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Patrick Kwon - One of the best experts on this subject based on the ideXlab platform.
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A comparative investigation on Flank Wear when turning three cast irons
Tribology International, 2018Co-Authors: Sirisak Tooptong, Kyung Hee Park, Patrick KwonAbstract:Abstract A series of turning experiments is conducted on Flake Graphite Iron (FGI), Compacted Graphite Iron (CGI), and Nodular Graphite Iron (NGI) under dry condition. With uncoated carbide inserts, the adhesion layer, covering the entire tool-work interfaces, is formed when cutting CGI and NGI, which reduced Flank Wear compared to FGI. By contrast, the Flank Wear on the multilayer-coated inserts, obviously significantly reduced compared to the uncoated inserts, is significantly higher for CGI and NGI compared to FGI. Finite Element Analysis (FEA) is used to estimate the average Flank temperature under various cutting conditions, which indicates the elevated cutting temperature with CGI and NGI. With the cutting temperatures from FEA simulations, the observed Flank Wear for FGI, CGI, and NGI conforms to the two-body abrasive Wear model, indicating a common abrasive Wear mechanism. Therefore, the main reason for the poor machinabilities of CGI and NGI is the higher cutting temperatures on the cutting tools when cutting CGI and NGI.
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Microstructural impact on Flank Wear during turning of various Ti-6Al-4V alloys
Wear, 2017Co-Authors: Dinh Nguyen, Kyung Hee Park, Di Kang, Thomas R. Bieler, Patrick KwonAbstract:Abstract Titanium alloys typically do not contain hard inclusion phases typically observed in other metallic alloys. However, the characteristic scoring marks and more distinctive micro- and/or macro-chippings are ubiquitously observed on the Flank faces of cutting tools in machining titanium alloys, which is the direct evidence of abrasive Wear (hard phase(s) in the microstructure abrading and damaging the Flank surface). Thus, an important question lies with the nature of the hard phases present in the titanium microstructure. In this work, we present a comprehensive study that examines the microstructural impact on Flank Wear attained by turning various Ti-6Al-4V bars having distinct microstructures with uncoated carbide inserts. In particular, four samples with elongated, mill-annealed, solution treated & annealed and fully-lamellar microstructures were selected for our turning experiments. After turning each sample, the Flank surface of each insert was observed with confocal laser scanning microscopy (CLSM) and analyzed to determine the Flank Wear behavior in relation to each sample' distinct microstructures. To characterize the microstructure, scanning electron microscopy (SEM) together with Orientation imaging microstructure (OIM) was used to identify and distinguish the phases present in each sample and the content and topography of each phase was correlated to the behavior of Flank Wear. The Flank Wear is also affected by the interface conditions such as temperature and pressure, which were estimated using finite element analysis (FEA) models. The temperature dependent abrasion models enable us to estimate the Flank Wear rate for each microstructure, and are compared with the experimentally measured Wear data.
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The Origin of Flank Wear in Turning Ti-6Al-4V
Journal of Manufacturing Science and Engineering-transactions of The Asme, 2016Co-Authors: Trung Thanh Nguyen, Patrick Kwon, Di Kang, Thomas R. BielerAbstract:Unlike ferrous materials, where the cementite (Fe3C) phase acts as an abrasive that contributes to Flank Wear on the cutting tool, most titanium (Ti) alloys possesses no significant hard phase. Thus, the origin of Flank Wear is unclear in machining Ti alloys. To address this question, a Ti-6Al-4V bar was turned under various conditions with uncoated carbide and polycrystalline diamond (PCD) inserts, most commonly used tool materials for machining Ti alloys. These inserts were retrieved sporadically while tuning to examine the Wear patterns using a confocal microscope. To correlate the patterns with the microstructure of the original bar, the microstructure was carefully characterized using Orientation Image Microscopy™ (OIM) with electron-backscattered diffraction (EBSD). From the Wear patterns, two distinct types of damage were identified: (a) microscopic and macroscopic fractures on the cutting edges and (b) scoring marks on Flank faces. This paper demonstrates that both types of damage were caused primarily by the heterogeneity in hardness in the α-crystals, where the plane perpendicular to the c-axis in an α-crystal is substantially harder than any other direction in the α-crystal as well as the isotropic β-crystal. In addition to such heterogeneities, adhesion layer, ubiquitous to machining Ti alloys, detaches small fragments of the tool, which resulted in microscopic and macroscopic fractures observed on Flank Wear.
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Flank Wear of multi-layer coated tool
Wear, 2011Co-Authors: Kyung Hee Park, Patrick KwonAbstract:Abstract Flank Wear analysis on the multi-layer (TiCN/Al 2 O 3 /TiCN) coated carbide inserts has been performed after turning AISI 1045 steel. Using advanced microscope and image processing techniques including wavelet transform, we have obtained the Flank Wear profiles and analyzed the surface roughness and groove sizes on the coating layers to understand the progress of Flank Wear and its Wear mechanisms. The dominant Wear mechanism was found to be the abrasion by the cementite phase in the work material. The adhesion took over after carbide substrate was exposed as the notch Wear also became more significant. Based on the experimental result, it was concluded that the hardness of the coating is the most important requirement to resist Flank Wear due to its high Wear resistance against abrasion. Therefore, the multilayer coating scheme does not provide any significant benefit to resist Flank Wear.
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Flank Wear of Multi-Layer Coated Tool and Wear Prediction Using Abrasion Wear Model
ASME 2009 International Manufacturing Science and Engineering Conference Volume 1, 2009Co-Authors: Kyung Hee Park, Patrick KwonAbstract:In this paper, we have combined experimental and numerical approaches to understand the Flank Wear and its evolution of the multi-layer (TiCN/Al2 O3 /TiCN) coated carbide insert after continuous turning of AISI 1045 steels. In addition using advanced microscope techniques such as scanning electron microscope, confocal laser scanning microscope, etc., we have captured the three dimensional images of Flank Wear. Using the wavelet filtering, the roughness profiles and groove sizes on the Flank surface were analyzed and compared. Both 2-body and 3-body abrasion models were used as the basis to predict Flank Wear lands, which are then compared with the experimentally observed Wear images. Finite Element (FE) models were developed to simulate the changes on the interfacial conditions as the Flank Wear progresses during cutting.Copyright © 2009 by ASME
S. Pittner - One of the best experts on this subject based on the ideXlab platform.
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Wavelet networks for sensor signal classification in Flank Wear assessment
Journal of Intelligent Manufacturing, 1998Co-Authors: S. Pittner, Sagar Kamarthi, Qinglan GaoAbstract:It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing Flank Wear. Based on this fact, this paper investigates a Flank Wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for Flank Wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to Flank Wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed Flank Wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.
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FOURIER AND WAVELET TRANSFORM FOR Flank Wear ESTIMATION — A COMPARISON
Mechanical Systems and Signal Processing, 1997Co-Authors: Sagar Kamarthi, S. PittnerAbstract:Abstract This article presents potential sensor data representation schemes for force and vibration signals in the context of Flank Wear estimation in turning processes. In particular, the performances of methods based on fast Fourier transforms (FFTs) and fast wavelet transforms (FWTs) are compared using data from turning experiments. This research, for the first time, studies the performance of these modern sensor data representation schemes for Flank Wear estimation on a common platform and provides a useful insight into their merits and drawbacks. The Flank Wear estimates are computed continually from the features extracted through each representation scheme by using a simple recurrent neural network architecture. The results can be used for selecting correct data representation schemes for Flank Wear estimation.
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fourier and wavelet transform for Flank Wear estimation a comparison
Mechanical Systems and Signal Processing, 1997Co-Authors: Sagar Kamarthi, S. PittnerAbstract:Abstract This article presents potential sensor data representation schemes for force and vibration signals in the context of Flank Wear estimation in turning processes. In particular, the performances of methods based on fast Fourier transforms (FFTs) and fast wavelet transforms (FWTs) are compared using data from turning experiments. This research, for the first time, studies the performance of these modern sensor data representation schemes for Flank Wear estimation on a common platform and provides a useful insight into their merits and drawbacks. The Flank Wear estimates are computed continually from the features extracted through each representation scheme by using a simple recurrent neural network architecture. The results can be used for selecting correct data representation schemes for Flank Wear estimation.
Shidin Balakrishnan - One of the best experts on this subject based on the ideXlab platform.
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Predicting tool Flank Wear using spindle speed change
International Journal of Machine Tools and Manufacture, 1995Co-Authors: J. E. Kaye, D. H. Yan, N. Popplewell, Shidin BalakrishnanAbstract:A unique technique is developed for on-line prediction of the tool Flank Wear in turning using the spindle speed change. A mathematical model is presented for the approach. The speed sensing element is an optical encoder mounted on the spindle shaft and interfaced to an IBM compatible microcomputer employing custom designed electronics. The changes in spindle speed are compensated for lathe transmission ratio, electrical configuration of the the lathe's motor, and the torque speed relationship of the machine. Using the surface response methodology (which reduces the number of cutting sessions required for accurate results), a series of cutting tests are performed with various combinations of cutting speed, feedrate, depth of cut and material hardness. Predictions from the model correlate well with actual Flank Wear measurements. © 1995.
Sagar Kamarthi - One of the best experts on this subject based on the ideXlab platform.
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Wavelet networks for sensor signal classification in Flank Wear assessment
Journal of Intelligent Manufacturing, 1998Co-Authors: S. Pittner, Sagar Kamarthi, Qinglan GaoAbstract:It is known that the force and vibration sensor signals in a turning process are sensitive to the gradually increasing Flank Wear. Based on this fact, this paper investigates a Flank Wear assessment technique in turning through force and vibration signals. Mainly to reduce the computational burden associated with the existing sensor-based methods for Flank Wear assessment, a so-called wavelet network is investigated. The basic idea in this new method is to optimize simultaneously the wavelet parameters (that represent signal features) and the signal-interpretation parameters (that are equivalent to neural network weights) to eliminate the feature extraction phase without increasing the computational complexity of the neural network. A neural network architecture similar to a standard one-hidden-layer feedforward neural network is used to relate sensor signal measurements to Flank Wear classes. A novel training algorithm for such a network is developed. The performance of this n ew method is compared with a previously developed Flank Wear assessment method which uses a separate feature extraction step. The proposed wavelet network can also be useful for developing signal interpretation schemes for manufacturing process monitoring, critical component monitoring, and product quality monitoring.
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FOURIER AND WAVELET TRANSFORM FOR Flank Wear ESTIMATION — A COMPARISON
Mechanical Systems and Signal Processing, 1997Co-Authors: Sagar Kamarthi, S. PittnerAbstract:Abstract This article presents potential sensor data representation schemes for force and vibration signals in the context of Flank Wear estimation in turning processes. In particular, the performances of methods based on fast Fourier transforms (FFTs) and fast wavelet transforms (FWTs) are compared using data from turning experiments. This research, for the first time, studies the performance of these modern sensor data representation schemes for Flank Wear estimation on a common platform and provides a useful insight into their merits and drawbacks. The Flank Wear estimates are computed continually from the features extracted through each representation scheme by using a simple recurrent neural network architecture. The results can be used for selecting correct data representation schemes for Flank Wear estimation.
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fourier and wavelet transform for Flank Wear estimation a comparison
Mechanical Systems and Signal Processing, 1997Co-Authors: Sagar Kamarthi, S. PittnerAbstract:Abstract This article presents potential sensor data representation schemes for force and vibration signals in the context of Flank Wear estimation in turning processes. In particular, the performances of methods based on fast Fourier transforms (FFTs) and fast wavelet transforms (FWTs) are compared using data from turning experiments. This research, for the first time, studies the performance of these modern sensor data representation schemes for Flank Wear estimation on a common platform and provides a useful insight into their merits and drawbacks. The Flank Wear estimates are computed continually from the features extracted through each representation scheme by using a simple recurrent neural network architecture. The results can be used for selecting correct data representation schemes for Flank Wear estimation.
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Flank Wear Estimation in Turning Through Wavelet Representation of Acoustic Emission Signals
Journal of Manufacturing Science and Engineering-transactions of The Asme, 1997Co-Authors: Sagar Kamarthi, Soundar R. T. Kumara, P. H. CohenAbstract:This paper investigates a Flank Wear estimation technique in turning through wavelet representation of acoustic emission (AE) signals. It is known that the power spectral density of AE signals in turning is sensitive to gradually increasing Flank Wear. In previous methods, the power spectral density of AE signals is computed from Fourier transform based techniques. To overcome some of the limitations associated with the Fourier representation of AE signals for Flank Wear estimation, wavelet representation of AE signals is investigated. This investigation is motivated by the superiority of the wavelet transform over the Fourier transform in analyzing rapidly changing signals such as AE, in which high frequency components are to be studied with sharper time resolution than low frequency components. The effectiveness of the wavelet representation of AE signals for Flank Wear estimation is investigated by conducting a set of turning experiments on AISI 6150 steel workpiece and K68 (C2) grade uncoated carbide inserts. In these experiments, Flank Wear is monitored through AE signals. A recurrent neural network of simple architecture is used to relate AE features to Flank Wear. Using this technique, accurate Flank Wear estimation results are obtained for the operating conditions that are within in the range of those used during neural network training. These results compared to those of Fourier transform representation are much superior. These findings indicate that the wavelet representation of AE signals is more effective in extracting the AE features sensitive to gradually increasing Flank Wear than the Fourier representation.
Dinghua Zhang - One of the best experts on this subject based on the ideXlab platform.
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an investigation of tool temperature in end milling considering the Flank Wear effect
International Journal of Mechanical Sciences, 2017Co-Authors: Dinghua Zhang, Baohai WuAbstract:Abstract In this paper, the tool temperature in end milling considering the Flank Wear effect was investigated by theoretical and experimental method. Theoretically, a new analytical model was developed to predict tool temperature in end milling. The new analytical model takes into account the Flank Wear effect, complex tool geometry and dynamic heat flux and partition in end milling. In order to tackle complex tool geometry, the tool is axially discretized into numerous tool differential elements. Based on heat source method, the heat transfer of each tool differential element is analyzed independently to model the temperature of each tool differential element. Then superpose the temperature of each tool differential element is to work out the total tool temperature. Experimental, according to single factor design, the end milling operations of Ti6Al4V were conducted with fresh and worn tool to determine the effect of Flank Wear on tool temperature and to validate the theoretical model. The experimental validation indicates that the new temperature model is able to predict the sharp and worn tool temperature in end milling accurately. The effect of Flank Wear on tool temperature, cutting force and heat partition on Flank face is evident. At same Flank Wear, the tool temperature and cutting force primarily depend on feed per tooth and secondly on cutting speed. The heat partition on tool-chip interface increases with the increase of feed per tooth, but decreases when the cutting speed increases. There is an inverse trend in variation of heat partition on Flank face compared with heat partition on tool-chip interface.
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AIM - Tool Flank Wear recognition based on the variation of milling force vector in end milling
2014 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2014Co-Authors: Yongfeng Hou, Dinghua Zhang, Ming LuoAbstract:In the manufacturing process, the cutting tool Wear is an important affecting factor of the product quality. Tool Wear condition monitoring is an effective means to ensure the workpiece quality, and to improve the tool life. Tool Flank Wear will directly lead to the variation of milling force. Therefore, a Flank Wear recognition approach of flat end milling tool based on the influence of the tool Wear on the milling force vector is proposed in this paper. In this approach, the friction effect force and the cutting force of milling tool are treated separately, the milling force model of flat end milling tool is established, and it is believed that the milling forces of the tool without Flank Wear are not influenced by the friction effect. The influence of the milling tool Flank Wear on the milling force vector variation is investigated, and this influence relationship is adopted to recognize the Flank Wear of flat end milling tool. Finally, the superalloy material is used to perform the Wear milling experiment on the CNC machine tool. The experiment results show that, this approach can recognize the milling tool Flank Wear efficiently and accurately.