kluwer Academic Publishers,Boston,MA. 10Deans,S.R.,1983,The Radon transform and some of its applications,John Wiley 8,Sons,New York. 11 Beyerer,J.,Puente Leon,F.,1997,Detection of Defects in Groove Textures of Honed Surfaces,Int. J.of Machine Tools 8,Manufacture,37/3:371-389. nts珩磨汔缸孔径的评价 摘要 内燃机汽缸孔的珩磨组织在润滑油的消耗量，有害气体排放，以及运转特性方面发挥了重要作用 .为了客观评价珩磨表面，描述表面织构的特征被量化成二维数据 .文章着重于两个关键步骤的数据分析：预处理，其目的是去除不相干的成分和提取感兴趣的信息，和提取特征以保证感兴趣的表面特征能够得到可靠的数值估计，如珩磨角， 沟槽 参数，表面缺陷等，评估结果可以很容易的应用于用户的评价。 关键词：珩磨，表面纹理，自动视觉检测 1、简介： 内燃机气缸孔是用珩磨的方法加工的，经过该加工的表面主要由两个随机在气缸对称轴不 同角度出现的螺旋槽带组成。纹理质量对于气缸的干燥作业性能，石油消费量，有害气体排放，和运行性能是非常重要的。直到目前，专家们仍然依靠基于微观图像的视觉观察来评价珩磨组织。这种方法枯燥，具有很大的主观性，并且耗时。为了得到客观和可重复性的结果，一个自动化的方法检查是必要的。 2 、检查方法 2.1表面数据 有一些不同的方法来衡量的珩磨表面。从表 1中可以看出，传统的方法，机械笔只执行表面轮廓的一维测量。与此相反，灰度图和光学简图提供二维数据在合理的时间。 由于珩磨纹理 的 横向几何特征 只能进行分析 二维数据， 在后面的 讨论中， 我们将集中 分析 这样的 数据。考察的与不同的测量原理相关的特征 也被列入本表。 描述 珩磨纹理 重要特征的信号模型是本文所讨论的评价方法的基础。基于这个模型，可以展示明确的和数学上完整定义的特性，使得组织评估具有重现性和客观性。这种方法不同于许多广泛应用的方法 如依靠神经网络，它往往被视为一个“暗箱” 1 。 特征的选择是基于珩磨图 2 ，和许多专家的意见，并且也在分析轮廓数据的实例中通过增加新的体积参数来拓展该方法。这得出了一系列的可以满足个人用户需求的特征。 2.2珩磨组织性能 图 1显示一些珩磨组织 的性能，在这个基础上来定义特征。最常用的是粗糙度参数，例如那些基于承载比曲线（雅培曲线）的参数 3 ，以及 Ra， Rz和 Rmax。 4 。然而，处理珩磨表面，重要的是要确定一些将横向几何形状量化的特征。通过这种方式，最相关的纹理特性可以被描述，如珩磨角度，材料涂片，断沟， 杂散沟槽 ，洞，外构和薄片，如图1所示。此外，描述沟槽平衡，稳态的存在，凹槽形状，裂缝，转折沟槽，零散标记的特征也需要。 机械铁笔 灰度图像 光学轮廓 测量区域 1-D 2-D 2-D 深度信息 是 不 是 横向几何 信息 不 是 是 覆盖整个表面 非常耗时 尽可能合理努力 非常耗时 计算处理费用 低 高 高 非接触测量 不 是 是 标准化参数 是 不 是 图表 1：表 1 ：比较机械手写设备，灰度图像和光学简图灰铸铁气缸套 nts 2.3自动检测 图 2显示自动检测应用的概述和其在珩磨加工中对质量控制的目的。一个二维或三维传感器提供珩磨表面的数据 g（ x），其中 x = （ x， y） T R 2指横向空间坐标系。灰色块图是传感器数据处理系统的一部分，该系统的输出数据可以用来简单地说明表面质量，还可以在加工工艺发生中断时发出 警报，或可以通过反馈控制器调节珩磨过程，因为珩磨组织包含有关功能和加工过程的信息，不论珩磨后珩磨刷执行或不执行。 以下各节中，我们将集中于自动化检测的两个关键步骤：对传感器数据的预处理和特征提取，我们将针对这些步骤举出一些例子。 图 2 ：自动检测的磨练表面。 3、 预处理 预处理的目的是要抑制无关部分，即不均匀性 i（ x）和外界干扰 b（ x） ，同时增强感兴趣图 1 ：珩磨纹理显示横向特点和缺陷： （ a）材料 涂片 ，沟中断 ; （ b）杂散沟槽 ; （ c）孔或外构的合作 ; （ d）薄片。 nts的信息，比如组织 t（ x）。在图像数据的获取过程中，不均匀性 i（ x）可能是由于表面光洁度的空间差异。其他的产生外界干扰的原因包括偏离理想情况下的沟槽和缺陷 ，比如说材料涂片和薄片等。 我们用一个信号模型 g（ x）来描述传感器数据，包含组织 t（ x）和无关成分 i（ x）和b（ x）： 为了能够替代感兴趣的信息 t（ x），首先要进行以下的假设：不同的成分必须在数学上是可以进行区分的。 如图 3所示，经过严格的分离程序，我们可以得到原始数据的成分。然后，分离出不需要的成分，经过逆变换可以得到预处理的结果。 图 3：预处理的原理 这种处理过程的好处是简化了特征提取的步骤，并且使图像处理过程更稳定，这可以通过以下的例子来说明。 3.1 均质 当凹槽组织被数据采集过程中的非均 匀性强度所降级，如非均匀照明，均质化就可以制止这种有害的组成部分 6 。图 4用这种方法做某一特定组织的例子。图的左边，是原始的形态。中间是用标准的同质化方法 同质滤波，假设非均质和组织结合 得出的结果。特别是在左上角的部分，这张图像的对比度很差。右边的图像是采用了图 3中的模型得出的结果。在这种情况下，对比度和临界值都是均匀的，它基于一种综合考虑了感兴趣的信号和非均
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