In-cycle monitoring of tool nose wear and surface roughness of turned parts using machine vision

Tool wear has been extensively studied in the past due to its effect on the surface quality of the finished product. Vision-based systems using a CCD camera are increasingly being used for measurement of tool wear due to their numerous advantages compared to indirect methods. Most research into tool wear monitoring using vision systems focusses on off-line measurement of wear. The effect of wear on surface roughness of the workpiece is also studied by measuring the roughness off-line using mechanical stylus methods. In this work, a vision system using a CCD camera and backlight was developed to measure the surface roughness of the turned part without removing it from the machine in-between cutting processes, i.e. in-cycle. An algorithm developed in previous work was used to automatically correct tool misalignment using the images and measure the nose wear area. The surface roughness of turned parts measured using the machine vision system was verified using the mechanical stylus method. The nose wear was measured for different feed rates and its effect on the surface roughness of the turned part was studied. The results showed that surface roughness initially decreased as the machining time of the tool increased due to increasing nose wear and then increased when notch wear occurred.     

Noncontact roughness measurement of turned parts using machine vision

The surface roughness of turned parts is usually measured using the conventional stylus type instruments. These instruments, although widely accepted, have several limitations such as low speed measurement, contacting in nature, requiring vibration-free environment, etc. Machine vision methods of roughness measurement are being developed worldwide due to their inherent advantages, including noncontact measurement, high information content, rapid measurement, and surface measurement capability. In past research, area-based light scattering method and gray scale line intensity measurement have been developed for roughness assessment using machine vision. Such methods, however, produced redundant data when applied to measure roughness of turned parts. In this paper, an alternative method of roughness measurement using the 2-D profile extracted from an edge image of the workpiece surface is proposed. Comparison with a stylus type instrument shows a maximum difference of 10% in the measurement of average roughness R _a using the vision method.     

Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform

This paper presents a system for automated, non-contact, and flexible prediction of surface roughness of end-milled parts through a machine vision system which is integrated with an artificial neural network (ANN). The images of milled surface grabbed by the machine vision system could be extracted using the algorithm developed in this work, in the spatial frequency domain using a two-dimensional Fourier transform to get the features of image texture (major peak frequency F ₁, principal component magnitude squared value F ₂, and the average gray level G _a). Since F1 is the distance between the major peak and the origin, it is a robust measure to overcome the effect of lighting of the environment. The periodically occurring features such as feed marks and tool marks present in the gray-level image can be easily observed from the principal component magnitude squared value F _2. The experimental machining variables speed S, feedrate F, depth of cut D, and the response extracted image variables F ₁, F ₂, and G _a could be used as input data, and the response surface roughness R _a measured by Surfcorder SE-1100 (traditional stylus method) could be used as output data of an ANN ability to construct the relationships between input and output variables. The ANN was trained using the back-propagation algorithm developed in this work due to its superior strength in pattern recognition and reasonable speed. Using the trained ANN, the experimental result had shown that the surface roughness of milled parts predicted by machine vision system over a wide range of machining conditions could be got with a reasonable accuracy compared with those measured by traditional stylus method. Compared with the stylus method, the constructed machine vision system is a useful method for prediction of the surface roughness faster, with a lower price, and lower environment noise in manufacturing process. Experimental results have shown that the proposed machine vision system can be implemented for automated prediction of surface roughness with accuracy of 97.53%. The results are encouraging that machine vision system can be extended to many real-time industrial prediction applications.     

车削工件表面粗糙度在机视觉测量装置的设计

为了解决传统触针式测量粗糙度仪器的不足,设计了一种车削工件表面粗糙度的在机测量装置,通过一套装夹机构,实现了视觉系统与机床的结合.为了获得高品质的图像,进行了光照实验,结果表明,采用红光60°角照射比白色背景光效果更好.此视觉测量装置,将为最终实现数控车床工件表面粗糙度在线监测,提供有效的手段.     

Laser optical roughness measurement

A method for non-contacting profile assessment and roughness measurements of engineering surfaces is presented. The well known effect of focusing a light beam on the surface is used, at the same time compensating for the effect on the measuring results of varying material colours by appropriately processing the measuring signal. The sensor and the transducer are connected with the data-processing unit of a stylus instrument, which provides for a simple procedure of recording the roughness profile and calculating standard roughness parameters from it. The measuring results thus obtained agree well with the results of traditional stylus instruments.     

Assessment of surface roughness based on super resolution reconstruction algorithm

In this work, an attempt has been made to use a super resolution image processing algorithm for preprocessing the images over and above existing image quality enhancement techniques. The improved quality images processed using a machine vision system have been used to assess the quality of the surfaces. To ensure the validity of the approach the roughness values quantified using these images are then compared with widely accepted standard mechanical stylus instrument values. Quantification of digital images for surface roughness is performed using two Fourier transform parameters (major peak frequency and principal component magnitude squared value) and the standard deviation of gray level intensity values. Then the group method of data handling (GMDH) technique was used to obtain an analytical relationship of the roughness parameters calculated using the digital surface image and the stylus instrument values. We present in this paper an analysis based on the comparison to make sure that the present approach of estimation of surface finish based on the digital processed image could be implemented in practice.     

电池壳体内曲面表面粗糙度测量系统

介绍了电池壳体内曲面表面粗糙度测量系统的原理、结构、关键设计技术、评定参数等。该仪器基于M制轮廓触针扫描测量原理 ,设计了特殊结构的高灵敏度触针式压电传感器和极低自振量的旋转式电动驱动机构 ,建立了自定义参数及量值传递系统     

Measurement of cylindrical form errors using a non-contact detector

Measurement using a spherical tip stylus cannot give a correct assessment of cylindrical form errors of finely turned parts due to their cutting tool marks. The electrostatic-capacitance type gap sensor applied in this research is useful in eliminating the influence of cutting tool marks, which should be regarded as the surface roughness. The sensor does not damage the cylinder surface, as a stylus might. Further, two measuring methods, a round tracing method and a spiral one, were compared     

Surface roughness classification using image processing

Surface roughness is an important factor in determining the satisfactory functioning of the machined components. Conventionally the surface roughness measurement is done with a stylus instrument. Since this measurement process is intrusive and is of contact type, it is not suitable for online measurements. There is a growing need for a reliable, online and non-contact method for surface measurements. Over the last few years, advances in image processing techniques have provided a basis for developing image-based surface roughness measuring techniques. Based upon the vision system, novel methods used for human identification in biometrics are used in the present work for characterization of machined surfaces. The Euclidean and Hamming distances of the surface images are used for surface recognition. Using a CCD camera and polychromatic light source, low-incident-angle images of machined surfaces with different surface roughness values were captured. A signal vector was generated from image pixel intensity and was processed using MATLAB software. A database of reference images with known surface roughness values was established. The Euclidean and Hamming distances between any new test surface and the reference images in the database were used to predict the surface roughness of the test surface.     

Effect of a skid on the accuracy of measuring surface roughness

The vertical movement of the skid caused by surface irregularities introduces some error into the measured value of surface roughness parameters. A theoretical and experimental investigation into the negative effect of the skid on measuring accuracy is reported.The effect of the skid for unidirectional surface roughness is greater than that for non-directional surface roughness. The effect increases with increase in the tip radius of the skid. The influence of the distance between the skid and the stylus on the effect of the skid is small and an electrical high pass filter reduces the effect.     

Roughness measurement with a laser scanning analyser

In the laser scanning analyser a laser beam is reflected from a rotating polygonal mirror so as to scan the workpiece surface at high speed, where it is again reflected into a photodetector which measures the reflected intensity. If the surface is rough, in addition to the specular reflectance the diffuse reflectance due to diffraction is measured as a function of the off-specular angle. In effect the system when used in this mode is a glossmeter which measures the optical properties of a section through the surface and is thus highly suitable for comparison with a stylus instrument. Experiments are described in which reflectance measurements on surfaces finished by various machining processes are compared with stylus roughness measurements. It is found that reflectance measurements are sensitive both to roughness and to the orientation of the lay. Reflectance measurements are found to vary as the average roughness and as the fourth power of the profile slope. There is no correlation with curvature and no evidence of the effect of longer surface wavelengths. There is some evidence that two different scattering mechanisms are involved. Possible applications of such a real-time non-contacting roughness measuring system are described.     

磨合试验及磨合吸引子初探

为了表征磨合过程和磨合状态,掌握磨合过程中表面形貌的变化规律,在销-盘式试验机上对45#钢摩擦副进行了润滑条件下的磨合试验,用T1000型表面粗糙度测量仪和计算机数据采集系统测量磨合过程中的表面形貌和粗糙度值,并用分形方法对表面形貌进行分形表征和分形参数计算。研究表明:随着磨合过程的进行,摩擦副表面的粗糙度和分形参数逐渐变化,当达到磨合状态时,2个表面具有互适匹配的稳态参数值。因而从非线性科学原理出发,将磨合过程当作摩擦学系统的自组织过程,提出磨合吸引子的概念,并进行了初步的探讨。     

An improved surface roughness measurement method for micro-heterogeneous texture in deep hole based on gray-level co-occurrence matrix and support vector machine

Microscopic vision system has been employed to measure the surface roughness of micro-heterogeneous texture in deep hole, by virtue of frequency domain features of microscopic image and back-propagation artificial neural network optimized by genetic algorithm. However, the measurement accuracy needs to be improved for engineering application. In this paper, we propose an improved method based on microscopic vision to detect the surface roughness of R-surface in the valve. Firstly, the measurement system for the roughness of R-surface in deep hole is described. Thereafter, the surface topography images of R-surface are analyzed by the gray-level co-occurrence matrix (GLCM) method, and several features of microscopic image, which are nearly monotonic with the surface roughness, are extracted to fabricate the prediction model of the roughness of R-surface accurately. Moreover, a support vector machine (SVM) model is presented to describe the relationship of GLCM features and the actual surface roughness. Finally, experiments on measuring the surface roughness are conducted, and the experimental results indicate that the GLCM-SVM model exhibits higher accuracy and generalization ability for evaluating the microcosmic surface roughness of micro-heterogeneous texture in deep hole.     

Reliability of roughness measurements using contact stylus instruments with particular reference to results of recent research at the Physikalisch-Technische bundesanstalt

Even today international comparison measurements of surface roughness show differences of 40% and more, as they did more than 10 years ago. However, within the framework of the Deutscher Kalibrierdienst, differences occurring in comparison measurements do not exceed 5%. At the end of 1982, a project of the European Communities was started for the purpose of also carrying out comparison measurements yielding small differences. The prerequisites are the following: clearly defined surface roughness parameters including details of the measurement conditions required and calibration and testing of the contact stylus instruments before the comparison measurements are made.In recent years the contacting process, the interaction between stylus and surface, has been the subject of very thorough investigations at the Physikalisch-Technische Bundesanstalt. Elastic and plastic deformations of the surface were examined. The parameters of influence are the geometry of the stylus tip and of the surface under examination, the properties of the materials, the static and dynamic measuring forces and the rate of feed. Calculation methods for optimizing these parameters are stated in order to keep the resulting measurement errors as small as possible. A new contactless measuring method is described which is based on the evaluation of scanning electron microscopy stereo pairs. An error analysis makes precision measurements possible which up to now could not be carried out at all. Examples examined have shown that sometimes, particularly on finely ground surfaces, measurements with contact stylus instruments on surfaces with R_z values below 1 μm can be very significantly in error.     

Spectral analysis for the effect of stylus tip curvature on measuring rough profiles

Spectral analysis and computer simulation are adopted to investigate the tracing of a random profile with a mechanical profiler with a spherical tipped stylus. It is shown that the measuring error strongly depends on the ratio of the radius of stylus tip r to the rms roughness σ and the ratio of the 1/e correlation distance b to the rms roughness σ. By using the concept of critical wave number, spectral density of measured profile can be estimated. Also, formulas are developed to estimate the rms error and the critical sampling interval.     

ACHIEVING THRESHOLD BARRIER OF 1 nm ROUGHNESS VALUE OF SILICON SURFACE BY DIAMOND TURNING

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Comparison of optical and stylus methods for measurement of surface texture

Optical methods are increasingly used for measurement of surface texture, particularly for areal measurements where the optical methods are generally faster. A new Working Group under Technical Committee (TC) 213 in the International Organization for Standardization is addressing standardization issues for areal surface texture measurement and characterization and has formed a project team to address issues posed by the optical methods. In this paper, we review the different methods of measuring surface texture and describe a classification scheme for them. We highlight optical methods and describe some of their characteristics as well as compare surface-profiling results obtained from three optical methods with those obtained from stylus profiler instruments. For moderately rough surfaces (Ra?≈?500 nm), roughness measurements obtained with white light interferometric (WLI) microscopy, confocal microscopy, and the stylus method seem to provide close agreement on the same roughness samples. For surface roughness measurements in the 50 to 300 nm range of Ra, discrepancies between WLI and the stylus method are observed. In some cases the discrepancy is as large as about 75% of the value obtained with the stylus method. By contrast, the results for phase shifting interferometry over its expected range of application are in moderately good agreement with those of the stylus method.     

The effect of tip size on the measured Ra of surface roughness specimens with rectangular profiles

When measuring rectangular and trapezoidal profile roughness specimens, the stylus tip increases the measured profile peak width and decreases the measured valley width. This can cause either an increase or a decrease in the apparent roughness average Ra, depending on the tip size and the ratio of peak width to valley width. Sometimes the change is larger than the combined measurement uncertainty from other sources. This raises the question as to whether measured surface parameters should be corrected for the effect of tip size.     

Restoration of blurred images for surface roughness evaluation using machine vision

An attempt is made to evaluate the surface roughness of uniformly moving machined surface (grinding, milling) using machine vision technique. In the case of moving surfaces the images are likely to blur due to the relative motion between the CCD camera and the object to be captured. Hence the degraded image has to be restored by removing distortion due to motion before subsequent analysis. In this work, image blur due to motion is considered, in particular, blur that occurs when the motion is uniform at constant speed and in a fixed direction. The blurred image is modeled as a convolution between the original image and a known point spread function. The Richardson–Lucy Restoration algorithm, a method of estimation based on Bayes theorem has been used to correct the image. The algorithm is tested in simulations and in practical experiments. A simulation gives complete control over the setup and enables to test the performance of the algorithm. The quantification of roughness for restored images are performed using the statistical parameters such as spatial frequency, arithmetic average of gray level and standard deviation after pre-processing. An Artificial Neural Network (ANN) was used with these three statistical parameters as input to predict the vision roughness. Finally, vision roughness values calculated using the deblurred images are compared with the stylus roughness value. An analysis based on the comparison to understand the validity of the present approach of estimation of surface roughness based on the digitally processed images for implementation in practice, is presented in this paper.     

Application of the lifting wavelet to rough surfaces

This paper proposes a lifting wavelet model for enhancement of accuracy of surface roughness characterisation. In this work, the theory and fast algorithm of the lifting wavelet are briefly introduced and a lifting wavelet model for extraction of roughness of surfaces has been developed. The rough surface recovered has good refinement accuracy in contrast to the least squares polynomial fitting. Applications are conducted by using a series of typical surfaces, planes and curves, measured by contact (stylus) and non-contact (phase-shifting interferometry) instruments, to demonstrate the feasibility and applicability of using the lifting wavelet model in the analysis of these surfaces.