Готові списки джерел за темами / Roughness prediction

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Добірка наукової літератури з теми "Roughness prediction"

Автор: Grafiati
Опубліковано: 4 листопада 2023

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Статті в журналах з теми "Roughness prediction"

1

Nalbant, Muammer, Hasan Gokkaya, and İhsan Toktaş. "Comparison of Regression and Artificial Neural Network Models for Surface Roughness Prediction with the Cutting Parameters in CNC Turning." Modelling and Simulation in Engineering 2007 (2007): 1–14. http://dx.doi.org/10.1155/2007/92717.

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Анотація:
Surface roughness, an indicator of surface quality, is one of the most specified customer requirements in machining of parts. In this study, the experimental results corresponding to the effects of different insert nose radii of cutting tools (0.4, 0.8, 1.2 mm), various depth of cuts (0.75, 1.25, 1.75, 2.25, 2.75 mm), and different feedrates (100, 130, 160, 190, 220 mm/min) on the surface quality of the AISI 1030 steel workpieces have been investigated using multiple regression analysis and artificial neural networks (ANN). Regression analysis and neural network-based models used for the prediction of surface roughness were compared for various cutting conditions in turning. The data set obtained from the measurements of surface roughness was employed to and tests the neural network model. The trained neural network models were used in predicting surface roughness for cutting conditions. A comparison of neural network models with regression model was carried out. Coefficient of determination was 0.98 in multiple regression model. The scaled conjugate gradient (SCG) model with 9 neurons in hidden layer has produced absolute fraction of variance(R2)values of 0.999 for the training data, and 0.998 for the test data. Predictive neural network model showed better predictions than various regression models for surface roughness. However, both methods can be used for the prediction of surface roughness in turning.
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2

Lin, Wan-Ju, Shih-Hsuan Lo, Hong-Tsu Young, and Che-Lun Hung. "Evaluation of Deep Learning Neural Networks for Surface Roughness Prediction Using Vibration Signal Analysis." Applied Sciences 9, no. 7 (April 8, 2019): 1462. http://dx.doi.org/10.3390/app9071462.

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Анотація:
The use of surface roughness (Ra) to indicate product quality in the milling process in an intelligent monitoring system applied in-process has been developing. From the considerations of convenient installation and cost-effectiveness, accelerator vibration signals combined with deep learning predictive models for predicting surface roughness is a potential tool. In this paper, three models, namely, Fast Fourier Transform-Deep Neural Networks (FFT-DNN), Fast Fourier Transform Long Short Term Memory Network (FFT-LSTM), and one-dimensional convolutional neural network (1-D CNN), are used to explore the training and prediction performances. Feature extraction plays an important role in the training and predicting results. FFT and the one-dimensional convolution filter, known as 1-D CNN, are employed to extract vibration signals’ raw data. The results show the following: (1) the LSTM model presents the temporal modeling ability to achieve a good performance at higher Ra value and (2) 1-D CNN, which is better at extracting features, exhibits highly accurate prediction performance at lower Ra ranges. Based on the results, vibration signals combined with a deep learning predictive model could be applied to predict the surface roughness in the milling process. Based on this experimental study, the use of prediction of the surface roughness via vibration signals using FFT-LSTM or 1-D CNN is recommended to develop an intelligent system.
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3

Saleh, A., D. W. Fryrear, and J. D. Bilbro. "AERODYNAMIC ROUGHNESS PREDICTION FROM SOIL SURFACE ROUGHNESS MEASUREMENT." Soil Science 162, no. 3 (March 1997): 205–10. http://dx.doi.org/10.1097/00010694-199703000-00006.

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4

Cai, Xiao Jiang, Z. Q. Liu, Q. C. Wang, Shu Han, Qing Long An, and Ming Chen. "Surface Roughness Prediction in Turning of Free Machining Steel 1215 by Artificial Neural Network." Advanced Materials Research 188 (March 2011): 535–41. http://dx.doi.org/10.4028/www.scientific.net/amr.188.535.

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Анотація:
Surface roughness is a significant aspect of the surface integrity concept. It is efficient to predict the surface roughness in advance by a prediction model. In this study, artificial neural network is used to model the surface roughness in turning of free machining steel 1215. The inputs considered in the prediction ANN model were cutting speed, feed rate and depth of cut, and the output was Ra. Several feed-forward neural networks with different architectures were compared in terms of prediction accuracy, and then the best prediction model, a 3-4-1-1 ANN was capable of predicting Ra with a mean squared error 5.46%, was presented.
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5

Li, Shilong, Xiaolei Yang, and Yu Lv. "Predictive capability of the logarithmic law for roughness-modeled large-eddy simulation of turbulent channel flows with rough walls." Physics of Fluids 34, no. 8 (August 2022): 085112. http://dx.doi.org/10.1063/5.0098611.

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Анотація:
Direct numerical simulation (DNS) and large-eddy simulation (LES) resolving roughness elements are computationally expensive. LES employing the logarithmic law as the wall model, without the need to resolve the flow at the roughness element scale, provides an efficient alternative for simulating turbulent flows over rough walls. In this work, we evaluate the predictive capability of the roughness-modeled LES by comparing its predictions with those from the roughness-resolved DNS for turbulent channel flows with rough walls. A good agreement is observed for the mean streamwise velocity. The Reynolds stresses predicted by the roughness-modeled LES also reasonably agree with the roughness-resolved predictions. Differences, on the other hand, are observed for the dispersive Reynolds stresses, integral scales, and space-time correlations. The roughness-modeled LES fails to predict the dispersive stresses as one can expect. In the outer layer, the integral length scale predicted by the roughness-modeled LES is lower than the roughness-resolved prediction, which cannot be improved by refining the grid. As for the space-time correlations, discrepancies are shown for the streamwise velocity fluctuations, with a faster decay of the correlation in the outer layer observed in the roughness-modeled predictions. Examination of the space-time correlation using the elliptic approximation model shows that the roughness-modeled LES underpredicts the convection velocity in the near wall region while overpredicting the sweeping velocity in the outer layer with no improvements observed when refining the grid.
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6

Alajmi, Mahdi S., and Abdullah M. Almeshal. "Prediction and Optimization of Surface Roughness in a Turning Process Using the ANFIS-QPSO Method." Materials 13, no. 13 (July 4, 2020): 2986. http://dx.doi.org/10.3390/ma13132986.

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Анотація:
This study presents a prediction method of surface roughness values for dry and cryogenic turning of AISI 304 stainless steel using the ANFIS-QPSO machine learning approach. ANFIS-QPSO combines the strengths of artificial neural networks, fuzzy systems and evolutionary optimization in terms of accuracy, robustness and fast convergence towards global optima. Simulations revealed that ANFIS-QPSO results in accurate prediction of surface roughness with RMSE = 4.86%, MAPE = 4.95% and R2 = 0.984 for the dry turning process. Similarly, for the cryogenic turning process, ANFIS-QPSO resulted in surface roughness predictions with RMSE = 5.08%, MAPE = 5.15% and R2 = 0.988 that are of high agreement with the measured values. Performance comparisons between ANFIS-QPSO, ANFIS, ANFIS-GA and ANFIS-PSO suggest that ANFIS-QPSO is an effective method that can ensure a high prediction accuracy of surface roughness values for dry and cryogenic turning processes.
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7

Zeng, Shi, and Dechang Pi. "Milling Surface Roughness Prediction Based on Physics-Informed Machine Learning." Sensors 23, no. 10 (May 22, 2023): 4969. http://dx.doi.org/10.3390/s23104969.

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Анотація:
Surface roughness is a key indicator of the quality of mechanical products, which can precisely portray the fatigue strength, wear resistance, surface hardness and other properties of the products. The convergence of current machine-learning-based surface roughness prediction methods to local minima may lead to poor model generalization or results that violate existing physical laws. Therefore, this paper combined physical knowledge with deep learning to propose a physics-informed deep learning method (PIDL) for milling surface roughness predictions under the constraints of physical laws. This method introduced physical knowledge in the input phase and training phase of deep learning. Data augmentation was performed on the limited experimental data by constructing surface roughness mechanism models with tolerable accuracy prior to training. In the training, a physically guided loss function was constructed to guide the training process of the model with physical knowledge. Considering the excellent feature extraction capability of convolutional neural networks (CNNs) and gated recurrent units (GRUs) in the spatial and temporal scales, a CNN–GRU model was adopted as the main model for milling surface roughness predictions. Meanwhile, a bi-directional gated recurrent unit and a multi-headed self-attentive mechanism were introduced to enhance data correlation. In this paper, surface roughness prediction experiments were conducted on the open-source datasets S45C and GAMHE 5.0. In comparison with the results of state-of-the-art methods, the proposed model has the highest prediction accuracy on both datasets, and the mean absolute percentage error on the test set was reduced by 3.029% on average compared to the best comparison method. Physical-model-guided machine learning prediction methods may be a future pathway for machine learning evolution.
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8

Ng, J. J., Z. W. Zhong, and T. I. Liu. "Prediction of Roughness Heights of Milled Surfaces for Product Quality Prediction and Tool Condition Monitoring." Journal of Materials and Applications 8, no. 2 (November 15, 2019): 97–104. http://dx.doi.org/10.32732/jma.2019.8.2.97.

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Анотація:
The objective of this research is to predict the roughness heights of milled surfaces, which indicates product quality and tool conditions. Two experiments are carried out to evaluate relevant factors such as vibration, force, and surface roughness. The purpose of the first experiment is to find out the limits of the machining variables compared to the constraints of the materials. The purpose of the second experiment is to identify, collect, and compare how each factor affects product quality and tool conditions. Based on this study, the vibration, force, and surface roughness are good indicators for tool conditions. When the magnitudes of the vibration and force increase, the surface roughness also increases. The increase in surface roughness with constant cutting parameters indicates the degrading of product quality and the decrease of the tool life. Thus, the variables, such as vibration and forces, are used as the inputs, and the surface roughness is used as the output of neural networks. By optimizing the network variables, it has been found that a 4,4,8,1 neural network can achieve the least absolute error, and accurately predict the actual roughness heights collected from the experiment. The minimum error of the prediction of surface roughness is 0.11%, the average error is 2.11%, and the maximum error is 6.98%. The prediction of surface roughness of milled surfaces is very important for the product quality prediction and tool condition monitoring.
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Zhang, Qi, Yuechao Pei, Yixin Shen, Xiaojun Wang, Jingqi Lai, and Maohui Wang. "A New Perspective on Predicting Roughness of Discontinuity from Fractal Dimension D of Outcrops." Fractal and Fractional 7, no. 7 (June 22, 2023): 496. http://dx.doi.org/10.3390/fractalfract7070496.

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Анотація:
In tunnel construction, predicting the roughness of discontinuity is significant for preventing the collapse of the excavation face. However, currently, we are unable to use a parameter with invariant properties to quantify and predict the roughness of discontinuity. Fractal dimension D is one such parameter that be used to characterize the roughness of discontinuity. The study proposes a new method to predict the roughness of discontinuity from the fractal dimension D of outcrops. The measurement method of the coordinates of outcrops is firstly summarized, and the most suitable method of calculating fractal dimension D is then provided. For characterizing the spatial variability of fractal dimension D, the random field of fractal dimension D is discretized, and the prediction model is then established based on Bayesian theory. The proposed method is applied to one tunnel for predicting the roughness of discontinuity, and the results indicate that the relative errors of prediction are less than 1.5%. The sensitivities of correlation function and discontinuity size are analyzed. It is found that the different correlation functions have no obvious effect on the prediction results, and the proposed method is well applied to relatively large sizes of discontinuity.
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Gu, Jiali, and Pingxiang Cao. "Prediction of straight tooth milling of Scots pine wood by shank cutter based on neural net computations and regression analysis." BioResources 17, no. 2 (February 4, 2022): 2003–19. http://dx.doi.org/10.15376/biores.17.2.2003-2019.

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Анотація:
Regression models and a neural net approach were used to predict the cutting performance during milling of Scots pine (Pinus sylvestris L.) by shank cutter. The influence of rake angle, spindle speed, and milling depth on surface roughness of the workpiece, as well as the connection between the milling force and the surface roughness, were thoroughly considered. Four approaches were used to predict the workpiece’s surface roughness based on the experimental data: Back Propagation Neural Network (BPNN), Radial Basis Function Neural Network (RBFNN), Support Vector Machines (SVM), and multiple linear regression. The comparative analysis of the predictive models showed that Neural Network (NN) had preferable performance for prediction of machined surface roughness, with an R2 of 0.98. The SVM had certain fluctuations and the R2 of the multiple linear regression was just 0.87, indicating that they did not fit well for prediction machined surface roughness. In summary, the effective trend of milling parameters on the machined surface roughness of Scots pine was similar to multiple nonlinear regression, and the accurate prediction by BPNN model can provide technical support for the surface roughness of the Scots Pine and enhance shank cutter performance.
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Дисертації з теми "Roughness prediction"

1

Munoz-Escalona, Patricia. "Surface roughness prediction when milling with square inserts." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519033.

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Shauche, Vishwesh. "Health Assessment based In-process Surface Roughness Prediction System." University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298323430.

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3

Staheli, Kimberlie. "Jacking Force Prediction: An Interface Friction Approach based on Pipe Surface Roughness." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-07052006-203035/.

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Thesis (Ph. D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2007.
Dr. J. David Frost, Committee Chair ; Dr. G. Wayne Clough, Committee Co-Chair ; Dr. William F. Marcuson III, Committee Member ; Dr. Paul W. Mayne, Committee Member ; Dr. Susan Burns, Committee Member.
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4

Yamaguchi, Keiko. "Improved ice accretion prediction techniques based on experimental observations of surface roughness effects on heat transfer." Thesis, Massachusetts Institute of Technology, 1990. http://hdl.handle.net/1721.1/14148.

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5

Sakthi, Gireesh. "WIND POWER PREDICTION MODEL BASED ON PUBLICLY AVAILABLE DATA: SENSITIVITY ANALYSIS ON ROUGHNESS AND PRODUCTION TREND." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-400462.

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The wind power prediction plays a vital role in a wind power project both during the planning and operational phase of a project. A time series based wind power prediction model is introduced and the simulations are run for different case studies. The prediction model works based on the input from 1) nearby representative wind measuring station 2) Global average wind speed value from Meteorological Institute Uppsala University mesoscale model (MIUU) 3) Power curve of the wind turbine. The measured wind data is normalized to minimize the variation in the wind speed and multiplied with the MIUU to get a distributed wind speed. The distributed wind speed is then used to interpolate the wind power with the help of the power curve of the wind turbine. The interpolated wind power is then compared with the Actual Production Data (APD) to validate the prediction model. The simulation results show that the model works fairly predicting the Annual Energy Production (AEP) on monthly averages for all sites but the model could not follow the APD trend on all cases. The sensitivity analysis shows that the variation in production does not depend on ’the variation in roughness class’ nor ’the difference in distance between the measuring station and the wind farm’. The thesis has been concluded from the results that the model works fairly predicting the AEP for all cases within the variation bounds. The accuracy of the model has been validated only for monthly averages since the APD was available only on monthly averages. But the accuracy could be increased based on future work, to assess the Power law exponent (a) parameter for different terrain and validate the model for different time scales provided if the APD is available on different time scales.
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Srinivasan, Sriram. "Development of a Cost Oriented Grinding Strategy and Prediction of Post Grind Roughness using Improved Grinder Models." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78298.

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Анотація:
Irregularities in pavement profiles that exceed standard thresholds are usually rectified using a Diamond Grinding Process. Diamond Grinding is a method of Concrete Pavement Rehabilitation that involves the use of grinding wheels mounted on a machine that scraps off the top surface of the pavement to smooth irregularities. Profile Analysis Software like ProVAL© offers simulation modules that allow users to investigate various grinding strategies and prepare a corrective action plan for the pavement. The major drawback with the current Smoothness Assurance Module© (SAM) in ProVAL© is that it provides numerous grind locations which are both redundant and not feasible in the field. This problem can be overcome by providing a constrained grinding model in which a cost function is minimized; the resulting grinding strategy satisfies requirements at the least possible cost. Another drawback with SAM exists in the built-in grinder models that do not factor in the effect of speed and depth of cut on the grinding head. High speeds or deep cuts will result in the grinding head riding out the cut and likely worsening the roughness. A constrained grinding strategy algorithm with grinder models that factor in speed and depth of cut that results in cost effective grinding with better prediction of post grind surfaces through simulation is developed in this work. The outcome of the developed algorithm is compared to ProVAL's© SAM results.
Master of Science
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Celik, Kazim Arda. "Development Of A Methodology For Prediction Of Surface Roughness Of Curved Cavities Manufactured By 5-axes Cnc Milling." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608368/index.pdf.

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The surface quality is identified by surface roughness parameters. The average surface roughness (Ra) is used in this study, as it is the most commonly used roughness parameter in the industry. A particular curved cavity of a forging die is selected for the experimental study. Different milling methods are tested. The comparison studies are conducted between 3-axes and 5-axes milling, linear and circular tool path strategies and down and up milling. According to the experimental study, appropriate method is determined for the milling of a particular curved cavity of a forging die. The experimental analysis based on design of experiments (DOE) has been employed by considering cutting speed, feed per tooth and stepover parameters. Multiple linear regression technique is used by which a mathematical formula has been developed to predict the Ra values for milling parameters. The results of the mathematical formula are controlled by conducting test experiments and good correlations are observed between the results of the formula and the results of test experiments.
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8

Cummings, Patrick. "Modeling the Locked-Wheel Skid Tester to Determine the Effect of Pavement Roughness on the International Friction Index." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1604.

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Анотація:
Pavement roughness has been found to have an effect on the coefficient of friction measured with the Locked-Wheel Skid Tester (LWT) with measured friction decreasing as the long wave roughness of the pavement increases. However, the current pavement friction standardization model adopted by the American Society for Testing and Materials (ASTM), to compute the International Friction Index (IFI), does not account for this effect. In other words, it had been previously assumed that the IFI's speed constant (SP), which defines the gradient of the pavement friction versus speed relationship, is an invariant for any pavement with a given mean profile depth (MPD), regardless of its roughness. This study was conducted to quantify the effect of pavement roughness on the IFI's speed constant. The first phase of this study consisted of theoretical modeling of the LWT using a two-degree of freedom vibration system. The model parameters were calibrated to match the measured natural frequencies of the LWT. The calibrated model was able to predict the normal load variation during actual LWT tests to a reasonable accuracy. Furthermore, by assuming a previously developed skid number (SN) versus normal load relationship, even the friction profile of the LWT during an actual test was predicted reasonably accurately. Because the skid number (SN) versus normal load relationship had been developed previously using rigorous protocol, a new method that is more practical and convenient was prescribed in this work. This study concluded that higher pavement long-wave roughness decreases the value of the SP compared to a pavement with identical MPD but lower roughness. Finally, the magnitude of the loss of friction was found to be governed by the non-linear skid number versus normal load characteristics of a pavement.
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Mangin, Steven F. "Development of an Equation Independent of Manning's Coefficient n for Depth Prediction in Partially-Filled Circular Culverts." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1284488143.

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Levin, Ori. "Stability analysis and transition prediction of wall-bounded flows." Licentiate thesis, KTH, Mechanics, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1663.

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Disturbances introduced in wall-bounded .ows can grow andlead to transition from laminar to turbulent .ow. In order toreduce losses or enhance mixing in energy systems, afundamental understanding of the .ow stability is important. Inlow disturbance environments, the typical path to transition isan exponential growth of modal waves. On the other hand, inlarge disturbance environments, such as in the presence of highlevels of free-stream turbulence or surface roughness,algebraic growth of non-modal streaks can lead to transition.In the present work, the stability of wall-bounded .ows isinvestigated by means of linear stability equations valid bothfor the exponential and algebraic growth scenario. Anadjoint-based optimization technique is used to optimize thealgebraic growth of streaks. The exponential growth of waves ismaximized in the sense that the envelope of the most ampli.edeigenmode is calculated. Two wall-bounded .ows areinvestigated, the Falkner–Skan boundary layer subject tofavorable, adverse and zero pressure gradients and the Blasiuswall jet. For the Falkner–Skan boundary layer, theoptimization is carried out over the initial streamwiselocation as well as the spanwise wave number and the angularfrequency. Furthermore, a uni.ed transition-prediction methodbased on available experimental data is suggested. The Blasiuswall jet is matched to the measured .ow in an experimentalwall-jet facility. Linear stability analysis with respect tothe growth of two-dimensional waves and streamwise streaks areperformed and compared to the experiments. The nonlinearinteraction of introduced waves and streaks and the .owstructures preceding the .ow breakdown are investigated bymeans of direct numerical simulations.

Descriptors: Boundary layer, wall jet, algebraic growth,exponential growth, lift-up e.ect, streamwise streaks,Tollmien-Schlichting waves, free-stream turbulence, roughnesselement, transition prediction, Parabolized StabilityEquations, Direct Numerical Simulation.

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Книги з теми "Roughness prediction"

1

Fox, Christopher Gene. Description, analysis and predictions of sea floor roughness using spectral models. Bay St. Louis, Miss: Naval Oceanographic Office, 1985.

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2

Kurlanda, Marian Henryk. Predicting roughness progression of asphalt overlays: Joint C-SHRP/Alberta Bayesian application. Ottawa: Canadian Strategic Highway Research Program, Transportation Association of Canada, 1995.

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Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190676889.013.16.

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As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
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Chan, Johnny C. L. Physical Mechanisms Responsible for Track Changes and Rainfall Distributions Associated with Tropical Cyclone Landfall. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780190699420.013.16.

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Анотація:
As a tropical cyclone approaches land, its interaction with the characteristics of the land (surface roughness, topography, moisture availability, etc.) will lead to changes in its track as well as the rainfall and wind distributions near its landfall location. Accurate predictions of such changes are important in issuing warnings and disaster preparedness. In this chapter, the basic physical mechanisms that cause changes in the track and rainfall distributions when a tropical cyclone is about to make landfall are presented. These mechanisms are derived based on studies from both observations and idealized simulations. While the latter are relatively simple, they can isolate the fundamental and underlying physical processes that are inherent when an interaction between the land and the tropical cyclone circulation takes place. These processes are important in assessing the performance of the forecast models, and hence could help improve the model predictions and subsequently disaster preparedness.
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McAdams, Stephen, and Bruno L. Giordano. The perception of musical timbre. Edited by Susan Hallam, Ian Cross, and Michael Thaut. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199298457.013.0007.

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This article discusses musical-timbre perception. Musical timbre is a combination of continuous perceptual dimensions and discrete features to which listeners are differentially sensitive. The continuous dimensions often have quantifiable acoustic correlates. The timbre-space representation is a powerful psychological model that allows predictions to be made about timbre perception in situations beyond those used to derive the model in the first place. Timbre can play a role in larger-scale movements of tension and relaxation and thus contribute to the expression inherent in musical form. Under conditions of high blend among instruments composing a vertical sonority, timbral roughness is a major component of musical tension. However, it strongly depends on the way auditory grouping processes have parsed the incoming acoustic information into events and streams.
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Частини книг з теми "Roughness prediction"

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Trung, Do Duc, Nhu Tung Nguyen, Hoang Tien Dung, Nguyen Van Thien, Tran Thi Hong, Tran Ngoc Giang, Nguyen Thanh Tu, and Le Xuan Hung. "A Study on Prediction of Grinding Surface Roughness." In Advances in Engineering Research and Application, 102–11. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64719-3_13.

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Sreekantan, P. G., and G. V. Ramana. "Roughness based prediction of geofoam interfaces with concrete." In Geosynthetics: Leading the Way to a Resilient Planet, 580–85. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003386889-61.

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Yan, Tingxu, Huiping Zhu, Xudong Liu, Xu Tu, Muran Qi, Yifeng Wang, and Xiaobo Li. "Wetting Behavior of LBE on 316L and T91 Surfaces with Different Roughness." In Springer Proceedings in Physics, 468–79. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_41.

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AbstractIn this paper, two typical candidate structural materials of 316L and T91 with different surface roughnesses were studied at temperatures from 200–500 ℃. The surface with different roughness was prepared by mechanical polishing on the sandpapers with particle sizes from 400 to 2000 mesh. The wetting test was carried out in a smart contact angle measuring device by using the sessile-drop method. Meanwhile, the microstructure of the liquid-solid surface was analyzed by scanning electron microscope (SEM). The results show that the surfaces of both materials are non-wetting to LBE in the tested temperature range. The contact angles of LBE drop on material surfaces decrease with increasing temperature in general. However, it appears to increase significantly at 400 ℃ for both two materials. Besides, the decrease of surface roughness can effectively inhibit the wettability of LBE on the material surface. In addition, compared with 316L, the wetting of the LBE to T91 surface is better, indicating the higher tendency of LME for T91 in practical application. These results can provide references for the prediction of the LME behavior of structural materials.
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Trung, Do Duc, Nguyen Nhu Tung, Nguyen Hong Son, Tran Thi Hong, Nguyen Van Cuong, Vu Nhu Nguyet, and Ngoc Pi Vu. "Prediction of Surface Roughness in Turning with Diamond Insert." In Advances in Engineering Research and Application, 607–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37497-6_69.

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Chen, Ying, Yanhong Sun, Han Lin, and Bing Zhang. "Prediction Model of Milling Surface Roughness Based on Genetic Algorithms." In Advances in Intelligent Systems and Computing, 1315–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15235-2_179.

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Ibrahim, Musa Alhaji, and Yusuf Şahin. "Surface Roughness Modelling and Prediction Using Artificial Intelligence Based Models." In Advances in Intelligent Systems and Computing, 33–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35249-3_3.

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Ding, Ning, Long Shan Wang, and Guang Fu Li. "Study of Intelligent Prediction Control of Surface Roughness in Grinding." In Advances in Abrasive Technology IX, 93–98. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-416-2.93.

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Osuri, Krishna K., U. C. Mohanty, and A. Routray. "Role of Surface Roughness Length on Simulation of Cyclone Aila." In Monitoring and Prediction of Tropical Cyclones in the Indian Ocean and Climate Change, 255–62. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7720-0_22.

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Kumar, M. A. Vinod. "Surface Roughness Prediction Using ANFIS and Validation with Advanced Regression Algorithms." In Advances in Intelligent Systems and Computing, 238–45. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51156-2_29.

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Tripathi, Akshay, and Rohit Singla. "Surface Roughness Prediction of 3D Printed Surface Using Artificial Neural Networks." In Lecture Notes in Mechanical Engineering, 109–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9956-9_11.

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Тези доповідей конференцій з теми "Roughness prediction"

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Wu, Dazhong, Yupeng Wei, and Janis Terpenny. "Surface Roughness Prediction in Additive Manufacturing Using Machine Learning." In ASME 2018 13th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/msec2018-6501.

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To realize high quality, additively manufactured parts, real-time process monitoring and advanced predictive modeling tools are crucial for accelerating quality assurance and quality control in additive manufacturing. While previous research has demonstrated the effectiveness of physics- and model-based diagnosis and prognosis for additive manufacturing, very little research has been reported on real-time monitoring and prediction of surface roughness in fused deposition modeling (FDM). This paper presents a new data-driven approach to surface roughness prediction in FDM. A real-time monitoring system is developed to monitor the health condition of a 3D printer and FDM processes using multiple sensors. A predictive model is built by random forests (RFs). Experimental results have shown that the predictive model is capable of predicting the surface roughness of a printed part with very high accuracy.
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Rami´rez, M. de J., M. Correa, C. Rodri´guez, and J. R. Alique. "Surface Roughness Modeling Based on Surface Roughness Feature Concept for High Speed Machining." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82256.

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This paper explains the concepts to develop a Model of Surface Roughness in order to help researchers to model predictors for high speed machining, also a concept of a surface roughness feature (RaF) is introduced. A RaF is an information piece that shows the factors used by a Ra prediction technique associate with a specific geometric feature. The surface roughness information model is a repository of the RaFs designed to focus on particular workpiece geometries. The Ra predictor developer can design the content of the Ra information model according with his Ra prediction technique to be developed. Each RaF matches with a prediction technique to form RaF predictors and they are united to form a general Ra predictor for the entire workpiece profile.
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"Roughness Prediction For FDM Produced Surfaces." In International Conference Recent treads in Engineering & Technology. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214527.

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Zhang, Dingtong, and Ning Ding. "Surface Roughness Intelligent Prediction on Grinding." In 3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015). Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/ic3me-15.2015.415.

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Agarwal, Sanjay, and P. Venkateswara Rao. "Surface Roughness Prediction Model for Ceramic Grinding." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-79180.

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Surface quality of workpiece during ceramic grinding is an ever-increasing concern in industries now-a-days. Every industry cares to produce products with supposedly better surface finish. The importance of the surface finish of a product depends upon its functional requirements. Since surface finish is governed by many factors and its experimental determination is laborious and time consuming. So the establishment of a model for the reliable prediction of surface roughness is still a key issue for ceramic grinding. In this study, a new analytical surface roughness model is developed on the basis of stochastic nature of the grinding process, governed mainly by the random geometry and the random distribution of cutting edges. The new model proposed for evaluating the surface roughness during ceramic grinding appears to yield results, which agree reasonably well with the experimental results. This model is found to be more accurate in predicting the surface roughness when compared to the existing model.
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Hanson, David, and Michael Kinzel. "An Improved CFD Approach for Ice-Accretion Prediction Using the Discrete Element Roughness Method." In ASME 2017 Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/fedsm2017-69365.

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Ice-shape prediction results are shown wherein Discrete-Element Roughness Method (DERM)-based CFD solutions are coupled with LEWICE to supplement the built-in heat transfer prediction module. This coupling produces multi-step ice-shape predictions. The effect of using the newer roughness-height distribution model of Han and Palacios rather than the roughness-height prediction of LEWICE is also gauged. DERM is used in an attempt to improve heat transfer predictions beyond the capability of a sand-grain-roughness model while only slightly increasing the computation time. LEWICE is the industry-standard ice growth prediction tool maintained by NASA. LEWICE is known to predict ice shapes very accurately within its validation envelope, but suffers lowered accuracy for icing conditions in the glaze regime. The predictions that result from the DERM-LEWICE coupling are compared with ice shapes generated in experiments from the Penn State Adverse Environment Rotor Test Stand (AERTS). It is observed that ice-shape predictions in the glaze-icing regime can be highly sensitive to the convective heat-transfer predictions.
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Wang, Xin, and Emil M. Petriu. "Neural fractal prediction of three dimensional surface roughness." In 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA). IEEE, 2011. http://dx.doi.org/10.1109/cimsa.2011.6059937.

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Beaugendre, Heloise, and Francois Morency. "FENSAP-ICE: Roughness Effects on Ice Accretion Prediction." In 41st Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-1222.

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Tezok, Fatih, Fassi Kafyeke, and Tuncer Cebeci. "Prediction of airfoil performance with leading edge roughness." In AIAA and SAE, 1998 World Aviation Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-5544.

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Alexandrov, Sergei, Ken-ichi Manabe, and Tsuyoshi Furushima. "Free Surface Roughness Prediction in Bending Under Tension." In THE 8TH INTERNATIONAL CONFERENCE AND WORKSHOP ON NUMERICAL SIMULATION OF 3D SHEET METAL FORMING PROCESSES (NUMISHEET 2011). AIP, 2011. http://dx.doi.org/10.1063/1.3623735.

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Звіти організацій з теми "Roughness prediction"

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Taylor, R. P., and B. K. Hodge. Validated heat-transfer and pressure-drop prediction methods based on the discrete element method: Phase 1, Three-dimensiional roughness. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/10154300.

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Taylor, R. P., and B. K. Hodge. Validated heat-transfer and pressure-drop prediction methods based on the discrete element method: Phase 1, Three-dimensiional roughness. Office of Scientific and Technical Information (OSTI), February 1992. http://dx.doi.org/10.2172/5096745.

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James, C. A., B. K. Hodge, and R. P. Taylor. Validated heat-transfer and pressure-drop prediction methods based on the discrete-element method: Phase 2, two-dimensional rib roughness. Office of Scientific and Technical Information (OSTI), May 1993. http://dx.doi.org/10.2172/10192770.

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Thegeya, Aaron, Thomas Mitterling, Arturo Martinez Jr, Joseph Albert Niño Bulan, Ron Lester Durante, and Jayzon Mag-atas. Application of Machine Learning Algorithms on Satellite Imagery for Road Quality Monitoring: An Alternative Approach to Road Quality Surveys. Asian Development Bank, December 2022. http://dx.doi.org/10.22617/wps220587-2.

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This paper examines the feasibility of using satellite imagery and artificial intelligence to develop an efficient and cost-effective way to determine and predict the condition of roads in the Asia and Pacific region. The paper notes that collecting information on road quality is difficult, particularly in harder to reach middle- and low-income areas, and explains why this method offers an alternative. It shows how the study’s preliminary algorithm was created using satellite imagery and existing road roughness data from the Philippines. It assesses the accuracy rate and finds it sufficient for the preliminary identification of poor to bad roads. It notes that additional enhancements are needed to increase its prediction accuracy and make it more robust.
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Al-Qadi, Imad, Jaime Hernandez, Angeli Jayme, Mojtaba Ziyadi, Erman Gungor, Seunggu Kang, John Harvey, et al. The Impact of Wide-Base Tires on Pavement—A National Study. Illinois Center for Transportation, October 2021. http://dx.doi.org/10.36501/0197-9191/21-035.

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Researchers have been studying wide-base tires for over two decades, but no evidence has been provided regarding the net benefit of this tire technology. In this study, a comprehensive approach is used to compare new-generation wide-base tires (NG-WBT) with the dual-tire assembly (DTA). Numerical modeling, prediction methods, experimental measurements, and environmental impact assessment were combined to provide recommendations about the use of NG-WBT. A finite element approach, considering variables usually omitted in the conventional analysis of flexible pavement was utilized for modeling. Five hundred seventy-six cases combining layer thickness, material properties, tire load, tire inflation pressure, and pavement type (thick and thin) were analyzed to obtained critical pavement responses. A prediction tool, known as ICT-Wide, was developed based on artificial neural networks to obtain critical pavement responses in cases outside the finite element analysis matrix. The environmental impacts were determined using life cycle assessment. Based on the bottom-up fatigue cracking, permanent deformation, and international roughness index, the life cycle energy consumption, cost, and green-house gas (GHG) emissions were estimated. To make the outcome of this research effort useful for state departments of transportation and practitioners, a modification to AASHTOWare is proposed to account for NG-WBT. The revision is based on two adjustment factors, one accounting for the discrepancy between the AASHTOware approach and the finite element model of this study, and the other addressing the impact of NG-WBT.
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Michaels, Michelle, Theodore Letcher, Sandra LeGrand, Nicholas Webb, and Justin Putnam. Implementation of an albedo-based drag partition into the WRF-Chem v4.1 AFWA dust emission module. Engineer Research and Development Center (U.S.), January 2021. http://dx.doi.org/10.21079/11681/42782.

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Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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LeGrand, Sandra, Theodore Letcher, Gregory Okin, Nicholas Webb, Alex Gallagher, Saroj Dhital, Taylor Hodgdon, Nancy Ziegler, and Michelle Michaels. Application of a satellite-retrieved sheltering parameterization (v1.0) for dust event simulation with WRF-Chem v4.1. Engineer Research and Development Center (U.S.), May 2023. http://dx.doi.org/10.21079/11681/47116.

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Анотація:
Employing numerical prediction models can be a powerful tool for forecasting air quality and visibility hazards related to dust events. However, these numerical models are sensitive to surface conditions. Roughness features (e.g., rocks, vegetation, furrows, etc.) that shelter or attenuate wind flow over the soil surface affect the magnitude and spatial distribution of dust emission. To aide in simulating the emission phase of dust transport, we used a previously published albedo-based drag partition parameterization to better represent the component of wind friction speed affecting the immediate soil sur-face. This report serves as a guide for integrating this parameterization into the Weather Research and Forecasting with Chemistry (WRF-Chem) model. We include the procedure for preprocessing the required input data, as well as the code modifications for the Air Force Weather Agency (AFWA) dust emission module. In addition, we provide an example demonstration of output data from a simulation of a dust event that occurred in the Southwestern United States, which incorporates use of the drag partition.
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Ziegler, Nancy, Nicholas Webb, Adrian Chappell, and Sandra LeGrand. Scale invariance of albedo-based wind friction velocity. Engineer Research and Development Center (U.S.), May 2021. http://dx.doi.org/10.21079/11681/40499.

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Obtaining reliable estimates of aerodynamic roughness is necessary to interpret and accurately predict aeolian sediment transport dynamics. However, inherent uncertainties in field measurements and models of surface aerodynamic properties continue to undermine aeolian research, monitoring, and dust modeling. A new relation between aerodynamic shelter and land surface shadow has been established at the wind tunnel scale, enabling the potential for estimates of wind erosion and dust emission to be obtained across scales from albedo data. Here, we compare estimates of wind friction velocity (u*) derived from traditional methods (wind speed profiles) with those derived from the albedo model at two separate scales using bare soil patch (via net radiometers) and landscape (via MODIS 500 m) datasets. Results show that profile-derived estimates of u* are highly variable in anisotropic surface roughness due to changes in wind direction and fetch. Wind speed profiles poorly estimate soil surface (bed) wind friction velocities necessary for aeolian sediment transport research and modeling. Albedo-based estimates of u* at both scales have small variability because the estimate is integrated over a defined, fixed area and resolves the partition of wind momentum be-tween roughness elements and the soil surface. We demonstrate that the wind tunnel-based calibration of albedo for predicting wind friction velocities at the soil surface (us*) is applicable across scales. The albedo-based approach enables consistent and reliable drag partition correction across scales for model and field estimates of us* necessary for wind erosion and dust emission modeling.
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Ziegler, Nancy, Nicholas Webb, John Gillies, Brandon Edward, George Nikolich, Justin Van Zee, Brad Cooper, Dawn Browning, Ericha Courtright, and Sandra LeGrand. Plant phenology drives seasonal changes in shear stress partitioning in a semi-arid rangeland. Engineer Research and Development Center (U.S.), September 2023. http://dx.doi.org/10.21079/11681/47680.

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Accurate representation of surface roughness in predictive models of aeolian sediment transport and dust emission is required for model accuracy. While past studies have examined roughness effects on drag partitioning, the spatial and temporal variability of surface shear velocity and the shear stress ratio remain poorly described. Here, we use a four-month dataset of total shear velocity (u*) and soil surface shear velocity (us*) measurements to examine the spatiotemporal variability of the shear stress ratio (R) before, during, and after vegetation green-up at a honey mesquite (Prosopis glandulosa Torr.) shrub-invaded grassland in the Chihuahuan Desert, New Mexico, USA. Results show that vegetation green-up, the emergence of leaves, led to increased drag and surface aerodynamic sheltering and a reduction in us* and R magnitude and variability. We found that us* decreased from 20% to 5% of u* as the vegetation form drag and its sheltering effect increased. Similarly, the spatiotemporal variability of R was found to be linked directly to plant phenological phases. We conclude that drag partition schemes should incorporate seasonal vegetation change, via dynamic drag coefficients and/or R, to accurately predict the timing and magnitude of seasonal aeolian sediment fluxes.
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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.

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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is a matter of great concern, especially in arid, semi-arid regions, e.g. much of Israel and vast area in US, where water is a limiting factor for crop production. A major reason for runoff of rain and overhead irrigation water is the structural crust that is generated over a bare soils surface during rainfall or overhead irrigation events and reduces its infiltration rate (IR), considerably. IR data is essential for predicting the amount of percolating rainwater and runoff. Available information on in situ infiltration rate and crust strength is necessary for the farmers to consider: when it is necessary to cultivate for breaking the soil crust, crust strength and seedlings emergence, precision farming, etc. To date, soil IR is measured in the laboratory and in small-scale field plots, using rainfall simulators. This method is tedious and consumes considerable resources. Therefore, an available, non-destructive-in situ methods for soil IR and soil crusting levels evaluations, are essential for the verification of infiltration and runoff models and the evaluation of the amount of available water in the soil. In this research, soil samples from the US and Israel were subjected to simulated rainstorms of increasing levels of cumulative energies, during which IR (crusting levels) were measured. The soils from the US were studied simultaneously in the US and in Israel in order to compare the effect of the methodology on the results. The soil surface reflectance was remotely measured, using laboratory and portable spectrometers in the VIS-NIR and SWIR spectral region (0.4-2.5mm). A correlation coefficient spectra in which the wavelength, consisting of the higher correlation, was selected to hold the highest linear correlation between the spectroscopy and the infiltration rate. There does not appear to be a single wavelength that will be best for all soils. The results with the six soils in both countries indeed showed that there is a significant correlation between the infiltration rate of crusted soils and their reflectance values. Regarding the wavelength with the highest correlation for each soil, it is likely that either a combined analysis with more then one wavelength or several "best" wavelengths will be found that will provide useful data on soil surface condition and infiltration rate. The product of this work will serve as a model for predicting infiltration rate and crusting levels solely from the reflectance readings. Developing the aforementioned methodologies will allow increased utilization of rain and irrigation water, reduced runoff, floods and soil erosion hazards, reduced seedlings emergence problems and increased plants stand and yields.
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