Showing papers on "Tool wear published in 2002"
TL;DR: A review of acoustic emission (AE)-based tool wear monitoring in turning is presented in this article, where the main contents included: 1. AE generation in metal cutting processes, AE signal classification, and AE signal correction, and 2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc.
Abstract: Research during the past several years has established the effectiveness of acoustic emission (AE)-based sensing methodologies for machine condition analysis and process monitoring. AE has been proposed and evaluated for a variety of sensing tasks as well as for use as a technique for quantitative studies of manufacturing processes. This paper reviews briefly the research on AE sensing of tool wear condition in turning. The main contents included are: 1. The AE generation in metal cutting processes, AE signal classification, and AE signal correction. 2. AE signal processing with various methodologies, including time series analysis, FFT, wavelet transform, etc. 3. Estimation of tool wear condition, including pattern classification, GMDH methodology, fuzzy classifier, neural network, and sensor and data fusion. A review of AE-based tool wear monitoring in turning is an important step for improving and developing new tool wear monitoring methodology.
456 citations
TL;DR: In this paper, the authors compared 138 publications dealing with on-line and indirect tool wear monitoring in turning by means of artificial neural networks and compared the methods applied in these publications as well as the methodologies used to select certain methods, to carry out simulation experiments, to evaluate and to present results.
397 citations
TL;DR: In this article, the authors present a summary of the monitoring methods, signal analysis and diagnostic techniques for tool wear and failure monitoring in drilling that have been tested and reported in the literature.
Abstract: This paper presents a summary of the monitoring methods, signal analysis and diagnostic techniques for tool wear and failure monitoring in drilling that have been tested and reported in the literature. The paper covers only indirect monitoring methods such as force, vibration and current measurements, i.e. direct monitoring methods based on dimensional measurement etc. are not included. Signal analysis techniques cover all the methods that have been used with indirect measurements including e.g. statistical parameters and Fast Fourier and Wavelet Transform. Only a limited number of automatic diagnostic tools have been developed for diagnosis of the condition of the tool in drilling. All of these rather diverse approaches that have been available are covered in this study. In the reported material there are both success stories and also those that have not been so successful. Only in a few of the papers have attempts been made to compare the chosen approach with other methods. Many of the papers only present one approach and unfortunately quite often the test material of the study is limited especially in what comes to the cutting process parameter variation, i.e. variation of cutting speed, feed rate, drill diameter and material and also workpiece material.
309 citations
TL;DR: In this article, the authors compared the performance of uncoated and diamond-coated carbide tools, using minimal lubrication and abundant soluble oil as a refrigerant/lubricant in the drilling of aluminum-silicon alloys.
250 citations
TL;DR: In this paper, different carbide drill designs with improved geometries and coatings were investigated and compared by characterizing the cutting forces, tool wear, hole quality, and chip formation.
249 citations
TL;DR: In this paper, a machining experiment with hardened AISI D2 cold work tool steel (∼58-HRC) using indexable insert ball nose end end mill employing carbide and cermet tools, and solid carbide ball n-end mill employing CNCs was presented.
164 citations
TL;DR: In this article, the effects of vibration on the flank wear both in the direction of main cutting force and radial cutting force have been investigated and the results show that the tool life of the alumina ceramic inserts is not satisfactory when machining nodular cast iron.
155 citations
TL;DR: In this article, a series of tool life experiments has been carried out using various coated carbides and ceramics by means of rapid face turning without coolant, and the results show that PVD-coated carbides KC7310 are more suitable for cutting Inconel 718 than CVD-covered carbidesKC935, and ceramic inserts of KY2000 with negative rake angle and KY2100 of round type are the best choice for the high speed turning of Inconels 718.
151 citations
TL;DR: In this article, the performance and wear behavior of different cubic boron nitride (CBN) tools in finish turning of hardened AISI 52100 steel was investigated based on the part surface finish and the tool flank wear.
145 citations
TL;DR: In this article, a diffusion wear model for high speed cutting processes is proposed, where diffusion at the tool-chip interface is controlled by the contact temperature and the cutting conditions are analyzed in terms of cutting conditions (cutting velocity, feed, rake angle), friction characteristics and material properties.
142 citations
TL;DR: A comparison of usability of these methods in practice is discussed, including a new artificial neural network based-fuzzy inference system with moving consequents in if–then rules.
TL;DR: In this paper, a robotic grinding and polishing system was used to automate the manual operation of turbine-vane overhaul and a passive compliance tool combined with an adaptive path planning approach was adopted to overcome intrinsic problems arising from part-to-part geometry variations.
TL;DR: On-line estimation of tool wear is used for combining anticipated compensation with real-time compensation and extends the scope of milling EDM to the machining of blanks of which the exact shape is not known in advance.
TL;DR: In this article, a tool-wear monitoring procedure in a metal turning operation using vibration features was described, and the measured toolwear forms were correlated to features in the vibration signals in the time and frequency domains.
Abstract: This paper describes a tool-wear monitoring procedure in a metal turning operation using vibration features. Machining of EN24 was carried out using coated grooved inserts, and on-line vibration signals were obtained. The measured tool-wear forms were correlated to features in the vibration signals in the time and frequency domains. Analysis of the results suggested that the vibration signals' features were effective for use in cutting tool-wear monitoring and wear qualification.
TL;DR: In this article, a new generation of driven rotary lathe tool was developed for high-speed machining of a titanium alloy, Ti-6Al-4V. The rotary tool was designed and fabricated based on the requirements of compact structure, sufficient stiffness and minimal edge runout.
Abstract: Machining of titanium at high cutting speeds such as from 4 m/s to 8 m/s is very challenging. In this paper, a new generation of driven rotary lathe tool was developed for high-speed machining of a titanium alloy, Ti–6Al–4V. The rotary tool was designed and fabricated based on the requirements of compact structure, sufficient stiffness and minimal edge runout. Cylindrical turning experiments were conducted using the driven rotary tool (DRT) and a stationary cutting tool with the same insert, for comparison in the high-speed machining of Ti–6Al–4V. The results showed that the DRT can significantly increase tool life. Increase in tool life of more than 60 times was achieved under certain conditions. The effects of the rotational speed of the insert were also investigated experimentally. Cutting forces were found to decline slightly with increase of the rotational speed. Tool wear appears to increase with the rotational speed in a certain speed range.
TL;DR: In this article, the same authors used high-speed steel, carbide-tipped, and polycrystalline diamond (PCD) drills to produce holes in 10 and 20 vol.% (Al2O3)p/6061.
TL;DR: In this paper, a three-dimensional mechanistic frequency domain chatter model for face turning processes is presented to account for the effects of tool wear including process damping, which can be used to determine stability boundaries under various cutting conditions and different states of flank wear.
Abstract: This paper presents a three-dimensional mechanistic frequency domain chatter model for face turning processes, that can account for the effects of tool wear including process damping. New formulations are presented to model the variation in process damping forces along nonlinear tool geometries such as the nose radius. The underlying dynamic force model simulates the variation in the chip cross-sectional area by accounting for the displacements in the axial and radial directions. The model can be used to determine stability boundaries under various cutting conditions and different states of flank wear. Experimental results for different amounts of wear are provided as a validation for the model.
TL;DR: In this paper, the machinability of Al2O3 particle-reinforced aluminium alloy composite composite was investigated with special attention paid to the effects of material structures and tool life increased with increasing the cutting speed for both cutting tools.
TL;DR: In this article, the role of cryogenic cooling by liquid nitrogen jet on average chip-tool interface temperature, tool wear, dimensional accuracy and surface finish in turning AISI 4140 steel under industrial speed-feed conditions was investigated.
TL;DR: In this paper, the wear performance of PCBN tool, ceramic tool, coated carbide tool and fine-grained carbide tools in high-speed face milling is presented when cutting cast iron, 45# tempered carbon steel and 45# hardened carbon steel.
TL;DR: In this article, the change in tribological conditions at the tool-chip interface as a function of cutting speed is discussed and the role of coatings in suppressing dissolution wear is demonstrated in both ductile iron and low carbon steel.
Abstract: The tribological phenomenon at the tool–chip interface controls chip formation and tool wear. As metal cutting speed increases from low to moderate speeds, the tribological conditions at the tool–chip interface tend to change from sliding to seizure, giving rise progressively to partially segmented chips. Once seizure sets in, thermoplastic shear occurs in the secondary shear zone, raising the local temperature at the tool–chip contact. Thermally activated processes set in. Dissolution of the tool into the chip takes place by a diffusion mechanism, causing tool crater wear. The maximum depth of the crater is located at some distance away from the cutting edge of the tool. At higher cutting speeds chip segmentation is caused by thermoplastic shear localisation at the primary shear zone causing high temperature rise and rapid tool wear close to the cutting edge of the tool. The change in tribological phenomenon at the tool–chip interface as a function of cutting speed is discussed. Quantitative data are reported to show the onset of seizure in metal cutting and the consequences of seizure in promoting dissolution wear by diffusion mechanism. The calculated temperature distribution under seizure conditions is compared with measured crater depth profiles to show that crater wear is coupled with phase transformation. Under conditions of seizure, the role of coatings in suppressing dissolution wear is demonstrated in both ductile iron and low carbon steel using TiN and HfN, which are compounds having the least solubility in the workpiece (steel matrix).
TL;DR: In this article, the influence of cutting edge geometry on chip removal process is studied through numerical simulation of cutting with sharp, chamfered or blunt edges and with carbide and CBN tools.
Abstract: In high speed machining of hard materials, tools with chamfered edge and materials resistant to diffusion wear are commonly used. In this paper, the influence of cutting edge geometry on the chip removal process is studied through numerical simulation of cutting with sharp, chamfered or blunt edges and with carbide and CBN tools. The analysis is based on the use of ALE finite element method for continuous chip formation process. Simulations include cutting with tools of different chamfer angles and cutting speeds. The study shows that a region of trapped material zone is formed under the chamfer and acts as the effective cutting edge of the tool, in accordance with experimental observations. While the chip formation process is not significantly affected by the presence of the chamfer, the cutting forces are increased. The effect of cutting speed on the process is also studied.
TL;DR: In this article, laser heat-assisted machining is adopted in machining Al2O3p/Al composite, good results were obtained. But the results of the present experiment showed that the cutting force was reduced by 30-50% and tool wear was increased by 20-30% compared with conventional cutting.
TL;DR: The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time.
Abstract: In recent past, several neural network models which employ cutting forces and AErms or their derivatives for estimation as well as classification of flank wear have been developed. However, a significant variation in mean cutting forces and AErms at the start of cutting operation for similar new tools can result in estimation and classification error. In order to deal with this problem, a new on-line fuzzy neural network (FNN) model is presented in this paper. This model has four parts. The first part of the model is developed to classify tool wear by using fuzzy logic. The second part of this model is designed for normalizing the inputs for the next part. The third part consisting of modified least-square backpropagation neural network is built to estimate flank and crater wear. The development of forth part was done in order to adjust the results of the third part. Several basic and derived parameters including forces, AErms, skew and kurtosis of force bands, as well as the total energy of forces were employed as inputs in order to enhance the accuracy of tool wear prediction. The experimental results indicate that the proposed on-line FNN model has a high accuracy for estimating progressive flank and crater wear with small computational time.
TL;DR: In this paper, the authors combine predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions.
Abstract: Productivity and quality in the finish turning of hardened steels can be improved by utilizing predicted performance of the cutting tools. This paper combines predictive machining approach with neural network modeling of tool flank wear in order to estimate performance of chamfered and honed Cubic Boron Nitride (CBN) tools for a variety of cutting conditions. Experimental work has been performed in orthogonal cutting of hardened H-13 type tool steel using CBN tools. At the selected cutting conditions the forces have been measured using a piezoelectric dynamometer and data acquisition system. Simultaneously flank wear at the cutting edge has been monitored by using a tool makers microscope. The experimental force and wear data were utilized to train the developed simulation environment based on back propagation neural network modeling. A trained neural network system was used in predicting flank wear for various different cutting conditions. The developed prediction system was found to be capable of accurate tool wear classification for the range it had been trained. 2001 Elsevier Science Ltd. All rights reserved.
TL;DR: In this article, the authors measured the normal force and friction force directly in an altered machining setup to simulate the pure frictional behavior of the tool-chip interface in cryogenic cutting.
Abstract: Cryogenic machining is considered an environmentally safe alternative to conventional machining where cutting fluid is used. However, the improved machinability in cryogenic machining has been attributed to the cooling effect of liquid nitrogen (LN2) in past research. Our recent studies indicate that LN2 may possess a lubrication effect in machining, as evidenced by a reduction of tool wear, the apparent coefficients of friction calculated by a mathematical model, and the secondary deformation in chip metallurgy. However, there is a need for a direct proof of LN2's lubrication effect before it can be claimed to be a lubricant in cryogenic machining. This paper presents the methodology and data from an experiment that measures the normal force and friction force directly in an altered machining setup. This procedure simulates the pure frictional behavior of the tool-chip interface in cryogenic cutting. The results show that LN2 cooled condition has a significantly lower coefficient of friction than dry con...
TL;DR: In this article, the laser-assisted machining of pressureless sintered mullite ceramics was evaluated in terms of cutting force, surface temperature, chip morphology, tool wear, surface roughness and subsurface damage.
Abstract: The present study focuses on the evaluation of the laser-assisted machining (LAM) of pressureless sintered mullite ceramics. Due to mullite's low thermal diffusivity and tensile strength, a new method for applying laser power is devised to eliminate cracking and fracture of the workpiece during laser heating. The LAM process is characterized in terms of cutting force, surface temperature, chip morphology, tool wear, surface roughness and subsurface damage for a variety of operating conditions. Estimated material removal temperatures and the ratio of the feed force to the main cutting force are used to determine material removal mechanisms and regimes for brittle fracture and semi-continuous and continuous chip formation. Surface roughness and subsurface damage are compared between typical parts produced by LAM and grinding. Tool wear characteristics are investigated for variations in laser power, and hence material removal temperature, during LAM of mullite with carbide tools.
TL;DR: In this paper, a self-optimized tool shape for friction-stir welding of AI359 + 20% SiC MMC produced a selfoptimized shape which when achieved resulted in excellent welds and no additional tool wear.
Abstract: Tool wear in the friction-stir welding of AI359 + 20% SiC MMC produced a self-optimized shape which when achieved resulted in excellent welds and no additional tool wear. This optimized tool shape was slightly different at weld speeds of 6 and 9 mm/s. Tool wear rate was observed to decrease linearly and to effectively cease above about 11 mm/s weld speed. There was some attrition or comminution of larger SiC particles during welding but the weld zones were very homogeneous in terms of SiC distribution between the base metal and the friction-stir weld zone. There was no weld-related degradation and the weld zone hardness was 30% higher than the base.
TL;DR: In this paper, the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation are described.
Abstract: The cutting process is a major material removal process; hence, it is important to search for ways of detecting tool failure. This paper describes the results of the application of an adaptive-network-based fuzzy inference system (ANFIS) for tool-failure detection in a single-point turning operation. In a turning operation, wear and failure of the tool are usually monitored by measuring cutting force, load current, vibration, acoustic emission (AE) and temperature. The AE signal and cutting force signal provide useful information concerning the tool-failure condition. Therefore, five input parameters of the combined signals (AE signal and cutting force signal) have been used in the ANFIS model to detect the tool state. In this model, we adopted three different types of membership function for analysis for ANFIS training and compared their differences regarding the accuracy rate of the tool-state detection. The result obtained for the successful classification of tool state with respect to only two classes (normal or failure) is very good. The results also indicate that a triangular MF and a generalised bell MF have a better rate of detection. We also applied grey relational analysis to determine the order of influence of the five cutting parameters on tool-state detection.
TL;DR: In this article, the wear mechanism of compacted graphite iron in the automotive industry was investigated and clarified, and it was shown that the presence of manganese-sulphur in the microstructure of graphite has a direct influence on the formation of a graphite layer on the cutting edge.