Machine tool condition monitoring using workpiece surface texture analysis

Tool wear affects the surface roughness dramatically. There is a very close correspondence between the geometrical features imposed on the tool by wear and micro-fracture and the geometry imparted by the tool on to the workpiece surface. Since a machined surface is the negative replica of the shape of the cutting tool, and reflects the volumetric changes in cutting-edge shape, it is more suitable to analyze the machined surface than look at a certain portion of the cutting tool. This paper discusses our work that analyzes images of workpiece surfaces that have been subjected to machining operations and investigates the correlation between tool wear and quantities characterizing machined surfaces. Our results clearly indicate that tool condition monitoring (the distinction between a sharp, semi-dull, or a dull tool) can be successfully accomplished by analyzing surface image data. Received: 9 June 1998 / Accepted: 6 October 1999     

Connectivity oriented fast Hough transform for tool wear monitoring

Tool wear monitoring can be achieved by analyzing the texture of machined surfaces. In this paper, we present the new connectivity oriented fast Hough transform, which easily detects all line segments in binary edge images of textures of machined surfaces. The features extracted from line segments are found to be highly correlated to the level of tool wear. A multilayer perceptron neural network is applied to estimate the flank wear in various machining processes. Our experiments show that this Hough transform based approach is effective in analyzing the quality of machined surfaces and could be used to monitor tool wear. A performance analysis of our Hough transform is also provided.     

Tool wear monitoring using a fast Hough transform of images of machined surfaces

The texture of machined surfaces provides reliable information regarding the extent of tool wear. In this paper, we propose a structure-based approach to analyzing machined surfaces. The original surface images are first preprocessed by a Canny edge detector. A new connectivity-oriented fast Hough transform is then applied to the edge image to detect all the line segments. The distributions of the orientations and lengths of the line segments are used to determine tool wear. Through our experiments, we found a strong correlation between tool wear and features. The computational complexity of the fast Hough transform is also analyzed.Received: 6 November 2002, Accepted: 18 December 2003, Published online: 13 May 2004 Correspondence to: A.A. Kassim     

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8 mm and 2.5 mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data.     

基于图像连通区域数的刀具磨损状态特征提取实验研究

为了利用计算机视觉技术进行刀具状态监测,设计了机械加工刀具状态监测实验系统,并通过将图像处理技术引入到机械加工刀具磨损状态监测中,提出了一种通过提取工件表面图像的连通区域数来判断刀具磨损状态的新方法。该方法首先采集被加工工件的表面图像;然后对图像进行预处理,并对区域行程算法进行了改进,再用改进的区域行程标记算法对机械加工工件表面图像进行标记;最后通过统计连通区域数来判断刀具的磨损状态。理论和实验分析表明,由于加工工件表面图像的连通区域数和刀具磨损有很强的相关性,其可以间接判断刀具磨损情况,从而可达到对刀具状态进行监测的目的。实验表明,该方法计算简单、识别速度快,可以有效地判断刀具的磨损状态。     

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, \(V_{B}\) into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N, in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power- that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements.     

An approach to in-process surface texture condition monitoring

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.     

机械加工零件表面纹理缺陷检测

在一些对机械加工零件表面的加工精度和表面质量要求较高的自动化工业中，对机械加工零件表面纹理缺陷进行可靠的、有效的检测和分析可以大大地提高生产加工的自动化水平。为了能够对机械加工零件表面进行可靠、有效的检测，根据机械加工零件表面的纹理特点，设计了一种新的图像频域滤波器，用于增强缺陷纹理图像和抑制背景纹理对缺陷纹理检测的干扰，再通过图像分割的方法的实现了缺陷纹理和背景纹理的分割。实验结果显示，这种方法检测速度较快，尤其适用于机械精加工零件表面纹理缺陷的准实时检测。     

磨粒图象纹理分形特征的研究

机械设备在运转过程中,发生各种各样形式的摩擦磨损,其磨损颗粒的数量、尺寸大小都随着运转过程而逐渐增加,在颗粒表面留下了与摩擦磨损机理相关的痕迹。这对磨粒的表面纹理特征进行分析,通过求取特征值,作为判断出机械装置的寿命、故障机理等指标的重要参数。     

Numerical analysis of the tool wear effect in the machining induced residual stresses

Machining is a dynamic process involving coupled phenomena: high strain and strain rate and high temperature. Prediction of machining induced residual stresses is an interesting objective at the manufacturing processes modelling field. Tool wear results in a change of tool geometry affecting thermo-mechanical phenomena and thus has a significant effect on residual stresses. The experimental study of the tool wear influence in residual stresses is difficult due to the need of controlling wear evolution during cutting. Also the involved phenomena make the analysis extremely difficult. On the other hand, Finite Element Analysis (FEA) is a powerful tool used to simulate cutting processes, allowing the analysis of different parameters influent on machining induced residual stresses.The aim of this work is to develop and to validate a numerical model to analyse the tool wear effect in machining induced residual stresses. Main advantages of the model presented in this work are, reduced mesh distortion, the possibility to simulate long length machined surface and time-efficiency. The model was validated with experimental tests carried out with controlled worn geometry generated by electro-discharge machining (EDM). The model was applied to predict machining induced residual stresses in AISI 316 L and reasonable agreement with experimental results were found.     

Tool condition monitoring using reflectance of chip surface and neural network

A wide variety of tool condition monitoring techniques has been introduced in recent years. Among them, tool force monitoring, tool vibration monitoring and tool acoustics emission monitoring are the three most common indirect tool condition monitoring techniques. Using multiple intelligent sensors, these techniques are able to monitor tool condition with varying degrees of success. This paper presents a novel approach for the estimation of tool wear using the reflectance of cutting chip surface and a back propagation neural network. It postulates that the condition of a tool can be determined using the surface finish and color of a cutting chip. A series of experiments has been carried out. The experimental data obtained was used to train the back propagation neural network. Subsequently, the trained neural network was used to perform tool wear prediction. Results show that the prediction is in good agreement with the flank wear measured experimentally.     

Local fractal and multifractal features for volumic texture characterization

For texture analysis, several features such as co-occurrence matrices, Gabor filters and the wavelet transform are used. Recently, fractal geometry appeared to be an effective feature to analyze texture. But it is often restricted to 2D images, while 3D information can be very important especially in medical image processing. Moreover applications are limited to the use of fractal dimension. This study focuses on the benefits of fractal geometry in a classification method based on volumic texture analysis. The proposed methods make use of fractal and multifractal features for a 3D texture analysis of a voxel neighborhood. They are validated with synthetic data before being applied on real images. Their efficiencies are proved by comparison to some other texture features in supervised classification processes (AdaBoost and support vector machine classifiers).The results showed that features based on fractal geometry (by combining fractal and multifractal features) contributed to new texture characterization. Information on new features was useful and complementary for a classification method.This study suggests that fractal geometry can provide a new useful information in 3D texture analysis, especially in medical imaging.     

数控加工中加工误差预测模型的研究

提出了一个在球头端铣加工中预测复杂曲面加工误差的理论模型.在理论模型的基础上,计算出了曲面各个部分的由刀具变形引起的加工误差.对影响加工误差的诸如切削模式、铣削位置角、曲面几何形状等各种切削状况进行了研究.最后,使用加工中心,在各种加工状况下.通过一系列实验对理论模型进行了验证.并利用计算机图形学工具对二者进行了建模仿真,结果显示理论值与实验值非常吻合.恰好证明了趣论模型在预测表面加工误差方面的适应性非常好.     

Constant scallop-height tool path generation for three-axis sculptured surface machining

This paper presents a new approach for the determination of efficient tool paths in the machining of sculptured surfaces using 3-axis ball-end milling. The objective is to keep the scallop height constant across the machined surface such that redundant tool paths are minimized. Unlike most previous studies on constant scallop-height machining, the present work determines the tool paths without resorting to the approximated 2D representations of the 3D cutting geometry. Two offset surfaces of the design surface, the scallop surface and the tool center surface, are employed to successively establish scallop curves on the scallop surface and cutter location tool paths for the design surface. The effectiveness of the present approach is demonstrated through the machining of a typical sculptured surface. The results indicate that constant scallop-height machining achieves the specified machining accuracy with fewer and shorter tool paths than the existing tool path generation approaches.     

Online tool wear prediction system in the turning process using an adaptive neuro-fuzzy inference system

Tool wear is a detrimental factor that affects the quality and tolerance of machined parts. Having an accurate prediction of tool wear is important for machining industries to maintain the machined surface quality and can consequently reduce inspection costs and increase productivity. Online and real-time tool wear prediction is possible due to developments in sensor technology. Recently, various sensors and methods have been proposed for the development of tool wear monitoring systems. In this study, an online tool wear monitoring system was proposed using a strain gauge-type sensor due to its simplicity and low cost. A model, based on the adaptive network-based fuzzy inference system (ANFIS), and a new statistical signal analysis method, the I-kaz method, were used to predict tool wear during a turning process. In order to develop the ANFIS model, the cutting speed, depth of cut, feed rate and I-kaz coefficient from the signals of each turning process were taken as inputs, and the flank wear value for the cutting edge was an output of the model. It was found that the prediction usually accurate if the correlation of coefficients and the average errors were in the range of 0.989–0.995 and 2.30–5.08% respectively for the developed model. The proposed model is efficient and low-cost which can be used in the machining industry for online prediction of the cutting tool wear progression, but the accuracy of the model depends upon the training and testing data.     

基于分形纹理特征和小波变换的网状纹理检测方法

网状纹理广泛存在于日常生活中. 其独特的网格结构以及极易发生形变的特点使网状纹理的描述和检测成为一件困难的工作. 本文以球网检测为例, 提出了基于分形纹理特征和小波变换的网状纹理检测方法. 先对足球视频图像进行小波多分辨率分解, 计算不同尺度图像的分形纹理特征向量, 基于因果关系对特征进行选择组成特征向量. 然后使用支持向量机检测图像中的网状纹理. 实验表明此方法有较强的鲁棒性, 能够在剧烈形变、复杂背景和基元大小差异明显的条件下成功检测出球网.     

Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties

One of the big challenges in machining is replacing the cutting tool at the right time. Carrying on the process with a dull tool may degrade the product quality. However, it may be unnecessary to change the cutting tool if it is still capable of continuing the cutting operation. Both of these cases could increase the production cost. Therefore, an effective tool condition monitoring system may reduce production cost and increase productivity. This paper presents a neural network based sensor fusion model for a tool wear monitoring system in turning operations. A wavelet packet tree approach was used for the analysis of the acquired signals, namely cutting strains in tool holder and motor current, and the extraction of wear-sensitive features. Once a list of possible features had been extracted, the dimension of the input feature space was reduced using principal component analysis. Novel strategies, such as the robustness of the developed ANN models against uncertainty in the input data, and the integration of the monitoring information to an optimization system in order to utilize the progressive tool wear information for selecting the optimum cutting conditions, are proposed and validated in manual turning operations. The approach is simple and flexible enough for online implementation.     

Monitoring of tool wear using measured machining forces and neuro-fuzzy modelling approaches during machining of GFRP composites

The challenges of machining, particularly milling, glass fibre-reinforced polymer (GFRP) composites are their abrasiveness (which lead to excessive tool wear) and susceptible to workpiece damage when improper machining parameters are used. It is imperative that the condition of cutting tool being monitored during the machining process of GFRP composites so as to re-compensating the effect of tool wear on the machined components. Until recently, empirical data on tool wear monitoring of this material during end milling process is still limited in existing literature. Thus, this paper presents the development and evaluation of tool condition monitoring technique using measured machining force data and Adaptive Network-Based Fuzzy Inference Systems during end milling of the GFRP composites. The proposed modelling approaches employ two different data partitioning techniques in improving the predictability of machinability response. Results show that superior predictability of tool wear was observed when using feed force data for both data partitioning techniques. In particular, the ANFIS models were able to match the nonlinear relationship of tool wear and feed force highly effective compared to that of the simple power law of regression trend. This was confirmed through two statistical indices, namely r² and root mean square error (RMSE), performed on training as well as checking datasets.     

基于分形特征的细胞核表面纹理分析

提出了一种改进的基于分块迭代函数系统的分形维估计方法,对骨髓涂片中不同成熟阶段的粒细胞核表面的纹理进行了研究.该方法首先通过压缩仿射变换对细胞核灰度图象进行分块迭代函数系统构建,然后从该系统中提取出反映细胞核分形特征的匹配块因子、尺度系数等参数用于计算分形维.为了避免对细胞核表面分形维的过高估计,就匹配块因子、尺度系数的计算进行了改进.对60幅不同类型的粒细胞核表面图象进行比较实验,其结果表明该方法提取的分形参数可有效地反映不同细胞核表面间的纹理差异,它们可作为细胞识别中的新特征量.     

Feature-filtered fuzzy clustering for condition monitoring of tool wear

Condition monitoring is of vital importance in order to assess the state of tool wear in unattended manufacturing. Various methods have been attempted, and it is considered that fuzzy clustering techniques may provide a realistic solution to the classification of tool wear states. Unlike fuzzy clustering methods used previously, which postulate cutting condition parameters as constants and define clustering centres subjectively, this paper presents a fuzzy clustering method based on filtered features for the monitoring of tool wear under different cutting conditions. The method uses partial factorial experimental design and regression analysis for the determination of coefficients of a filter, then calculates clustering centres for filtering the effect of various cutting conditions, and finally uses a developed mathematical model of membership functions for fuzzy classification. The validity and reliability of the method are experimentally illustrated using a CNC machining centre for milling.