Tool Wear Measurement Research Articles

Precise μEDM-drilling using real-time indirect tool wear compensation

Tool electrode wear in μEDM-drilling is more significant than in conventional EDM-drilling. Due to the longitudinal wear of the electrode, the actual depth of micro-hole does not remain identical with the programmed ‘Z’ axis value. In order to compensate this tool wear, in the present work, a modified ‘volume removal per discharge’ (VRD) approach has been applied in real-time. Instead of measuring tool wear directly, this approach does tool wear compensation by estimating ‘real-time volume removal’ from workpiece. Since VRD doesn’t remain constant with the depth of machining, the proposed VRD approach includes this aspect in the tool wear compensation strategy for estimating ‘real-time volume removal’ more accurately. In addition to the longitudinal wear of the tool electrode, this strategy also considers corner wear and tapering of the tool as well as overcut. For determining VRD more accurately, a pulse discriminating system, developed in-house is used to identify different pulses. The proposed approach has been applied to two different work-tool material combinations and also at two different input machining conditions. The results are compared with the ‘uniform wear method’ (UWM) revealing a noticeable improvement.

A Novel Tool Wear Measurement Method in Ultra-Precision Raster Milling

Tool wear measurement has drawn a significant of attention in the past decades. However, no research has been found on the investigation of tool wear measurement in ultra-precision raster milling (UPRM) process since it is a relative complex cutting process. In the present study, tool wear characteristics were identified by using cutting chip morphologies and a groove cutting. Tool wear investigation using cutting chips is effective because diamond tool wear characteristics can be directly imprinted on the cutting chip surface. Through the inspection of chip surfaces, the profile and location of the tool fracture can be identified. Also, through the groove cutting, the cutting edge retreat due to the tool flank wear can be identified. In this research, a mathematical model was established to calculate the tool retreat. The experimental result shows that the proposed tool wear investigation method is an effective method.

Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis.

切削液の蛍光共焦点検出を利用した工具刃先摩耗形状計測に関する基礎研究

切削液の蛍光共焦点検出を利用した工具刃先摩耗形状計測に関する基礎研究

Online Tool Wear Measurement for Hobbing of Highly Loaded Gears in Geared Turbo Fans

Continuous increase in flight passengers alongside with a high demand for fuel efficiency has led to the development of Geared Turbo Fans (GTF). Being a safety-critical part, the gearbox faces strong safety requirements that also account for sophisticated manufacturing processes and monitoring systems. One major issue is tool wear and the threat of tool breakage during the hobbing process. Due to the high costs of both the raw material and the tool, wear induced tool breakage is a major cost driver. Common practice today is to use each tool for a designated time, but in-situ online wear assessment would result in a major reduction of costs. Tool wear of hobs is not spread equally across all cutting edges; hence the assignment of tool wear to each blade would enable a monitoring system to analyze the individual tool life and predict its operational capability. This research paper discusses the general feasibility of a blade-oriented monitoring system for gear hobbing processes. A correlation between tool wear and effective power is found and analyzed. It can be shown that the respective position of the tool wear changes the effective power signal significantly. These findings enable further research on an online, model based and position-oriented tool wear monitoring system.

An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis

We present a new micro-vision system for tool wear monitoring, which is essential for intelligent manufacturing. The tool wear area is divided into regions by a watershed transform, then subjected to automatic focusing and segmentation. The individual pixel gray values in each region are then replaced with the corresponding regional mean gray value. A hill climbing algorithm based on the sum modified laplacian (SML) focusing evaluation function is used to search the focal plane. In addition, we implement an adaptive Markov Random Field (MRF) algorithm to segment each region of tool wear. For our MRF model, the connection parameter value is adaptively determined by the connection degree between regions, which improves image acquisition of more integral tool wear areas. Our findings suggest that automatic focusing and segmentation of the tool wear area by region (within the tool wear area) enhance accuracy and robustness, and allow for real time acquisition of tool wear images. We also implement a complementary tool wear assessment procedure based on the surface texture of the workpiece. The optimal texture analysis window is determined using the entropy metric – a texture feature generated using a Gray Level Co-occurrence Matrix (GLCM). In the best texture analysis window, entropy remains monotonic as tool wear increases, demonstrating that entropy can be used effectively to monitor tool wear. Information from combined measurements of tool wear and workpiece texture can reliably be used to monitor tool wear conditions and improve monitoring success rates.

Measurement and optimization of surface roughness and tool wear via grey relational analysis, TOPSIS and RSA techniques

Magnesium alloy (Mg alloy) is one among the lightest materials and which has wide applications in the production of aircraft engines, airframes, helicopter components, light trucks, automotive parts and computers parts for its attractive properties. In this paper, a study to analyze the turning properties of magnesium alloy AZ91D in dry condition with polycrystalline diamond (PCD) cutting inserts is presented. Firstly, to investigate turning of magnesium alloy using grey relational analysis and TOPSIS of optimum cutting parameter values. Secondly, to determine using response surface analysis of mathematical model depending on cutting parameters of surface roughness and tool flank wear in turning. The adequacy of the developed mathematical model is proved by ANOVA. The findings from the investigation showed that feed rate and cutting speed are the dominant factors for surface roughness and tool flank wear respectively.

A New Approach to Spatial Tool Wear Analysis and Monitoring

The tool wear of cutting tools has a very strong impact on the product quality as well as on the efficiency of the machining processes. Despite the current high automation level in the machining industry, a few key issues prevent complete automation of the entire turning process. One of these issues is tool wear, which is usually measured off the machine tool. Therefore, its in-line characterization is crucial. This paper presents an innovative, robust and reliable direct measurment procedure for measuring spatial cutting tool wear in-line, using a laser profile sensor. This technique allows for the determination of 3D wear profiles, which is an advantage over the currently used 2D subjective techniques (microscopes, etc.). The use of the proposed measurement system removes the need for manual inspection and minimizes the time used for wear measurement. In this paper, the system is experimentally tested on a case study, with further in-depth analyses of spatial cutting tool wear performed. In addition to tool wear measurements, tool wear modelling and tool life characterization are also performed. Based on this, a new tool life criterion is proposed, which includes the spatial characteristics of the measured tool wear. The results of this work show that novel tool wear and tool life diagnostics yield an objective and robust methodology allowing tool wear progression to be tracked, without interruptions in the machining process or in the performance of the machining process. This work shows that such an automation of tool wear diagnostics, on a machine tool, can positively influence the productivity and quality of the machining process.

On-line detection and measurements of tool wear for precision boring of titanium components

On-line detection and measurements of tool wear is important to assure manufacturing accuracy, enhance manufacturing efficiency, and reduce manufacturing costs. In this research, adaptive neuro-fuzzy inference systems are utilized in conjunction with features extracted from three-axis cutting force data for the on-line detection and measurements of tool wear for precision boring of titanium components. Cutting force data were measured for carbide tools during the boring of titanium parts. At the end of every boring process, the average flank wear width was measured to determine the cutting tool conditions. Measurements were accomplished with the aid of a toolmaker’s microscope. In total, 14 features were obtained from the cutting force data. Euclidean distance measure was utilized to determine which features showed the best indication of cutting tool conditions. This approach can reduce the number of features for on-line detection and measurements of tool wear for precision boring of titanium parts. The selected two most prominent features were kurtosis of longitudinal force and average of the ratio between tangential force and radial force. On-line detection of boring tool wear obtained excellent results, using a 2×2 adaptive neuro-fuzzy inference systems, of being able to predict tool conditions on-line with 100% reliability. On-line measurements of boring tool wear also produced exceedingly successful results with a minimum error for a 1×10 adaptive neuro-fuzzy inference systems of 0.87%.

Adaptive resampling-based particle filtering for tool life prediction

Trending analysis has been widely investigated for prediction of tool wear, which impacts not only tool life but also the quality of machined products. This paper presents a Bayesian approach to predicting the flank wear rate, linking vibration data measured during machining with the state of tool wear. Variations in the measured data are aggregated based on the Kullback–Leibler divergence, which provides a measure for the distance between the current and initial probability distributions of measurement, when no wear is present. Subsequently, state space estimation of the tool wear is realized by particle filtering (PF), a non-linear and non-Gaussian system estimation technique. To overcome the sample impoverishment problem in sequential importance resampling (SIR), a new resampling scheme is proposed, which has shown to more reliably quantify the confidence interval and improve the prediction accuracy of the remaining useful life (RUL) prediction of the tool as compared to standard SIR method. The developed method is experimentally evaluated using a set of benchmark data measured from a high speed CNC machine that performed milling operations. Good results are confirmed by the comparison between the predicted tool wear state and off-line tool wear measurement.

Modelling and prediction of tool wear using LS-SVM in milling operation

This article focuses on the least squares support vector machine (LS-SVM), which can solve highly nonlinear and noisy black-box modelling problems, and tool wear model based on LS-SVM for ball-end milling cutter is established by considering the joint effect of machining conditions. In the established model, machining parameters and position parameter of ball-end cutter are considered as input and the output of the proposed model is tool wear of cutting edge position. The experimental measured tool wear is used to train the established model, and the interconnection relationship between input and output parameters is determined after training. The analysis and comparison of predicted performance are given by taking different tuning parameters and data regularisation. Some interesting analysis results are deduced from the established LS-SVM-based tool wear model. In order to further show the effectiveness of LS-SVM-based tool wear model, the verified comparison between LS-SVM-based and ANN-based model is given. Finally, the discussion of interactional effect of machining parameters on tool wear estimation is used to evaluate prediction performance of LS-SVM-based model. The verification shows that the LS-SVM-based tool wear model is suitable to predict tool wear at certain range of cutting conditions in milling operation.

Structural Changes of Surface Layers of Hard Turned Parts by Wiper and Conventional Geometry

Aims: The aim of this paper is to achieve information about surface and sub-surface layers after hard turning by mixed ceramic tool with different geometry – Wiper and conventional and to compare achieved results to find out advantages of its use. Second aim is to obtain graphs of the influence of the tool wear on the surface structural changes. Study Design: The experiment on hard turning was carried out with differently shaped mixed ceramic tools. Place and Duration of Study: Slovak University of Technology in Bratislava, Faculty of Materials Science and Technology, Institute of Production Technologies and Institute of Materials Science, between November 2013 and May 2014. Methodology: In the case of cutting tool wear and structural changes measurement this experiment was carried out on two different workpiece sizes – 1.125 mm long cylinder to achieve cutting tool wear, 2. 10 mm long rings for structural changes. Microscopy for cutting tool wear measurement and X – ray diffraction for structural changes measurement was used. Cutting tool wear was represented by VB parameter and structural changes were represented by structural Original Research Article Samardziova et al.;BJAST, 6(4): 335-341, 2015; Article no.BJAST.2015.092 336 phase content, crystallites and lattice. Results: Information about structural phase contents was achieved. There are some differences between surface and sub-surface layers after hard turning by mixed ceramic tool. Surface layer consists of more austenite phase than sub-surface layers. But there were not very big differences between used cutting tool geometry and its influence on the cutting tool wear. Both geometries were used more than 40 minutes till the flank wear parameter VB reached to 0.25 mm. The thickness of the surface heat influenced layer depends on the cutting tool wear and it is influenced by the cutting tool geometry – application of the Wiper geometry leads to thinner “white layer”. Conclusion: It is very important to take into consideration cutting tool geometry when dealing with surface integrity after hard turning. But all achieved results depend on the used cutting parameters, cutting tool material, workpiece material, cutting force components, machine stiffness in specific experiments.

Interferometric measurements of single crystal diamond tool wear

Interferometric measurements of plunge cuts made in a reference component intermittently during cutting tool wear tests allow measurement of the detail of tool edge recession without removing the tool from the machine. Hence the evolution of the tool wear can be evaluated with a resolution of less than 5nm. A plunge cut using the new tool is subtracted from subsequent measurements, providing a direct measure of wear and removing bias from the scanning white light interferometer that would arise from non-zero slopes in the field of view. The method is illustrated with results from single crystal diamond tool wear tests when machining a family of nickel-copper alloys of varying composition. The dominant wear mechanism depends on the alloy and the cutting parameters. Nose flattening, leading edge recession, chipping, and formation of Pekelharing grooves can all be identified.

Automatic Multiple Sensor Data Acquisition System in a Real-time Production Environment

This paper presents a multiple sensor automatic data acquisition system deployed on a CNC turning center in a real- time production environment at Schivo Precision, Waterford, Ireland. The machine has been fitted with a variety of sensors measuring acoustic emission, cutting force & vibration installed at the turret of the machine, coupled with an automatic image acquisition system monitoring the wear on the tools in real time after each operation. The combination of sensors and data acquisition is novel in that it brings together all the currently popular sensoring techniques in the field of tool condition monitoring and tests the validity of these techniques in a live production environment. The sensor data acquisition and optical tool wear measurements are controlled by a computer automatically with no intervention from the machine tool operator in normal production on a variety of component geometries, materials and cutting inserts. All the acquired data is available on-line for the research partners in the various countries. Independent operator feedback on the performance of the process in terms of both product dimensional stability and machined surface stability is used for evaluation of the acquired data applicability for tool condition monitoring.

Novel Spatial Cutting Tool-wear Measurement System Development and its Evaluation

The tool-wear of cutting tools has a very strong impact on the product quality as well as efficiency of the machining processes. Despite the nowadays high automation level in machining industry, tool-wear diagnostic that is measured of the machine tool, still prevent complete automation of the entire machining process. Therefore, its in-line characterization is crucial. Thus, the paper presents developed innovative, robust and reliable direct measuring procedure for measuring spatial cutting tool-wear in-line, with the usage of laser profile sensor. The technique provides possibility for determination of 3D wear profiles, as advantage to currently used 2D subjective techniques (microscopes, etc.). The use of proposed measurement system removes the subjective manual inspection and minimizes the time used for wear measurement. In the manuscript the system is experimentally tested on a case study, with further in-depth performed analyses of spatial cutting tool-wear.

The Tool Life of Ball Nose end Mill Depending on the Different Types of Ramping

Abstract The article deals with the cutting tool wear measurement process and tool life of ball nose end mill depending on upward ramping and downward ramping. The aim was to determine and compare the wear (tool life) of ball nose end mill for different types of copy milling operations, as well as to specify particular steps of the measurement process. In addition, we examined and observed cutter contact areas of ball nose end mill with machined material. For tool life test, DMG DMU 85 monoBLOCK 5-axis CNC milling machine was used. In the experiment, cutting speed, feed rate, axial depth of cut and radial depth of cut were not changed. The cutting tool wear was measured on Zoller Genius 3s universal measuring machine. The results show different tool life of ball nose end mills depending on the copy milling strategy.

Evaluation of correlation between vibration signal features and three-dimensional finite element simulations to predict cutting tool wear in turning operation

This article presents the correlation of vibration signal feature, that is, displacement due to vibration (microns) during the machining and three-dimensional finite element simulations in tool wear monitoring of a metal turning operation. Machining of AISI 1040 has been carried on using an uncoated inserts and online vibration signals acquired using a laser Doppler vibrometer. The measured tool wear forms correlate to features in the vibration signals in frequency domains. Analyses of the results suggested that the vibration signals’ features are effective for use in cutting tool wear monitoring and wear qualification. Present work demonstrates the three-dimensional finite element analysis to predict the workpiece displacements in feed direction and corresponding tool wear with the help of induced vibrations in face turning under dry machining conditions. Vibration-assisted turning model is developed and validated by comparing the simulation results with experimental results. In this research, three-dimensional finite element modelling and simulation issues for vibration-assisted turning are explored in detail. Machine dynamic effects are taken into account to predict the outcomes such as tool wear displacements and chip formation. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by correlating displacement variations during the machining. The correlation between the displacement due to vibration and flank wear has been evaluated through three-dimensional finite element modelling and simulation. Comparisons of simulations with experimental results demonstrate their predictive capability. From the results, useful conclusions may be drawn, and it can be stated that the proposed models can be used for industrial application.

High wear performance of the dual-layer graded composite diamond coated cutting tools

In the present experimental study, wear performance of the diamond coated cutting tools with various coating architectures was studied by turning Alm–30%SiCp metal matrix composite material. Chemical vapor deposited (CVD) diamond can be classified into nanocrystalline diamond (NCD) and microcrystalline diamond (MCD) that are known for their distinct characteristics. Diamond coatings with three different coating architectures were deposited for the machining study; (i) mono-layer MCD coating (MCD/WC–Co), (ii) dual-layer composite diamond coating (NCD/MCD/WC–Co) and (iii) dual-layer graded composite diamond coating (NCD/transition-layer/MCD/WC–Co). Wear performance of the diamond coated (3 architectures), uncoated and commercial TiN coated tools was evaluated and compared by conducting high speed machining tests. Superior wear performance of the diamond coated tools was clearly evident from the tool wear measurements. The poor tool life (t<1min) of the uncoated and TiN coated tools was attributed to the abrasive action of the hard SiC reinforcement particles. Dual-layer graded composite diamond coated (t=14.7min) and mono-layer MCD coated (t=13.5min) tools showed superior machining performance in comparison to that of the dual-layer composite diamond coated tool (t=9.8min). Dual-layer graded composite diamond coatings deposited with the concept of transition-layer are the prospective tool coatings for high performance machining applications due to their top-layer nanocrystallinity and enhanced interfacial integrity.

Analysis of Tool Wear Behavior of Single Crystal Diamond Based on FEM Simulation in Micro Turning

Micro turning test was performed on nickel plated roll die using ultra precision lathe and lenticular shape single crystal diamond (SCD) tools. For the test, fresh tools were used for each experiment to observe tool wear evolution at the cutting distances. Finite element method (FEM) simulation based on Lagrangian method was also used to calculate contact stress on the cutting surface during the machining process. For the tool wear measurement, scanning electron microscope (SEM) and 3D Nano View, which can provide surface topography of the tools, were used. In addition, Kistler dynamometer was utilized for cutting force measurement. From the experimental result, it was found that a dominant wear mechanism was abrasion due to high contact stresses on the cutting tool surface. And it was also observed that high cutting speed caused high wear rate but slighlty reduced the cutting forces.

Tool wear model based on least squares support vector machines and Kalman filter

In this study, the least squares support vector machines (LS-SVM) and Kalman filter (KF) technique are used to establish the tool wear estimation model. Tool wear prediction model, based on LS-SVM, is given to describe the mapping relationship between input–output factors. The cutting conditions (feed rate, cutting speed, and depth of cut), cutting time, and wear position constitute the input factors and tool wear is the output parameter of the model. In order to improve the accuracy of the LS-SVM results, the KF technique is used to update the tool wear estimated results of LS-SVM-based model, which is called the LS-KF model, according to the measured tool wear values. Experiment work is performed on machining center for cemented carbide ball-end cutter cutting stainless steel. Those two models (LS-SVM model and LS-KF model) are applied to the actual milling machining to verify their performance. Results show that predicted tool wear based on the proposed LS-KF model has more precision than that of LS-SVM model.