Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures

The texture of a machined surface generated by a cutting tool, with geometrically well-defined cutting edges, carries essential information regarding the extent of tool wear. There is a strong relationship between the degree of wear of the cutting tool and the geometry imparted by the tool on to the workpiece surface. The monitoring of a tool’s condition in production environments can easily be accomplished by analyzing the surface texture and how it is altered by a cutting edge experiencing progressive wear and micro-fractures. This paper discusses our work which involves fractal analysis of the texture of surfaces that have been subjected to machining operations. Two characteristics of the texture, high directionality and self-affinity, are dealt with by extracting the fractal features from images of surfaces machined with tools with different levels of tool wear. The Hidden Markov Model is used to classify the various states of tool wear. In this paper, we show that fractal features are closely related to tool condition and HMM-based analysis provides reliable means of tool condition prediction.     

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.     

Automatic high-resolution optoelectronic photogrammetric 3D surface geometry acquisition system

A fast, high-resolution, automatic, non-contact 3D surface geometry measuring system using a photogrammetric optoelectronic technique based on lateral-photoeffect diode detectors has been developed. Designed for the acquisition of surface geometries such as machined surfaces, biological surfaces, and deformed parts, the system can be used in design, manufacturing, inspection, and range finding. A laser beam is focused and scanned onto the surface of the object to be measured. Two cameras in stereo positions capture the reflected light from the surface at 10 kHz. Photogrammetric triangulation quickly transforms the pair of 2D signals created by the camera detectors into 3D coordinates of the light spot. Because only one small spot on the object is illuminated at a time, the stereo correspondence problem is solved in real time. The resolution is determined by a 12-bit A/D converter and can be improved up to 25 60025 600 by oversampling. The irregular 3D data can be regularized for use with image-based algorithms. Received: 8 October 1996 / Accepted: 3 February 1997     

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     

Volume-based three-dimensional metamorphosis using sphere-guided region correspondence

surface metamorphosis extends this paradigm by interpolating between discrete volume representations of the surfaces. The insensitivity of the established techniques to the surface topology enables morphing between completely different surfaces: however it can also lead to intermediate surfaces which have different topology from the originals. We present a method which improves on this situation by ensuring that no part of each surface remains disconnected during the morph. The morph is guided by region correspondence, derived automatically from a sphere representation of each surface: this can be combined with manual correspondence to retain user control over the morph. What emerges is a fast and flexible method for morphing surfaces, as demonstrated on several examples. Published online: 19 July 2001     

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.     

The normalform of a space curve and its application to surface design

x )=0 with ∥▿h∥=1. The normalform function h is (unlike the latter cases) not differentiable at curve points. Despite of this disadvantage the normalform is a suitable tool for designing surfaces which can be treated as common implicit surfaces. Many examples (bisector surfaces, constant distance sum/product surfaces, metamorphoses, blending surfaces, smooth approximation surfaces) demonstrate applications of the normalform to surface design. Published online: 25 July 2001     

THRED: a two-handed design system

This paper describes a computer-aided design system for sketching free-form polygonal surfaces such as terrains and other natural objects. The user manipulates two 3D position and orientation trackers with three buttons, one for each hand. Each hand has a distinct role to play, with the dominant hand being responsible for picking and manipulation, and the less dominant hand being responsible for context setting of various kinds. The less dominant hand holds the workpiece, sets which refinement level that can be picked by the dominant hand, sets the constraint mode and the reshape operator, and generally acts as a counterpoint to the dominant hand. In this paper, the architecture of the system is outlined, the interaction techniques are presented, and a simple surface is shown.     

Parametric G n blending of curves and surfaces

We introduce a simple blending method for parametric curves and surfaces that produces families of parametrically defined, G ⁿ –continuous blending curves and surfaces. The method depends essentially on the parameterizations of the curves/surfaces to be blended. Hence, the flexibility of the method relies on the existence of suitable parameter transformations of the given curves/surfaces. The feasibility of the blending method is shown by several examples. The shape of the blend curve/surface can be changed in a predictable way with the aid of two design parameters (thumb weight and balance).     

Practical volumetric sculpting

sculpture metaphor for rapid shape prototyping. The sculpted shape is the isosurface of a scalar field spatially sampled. The user can deposit material wherever he desires in space and then iteratively refine it, using a tool to add, remove, paint, or smooth some material. We allow the use of free-form tools that can be designed inside the application. We also propose a technique to mimic local deformations so that we can use the tool as a stamp to make imprints on an existing shape. We focus on the rendering quality too, exploiting lighting variations and environment textures that simulate good-quality highlights on the surface. Both greatly enhance the shape estimation, which is a crucial step in this iterative design process, in our opinion. The use of stereo also greatly eases the understanding of spatial relationships. Our current implementation is based on GLUT and can run the application both on Unix-based systems, such as Irix and Linux, and on Windows systems. We obtain interactive response times, strongly related to the size of the tool. The performance issues and limitations are discussed.     

Condition monitoring of CNC machining using adaptive control

In this work, an adaptive control constraint system has been developed for computer numerical control (CNC) turning based on the feedback control and adaptive control/self-tuning control. In an adaptive controlled system, the signals from the online measurement have to be processed and fed back to the machine tool controller to adjust the cutting parameters so that the machining can be stopped once a certain threshold is crossed. The main focus of the present work is to develop a reliable adaptive control system, and the objective of the control system is to control the cutting parameters and maintain the displacement and tool flank wear under constraint valves for a particular workpiece and tool combination as per ISO standard. Using Matlab Simulink, the digital adaption of the cutting parameters for experiment has confirmed the efficiency of the adaptively controlled condition monitoring system, which is reflected in different machining processes at varying machining conditions. This work describes the state of the art of the adaptive control constraint (ACC) machining systems for turning. AISI4140 steel of 150 BHN hardness is used as the workpiece material, and carbide inserts are used as cutting tool material throughout the experiment. With the developed approach, it is possible to predict the tool condition pretty accurately, if the feed and surface roughness are measured at identical conditions. As part of the present research work, the relationship between displacement due to vibration, cutting force, flank wear, and surface roughness has been examined.     

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.     

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。     

Simulation of micro-patterns engraved by grinding process with screw shaped wheel

Grinding is an important means of realizing precision and ultra-precision machining of workpiece surface. The surface patterning of workpiece directly affects its mechanical properties such as friction, wear, contact stiffness, fitting property. Therefore, prediction of the geometry of the workpiece surface is very important to evaluate the workpiece quality to perform mechanical function accurately. In this paper using MATLAB simulation, prediction for the geometry pattern of the workpiece according to the developed shape of the grinding wheel dressed by thread cutting was studied. The model for the geometry of the grinding wheel surface was first developed and subsequently, a new simulation model for surface pattern by grinding process was established. The simulation results could be used to optimize the grinding process and to improve the workpiece surface quality or predict the surface pattern by given grinding parameters.     

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.     

Reparameterization of piecewise rational Bezier curves and its applications

degree . Although the curve segments are C ¹ continuous in three dimensions, they may be C ⁰ continuous in four dimensions. In this case, the multiplicity of each interior knot cannot be reduced and the B-spline basis function becomes C ⁰ continuous. Using a surface generation method, such as skinning these kinds of rational B-spline curves to construct an interpolatory surface, may generate surfaces with C ⁰ continuity. This paper presents a reparameterization method for reducing the multiplicity of each interior knot to make the curve segments C ¹ continuous in four dimensions. The reparameterized rational B-spline curve has the same shape and degree as before and also has a standard form. Some applications in skinned surface and ruled surface generation based on the reparameterized curves are shown. Published online: 19 July 2001     

Minimal area for surface reconstruction from cross sections

Surface reconstruction from cross-sectional data is important in a variety of applications. It is usually possible to generate a surface in many ways, but only reasonable ones are acceptable. A surface of minimal area has been considered as one of the most natural optimal criteria for the original tiling method of surface reconstruction from cross sections. In the paper, we consider minimal surfaces for continuous generalization of the tiling approach and in the general situation of reconstruction from cross sections. We show that in these cases the minimal area criterion leads to defective surfaces and is thus unacceptable. Published online: 23 July 2002 Correspondence to: D. Berzin