The Experts below are selected from a list of 416124 Experts worldwide ranked by ideXlab platform
Nabil Gindy - One of the best experts on this subject based on the ideXlab platform.
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tool wear Predictive Model based on least squares support vector machines
Mechanical Systems and Signal Processing, 2007Co-Authors: Dongfeng Shi, Nabil GindyAbstract:The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear Predictive Model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW. PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction Model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed Predictive Model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials.
Dongfeng Shi - One of the best experts on this subject based on the ideXlab platform.
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tool wear Predictive Model based on least squares support vector machines
Mechanical Systems and Signal Processing, 2007Co-Authors: Dongfeng Shi, Nabil GindyAbstract:The development of tool wear monitoring system for machining processes has been well recognised in industry due to the ever-increased demand for product quality and productivity improvement. This paper presents a new tool wear Predictive Model by combination of least squares support vector machines (LS-SVM) and principal component analysis (PCA) technique. The corresponding tool wear monitoring system is developed based on the platform of PXI and LabVIEW. PCA is firstly proposed to extract features from multiple sensory signals acquired from machining processes. Then, LS-SVM-based tool wear prediction Model is constructed by learning correlation between extracted features and actual tool wear. The effectiveness of proposed Predictive Model and corresponding tool wear monitoring system is demonstrated by experimental results from broaching trials.
Romain Mathis - One of the best experts on this subject based on the ideXlab platform.
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Assessment of inner–outer interactions in the urban boundary layer using a Predictive Model
Journal of Fluid Mechanics, 2019Co-Authors: Karin Blackman, Laurent Perret, Romain MathisAbstract:Urban-type rough-wall boundary layers developing over staggered cube arrays with plan area packing density, λ p , of 6.25%, 25% or 44.4% have been studied at two Reynolds numbers within a wind tunnel using hot-wire anemometry (HWA). A fixed HWA probe is used to capture the outer-layer flow while a second moving probe is used to capture the inner-layer flow at 13 wall-normal positions between 1.25h and 4h where h is the height of the roughness elements. The synchronized two-point HWA measurements are used to extract the near-canopy large-scale signal using spectral linear stochastic estimation and a Predictive Model is calibrated in each of the six measurement configurations. Analysis of the Predictive Model coefficients demonstrates that the canopy geometry has a significant influence on both the superposition and amplitude modulation. The universal signal, the signal that exists in the absence of any large-scale influence, is also modified as a result of local canopy geometry suggesting that although the non-linear interactions within urban-type rough-wall boundary layers can be Modelled using the Predictive Model as proposed by Mathis et al. (2011a), the Model must be however calibrated for each type of canopy flow regime. The Reynolds number does not significantly affect any of the Model coefficients, at least over the limited range of Reynolds numbers studied here. Finally, the Predictive Model is validated using a prediction of the near-canopy signal at a higher Reynolds number and a prediction using reference signals measured in different canopy geometries to run the Model. Statistics up to the 4 th order and spectra are accurately reproduced demonstrating the capability of the Predictive Model in an urban-type rough-wall boundary layer.
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Toward the development of a Predictive Model of the roughness sublayer flow
2019Co-Authors: Karin Blackman, Laurent Perret, Romain MathisAbstract:The non-linear interactions between large-scale momentum regions and small-scale structures induced by the presence of the roughness have been studied in boundary layers consisting of staggered cube arrays with plan area packing density of 6.25%, 25% or 44.4%. The measurements, consisting of hot-wire anemometry, were conducted at two Reynolds numbers in each of the canopy configurations. The canopy configuration is shown to have a significant influence on all parameters of the Predictive Model close to the roughness elements which is a result of the characteristics of the small-scale structures induced by the presence of the cubes. Several tests of the Predictive Model have been undertaken, demonstrating the good capability of the Model to reproduce accurately spectra and statistics up to the 4th order. The Model must be however calibrated for each type of canopy flow regime.
Karin Blackman - One of the best experts on this subject based on the ideXlab platform.
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Assessment of inner–outer interactions in the urban boundary layer using a Predictive Model
Journal of Fluid Mechanics, 2019Co-Authors: Karin Blackman, Laurent Perret, Romain MathisAbstract:Urban-type rough-wall boundary layers developing over staggered cube arrays with plan area packing density, λ p , of 6.25%, 25% or 44.4% have been studied at two Reynolds numbers within a wind tunnel using hot-wire anemometry (HWA). A fixed HWA probe is used to capture the outer-layer flow while a second moving probe is used to capture the inner-layer flow at 13 wall-normal positions between 1.25h and 4h where h is the height of the roughness elements. The synchronized two-point HWA measurements are used to extract the near-canopy large-scale signal using spectral linear stochastic estimation and a Predictive Model is calibrated in each of the six measurement configurations. Analysis of the Predictive Model coefficients demonstrates that the canopy geometry has a significant influence on both the superposition and amplitude modulation. The universal signal, the signal that exists in the absence of any large-scale influence, is also modified as a result of local canopy geometry suggesting that although the non-linear interactions within urban-type rough-wall boundary layers can be Modelled using the Predictive Model as proposed by Mathis et al. (2011a), the Model must be however calibrated for each type of canopy flow regime. The Reynolds number does not significantly affect any of the Model coefficients, at least over the limited range of Reynolds numbers studied here. Finally, the Predictive Model is validated using a prediction of the near-canopy signal at a higher Reynolds number and a prediction using reference signals measured in different canopy geometries to run the Model. Statistics up to the 4 th order and spectra are accurately reproduced demonstrating the capability of the Predictive Model in an urban-type rough-wall boundary layer.
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Toward the development of a Predictive Model of the roughness sublayer flow
2019Co-Authors: Karin Blackman, Laurent Perret, Romain MathisAbstract:The non-linear interactions between large-scale momentum regions and small-scale structures induced by the presence of the roughness have been studied in boundary layers consisting of staggered cube arrays with plan area packing density of 6.25%, 25% or 44.4%. The measurements, consisting of hot-wire anemometry, were conducted at two Reynolds numbers in each of the canopy configurations. The canopy configuration is shown to have a significant influence on all parameters of the Predictive Model close to the roughness elements which is a result of the characteristics of the small-scale structures induced by the presence of the cubes. Several tests of the Predictive Model have been undertaken, demonstrating the good capability of the Model to reproduce accurately spectra and statistics up to the 4th order. The Model must be however calibrated for each type of canopy flow regime.
Yingshu Chen - One of the best experts on this subject based on the ideXlab platform.
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A tool wear Predictive Model based on SVM
2010 Chinese Control and Decision Conference, 2010Co-Authors: Yiqiu Qian, Tian Jia, Libing Liu, Yingshu ChenAbstract:Tool wear monitoring is an integral part of modern CNC machine control. This paper presents a new tool wear Predictive Model by combination of workpiece surface texture analysis and support vector machine with genetic algorithm (SVMG). Firstly, the column projection method and the Gabor filter method are proposed to extract texture features of machined surfaces. Then, SVMG-based tool wear Predictive Model is constructed by learning correlation between extracted texture features and actual tool wear. The effectiveness of the proposed Predictive Model and corresponding tool wear monitoring system is demonstrated by experimental results from turning trials. After simulated and compared with the Predictive Model based on BP neural networks, the method shows much better performance on the Predictive precision and the intelligent adjusting parameters.
