The effect of surface waviness on friction between Neolite and quarry tiles

Friction is widely used as an indicator of surface slipperiness in preventing accidents in slips and falls. Surface texture affects friction, but it is not clear which surface characteristics are better correlated with friction. Highly correlated surface characteristics could be used as potential interventions to prevent slip and fall accidents. The dynamic friction between quarry tiles and a commonly used sole testing material, Neolite, using three different mixtures of glycerol and water as contaminants at the interface was correlated with the surface parameters of the tile surfaces. The surface texture was quantified with various surface roughness and surface waviness parameters using three different cut-off lengths to filter the measured profiles for obtaining the profiles of either surface roughness or surface waviness. The correlation coefficients between the surface parameters and the measured friction were affected by the glycerol contents and cut-off lengths. Surface waviness parameters could potentially be better indicators of friction than commonly used surface roughness parameters, especially when they were measured with commonly used cut-off lengths or when the viscosity of the liquid contaminant was high.     

The effect of surface waviness on friction between Neolite and quarry tiles

Friction is widely used as an indicator of surface slipperiness in preventing accidents in slips and falls. Surface texture affects friction, but it is not clear which surface characteristics are better correlated with friction. Highly correlated surface characteristics could be used as potential interventions to prevent slip and fall accidents. The dynamic friction between quarry tiles and a commonly used sole testing material, Neolite, using three different mixtures of glycerol and water as contaminants at the interface was correlated with the surface parameters of the tile surfaces. The surface texture was quantified with various surface roughness and surface waviness parameters using three different cut-off lengths to filter the measured profiles for obtaining the profiles of either surface roughness or surface waviness. The correlation coefficients between the surface parameters and the measured friction were affected by the glycerol contents and cut-off lengths. Surface waviness parameters could potentially be better indicators of friction than commonly used surface roughness parameters, especially when they were measured with commonly used cut-off lengths or when the viscosity of the liquid contaminant was high.     

The effect of surface roughness and contaminant on the dynamic friction of porcelain tile

Surface roughness affects friction, but it is not clear what surface roughness characteristics are better correlated with friction. The average of the maximum height above the mean line in each cut-off length (Rpm) and the arithmetical average of surface slope (deltaa) had the highest correlation with dynamic friction coefficient in a previous study. The previous study was expanded to two different footwear materials and four different contaminants on a porcelain tile in the current investigation. The results showed that dynamic friction decreased as the interface speed and glycerol content in the contaminant were increased due to the hydrodynamic lubrication effect. Deltaa had the highest correlation with friction for most of the test conditions with neolite. For Four S rubber, friction coefficient appeared to have the highest correlation with the parameters related to the surface void volume at 30% glycerol content, related to the surface slope at 70 and 85% glycerol contents, and related to the peak to valley distance at 99% glycerol content. A good indicator of surface slip resistance probably should consist of the surface parameters representing the surface slope, the surface void volume and the surface peak-to-valley distance with the coefficients determined by the system parameters.     

Optimization studies in high speed turning of Ti-6Al-4V

Surface roughness is a major concern to the present manufacturing sector without the wastage of material. Hence, in order to achieve good surface roughness and reduce production time, optimization is necessary. In this study optimization techniques based on swarm intelligence (SI) namely firefly algorithm (FA), particle swarm optimization (PSO) and a newly introduced metaheuristic algorithm namely bat algorithm (BA) has been implemented for optimizing machining parameters namely cutting speed, feed rate, depth of cut and tool flank wear and cutting tool vibrations in order to achieve minimum surface roughness. Two parameters R_a and R_t have been considered for evaluating the surface roughness. The performance of BA algorithm has been compared with FA algorithm and PSO, which is a commonly and widely used optimization algorithm in machining. The results conclude that BA produces better optimization, when compared to FA and PSO. Based on the literature review carried out, this work is a first attempt at using a metaheuristic algorithm namely BA in machining applications.     

The role of surface roughness in the measurement of slipperiness

Surface roughness has been shown to have substantial effects on the slip resistance between shoe heels and floor surfaces under various types of walking environments. This paper summarizes comprehensive views of the current understanding on the roles of surface roughness on the shoe and floor surfaces in the measurement of slipperiness and discusses promising directions for future research. Various techniques and instruments for surface roughness measurements and related roughness parameters are reviewed in depth. It is suggested that a stylus-type profilometer and a laser scanning confocal microscope are the preferred instruments for surface roughness measurements in the field and laboratory, respectively. The need for developing enhanced methods for reliably characterizing the slip resistance properties is highlighted. This could be based on the principal understanding of the nature of shoe and floor interface and surface analysis techniques for characterizing both surfaces of shoe and floor. Therefore, surface roughness on both shoe and floor surfaces should be measured and combined to arrive at the final assessment of slipperiness. While controversies around the friction measurement for slipperiness assessment still remain, surface roughness measurement may provide an objective alternative to overcoming the limitations of friction measurements.     

Retrieving photometric properties of desert surfaces in China using the Hapke model and MISR data

The retrieval of photometric properties of desert surfaces is an important first step in the parameterization of land surface components of regional dust emission and global radiation models and in Earth system modeling. In this study, the values of Hapke's photometric parameters (ω, h, b, c, B0, and θ?) were retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) instrument at locations in China's deserts. Four pixels represented the typical surface characteristics of the Taklimakan Desert, sand dunes of Kumtag Desert, relatively smooth areas of the Kumtag Desert and the aeolian sandy soil of Loulan. In contrast to earlier studies, we found that the retrieved parameter values were largely affected by the initial value. To combat this problem we used a Monte Carlo method with physical constraints and a conformity indicator to ensure physically meaningful inversion.The results showed that the angular domain of MISR observations was sufficiently large to determine confidently the values of Hapke's photometric parameters with the exception of the opposition effect width (h). Retrieved values for the single scattering albedo (ω) and macroscopic roughness (θ?) were consistent with qualitative observations about the structure and composition of the surface material and the nature of the dune forms, respectively. At Loulan, where the surface was smoother than other sites, retrieved values exhibited the strongest backward scattering. These results indicated that at the sensor scale, a rough surface (e.g., dunes) does not necessarily mean more backward scattering than a smooth surface. This finding has significant implications for empirical methods (e.g., using the normalized index of backward-scattered radiance minus forward-scattered radiance as an indicator to indicate surface roughness) which should be used carefully for analyzing surface roughness from remote sensing data. Future research is needed to 1) understand how surface roughness at the sub-pixel scale modifies the angular characteristics of reflectance and to 2) find practical methods for rapid whole image processing for mapping the photometric parameters.     

Effects of disk surface roughness on static flying characteristics of air bearing slider by using a combined method of Reynolds equation and rough disk surface

Reynolds equation was modified with adding the surface roughness parameters to analyze the effects of disk surface roughness on the static flying characteristic of an air bearing slider. However, the modification demands the complicated mathematical expressions and related knowledge of physics and mathematics. In this paper, a combined method of Reynolds equation without introducing the roughness parameters and rough disk surface is proposed to investigate the effects of disk surface roughness on the static flying characteristics of an air bearing slider, it is different from those models of modified Reynolds equation introducing the disk surface roughness used by many researchers. More importantly, this method avoids the complicated numerical calculation resulted from the mathematical expressions including the Peklenik parameter \(\gamma\) and roughness R_a. By using an Ω air bearing slider, we investigated the effects of disk surface roughness on the static flying characteristics of this slider, the results show that the Peklenik parameter \(\gamma\) and roughness R_a have a significant influence on the pressure distribution, the load carrying capacity and the location of the pressure centre.     

Evaluation of the SMOS L-MEB passive microwave soil moisture retrieval algorithm

Soil moisture will be mapped globally by the European Soil Moisture and Ocean Salinity (SMOS) mission to be launched in 2009. The expected soil moisture accuracy will be 4.0 %v/v. The core component of the SMOS soil moisture retrieval algorithm is the L-band Microwave Emission of the Biosphere (L-MEB) model which simulates the microwave emission at L-band from the soil-vegetation layer. The model parameters have been calibrated with data acquired by tower mounted radiometer studies in Europe and the United States, with a typical footprint size of approximately 10 m. In this study, aircraft L-band data acquired during the National Airborne Field Experiment (NAFE) intensive campaign held in South-eastern Australia in 2005 are used to perform the first evaluation of the L-MEB model and its proposed parameterization when applied to coarser footprints (62.5 m). The model could be evaluated across large areas including a wide range of land surface conditions, typical of the Australian environment. Soil moisture was retrieved from the aircraft brightness temperatures using L-MEB and ground measured ancillary data (soil temperature, soil texture, vegetation water content and surface roughness) and subsequently evaluated against ground measurements of soil moisture. The retrieval accuracy when using the L-MEB ‘default’ set of model parameters was found to be better than 4.0 %v/v only over grassland covered sites. Over crops the model was found to underestimate soil moisture by up to 32 %v/v. After site specific calibration of the vegetation and roughness parameters, the retrieval accuracy was found to be equal or better than 4.8 %v/v for crops and grasslands at 62.5-m resolution. It is suggested that the proposed value of roughness parameter H_R for crops is too low, and that variability of H_R with soil moisture must be taken into consideration to obtain accurate retrievals at these scales. The analysis presented here is a crucial step towards validating the application of L-MEB for soil moisture retrieval from satellite observations in an operational context.     

Estimating aerodynamic resistance of rough surfaces using angular reflectance

Current wind erosion and dust emission models neglect the heterogeneous nature of surface roughness and its geometric anisotropic effect on aerodynamic resistance, and over-estimate the erodible area by assuming it is not covered by roughness elements. We address these shortfalls with a new model which estimates aerodynamic roughness length (z₀) using angular reflectance of a rough surface. The new model is proportional to the frontal area index, directional, and represents the geometric anisotropy of z₀. The model explained most of the variation in two sets of wind tunnel measurements of aerodynamic roughness lengths (z₀). Field estimates of z₀ for varying wind directions were similar to predictions made by the new model. The model was used to estimate the erodible area exposed to abrasion by saltating particles. Vertically integrated horizontal flux (F_h) was calculated using the area not covered by non-erodible hemispheres; the approach embodied in dust emission models. Under the same model conditions, F_h estimated using the new model was up to 85% smaller than that using the conventional area not covered. These F_h simulations imply that wind erosion and dust emission models without geometric anisotropic sheltering of the surface, may considerably over-estimate F_h and hence the amount of dust emission. The new model provides a straightforward method to estimate aerodynamic resistance with the potential to improve the accuracy of wind erosion and dust emission models, a measure that can be retrieved using bi-directional reflectance models from angular satellite sensors, and an alternative to notoriously unreliable field estimates of z₀ and their extrapolations across landform scales.     

Investigation of surface roughness in the milling of Al7075 and open-cell SiC foam composite and optimization of machining parameters

In the present study, aluminum alloy 7075 (Al7075)-based open-cell silicon carbide (SiC) foam composite was fabricated and the machinability of both Al7075 and the open-cell SiC foam Al metal matrix composite was investigated during milling using an uncoated carbide tool. The machining trials were conducted using the Taguchi L₂₇ full-factorial orthogonal array, and the milling parameters were optimized for surface roughness. Analysis of variance was employed to determine the effect of the cutting variables on surface roughness. The experimental results were evaluated by signal-to-noise ratio, 3D surface graphs, artificial neural networks (ANNs) and main effect graphs. The analysis results show that the feed rate was the most significant milling parameter affecting surface roughness of both Al7075 and the open-cell SiC foam composite. Prediction models have been developed for the surface roughness through regression analysis and ANNs. Confirmation experiments were performed to identify the performance of mathematical models, and the surface roughness was predicted with a mean squared error equal to 1.6 and 0.24 % in the milling of Al7075 and open-cell SiC foam composite, respectively. The test result showed that the three-dimensional open-pore SiC foam network reinforcement was restricted the movement of the soft matrix and provided an acceptable surface quality in the milling of MMCs.

     

Artificial neural network models for the prediction of surface roughness in electrical discharge machining

In the present paper Artificial Neural Networks (ANNs) models are proposed for the prediction of surface roughness in Electrical Discharge Machining (EDM). For this purpose two well-known programs, namely Matlab^® with associated toolboxes, as well as Netlab^®, were emplo- yed. Training of the models was performed with data from an extensive series of EDM experiments on steel grades; the proposed models use the pulse current, the pulse duration, and the processed material as input parameters. The reported results indicate that the proposed ANNs models can satisfactorily predict the surface roughness in EDM. Moreover, they can be considered as valuable tools for the process planning for EDMachining.     

Using artificial intelligence models for the prediction of surface wear based on surface isotropy levels

Currently, a key industrial challenge in friction processes is the prediction of surface roughness and loss of mass under different machining processes, such as Electro-Discharge Machining (EDM), and turning and grinding processes. Under industrial conditions, only the sliding distance is easily evaluated in friction processes, while the acquisition of other variables usually implies expensive costs for production centres, such as the integration of sensors in functioning machine-tools. Besides, appropriate datasets are usually very small, because the testing of different friction conditions is also expensive. These two restrictions, small datasets and very few inputs, make it very difficult to use Artificial Intelligence (AI) techniques to model the industrial problem. So, the use of the isotropy level of the surface structure is proposed, as another input that is easily evaluated prior to the friction process. In this example, the friction processes of a cubic sample of 102Cr6 (40 HRC) steel and a further element made of X210Cr12 (60 HRC) steel are considered. Different artificial intelligence techniques, such as artificial regression trees, multilayer perceptrons (MLPs), radial basis networks (RBFs), and Random Forest, were tested considering the isotropy level as either a nominal or a numeric attribute, to evaluate improvements in the accuracy of surface roughness and loss-of-mass predictions. The results obtained with real datasets showed that RBFs and MLPs provided the most accurate models for loss of mass and surface roughness prediction, respectively. MLPs have slightly higher surface prediction accuracy than Random Forest, although MLP models are very sensitive to the tuning of their parameters (a small mismatch between the learning rate and the momentum in the MLP will drastically reduce the accuracy of the model). In contrast, Random Forest has no parameter to be tuned and its prediction is almost as good as MLPs for surface roughness, so Random Forest will be more suitable for industrial use where no expert in AI model tuning is available. Moreover, the inclusion of the isotropy level in the dataset, especially as a numeric attribute, greatly improved the accuracy of the models, in some cases, by up to 52% for MLPs, and by a smaller proportion of 16% in the Random Forest models in terms of Root Mean Square Error. Finally, Random Forest ensembles only trained with low and very high isotropy level experimental datasets generated reliable models for medium levels of isotropy, thereby offering a solution to reduce the size of training datasets.     

Recurrent ANN-based modelling of the dynamic evolution of the surface roughness in grinding

Grinding is critical in modern manufacturing due to its capacity for producing high surface quality and high-precision parts. One of the most important parameters that indicate the grinding quality is the surface roughness (R _a). Analytical models developed to predict surface finish are not easy to apply in the industry. Therefore, many researchers have made use of artificial neural networks. However, all the approaches provide a particular solution for a wheel–workpiece pair, not generalizing to new grinding wheels. Besides, these solutions do not give surface roughness values related to the grinding wheel status. Therefore, in this work the modelling of the dynamic evolution of the surface roughness (R _a) based on recurrent neural networks is presented with the capability to generalize to new grinding wheels and conditions taking into account the wheel wear. Results show excellent prediction of the surface finish dynamic evolution. The absolute maximum error is below 0.49 µm, being the average error around 0.32 µm. Besides, the analysis of the relative importance of the inputs shows that the grinding conditions have higher influence than the wheel characteristics over the prediction of the surface roughness confirming experimental knowledge of grinding technology users.

     

Support vector regression based friction modeling and compensation in motion control system

Friction has been experimentally shown to be one of the major sources of performance degradation in motion control system. Although for model-based friction compensation, several sophisticated friction models have been proposed in the literatures, there exists no universally agreed parametric friction model, which by implication has made selection of an appropriate parametric model difficult. More so, accurate determination of the parameters of these sophisticated parametric friction models has been challenging due to complexity of friction nonlinearities. Motivated by the need for a simple, non-parametric based, and yet effective friction compensation in motion control system, an Artificial Intelligent (AI)-based (non-parametric) friction model using v-Support Vector Regression (v-SVR) is proposed in this work to estimate the non-linear friction in a motion control system. Unlike conventional SVR technique, v-SVR is characterized with fewer parameters for its development, and requires less development time. The effectiveness of the developed model in representing and compensating for the frictional effects is evaluated experimentally on a rotary experimental motion system. The performance is benchmarked with three parametric based (Coulomb, Tustin, and Lorentzian) friction models. The results show the v-SVR as a viable and efficient alternative to the parametric-based techniques in representing and compensating friction effects.     

Monitoring root-zone soil moisture through the assimilation of a thermal remote sensing-based soil moisture proxy into a water balance model

Two types of Soil Vegetation Atmosphere Transfer (SVAT) modeling approaches can be applied to monitor root-zone soil moisture in agricultural landscapes. Water and Energy Balance (WEB) SVAT modeling is based on forcing a prognostic root-zone water balance model with observed rainfall and predicted evapotranspiration. In contrast, thermal Remote Sensing (RS) observations of surface radiometric temperature (T_R) are integrated into purely diagnostic RS-SVAT models to predict the onset of vegetation water stress. While RS-SVAT models do not explicitly monitor soil moisture, they can be used in the calculation of thermal-based proxy variables for the availability of soil water in the root zone. Using four growing seasons (2001 to 2004) of profile soil moisture, micro-meteorology, and surface radiometric temperature measurements at the United States Department of Agriculture (USDA) Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) study site in Beltsville, MD, prospects for improving WEB-SVAT root-zone soil water predictions via the assimilation of diagnostic RS-SVAT soil moisture proxy information are examined. Results illustrate the potential advantages of such an assimilation approach relative to the competing approach of directly assimilating T_R measurements. Since T_R measurements used in the analysis are tower-based (and not obtained from a remote platform), a sensitivity analysis demonstrates the potential impact of remote sensing limitations on the value of the RS-SVAT proxy. Overall, results support a potential role for RS-SVAT modeling strategies in improving WEB-SVAT model characterization of root-zone soil moisture.     

Estimating forest canopy fuel parameters using LIDAR data

Fire researchers and resource managers are dependent upon accurate, spatially-explicit forest structure information to support the application of forest fire behavior models. In particular, reliable estimates of several critical forest canopy structure metrics, including canopy bulk density, canopy height, canopy fuel weight, and canopy base height, are required to accurately map the spatial distribution of canopy fuels and model fire behavior over the landscape. The use of airborne laser scanning (LIDAR), a high-resolution active remote sensing technology, provides for accurate and efficient measurement of three-dimensional forest structure over extensive areas. In this study, regression analysis was used to develop predictive models relating a variety of LIDAR-based metrics to the canopy fuel parameters estimated from inventory data collected at plots established within stands of varying condition within Capitol State Forest, in western Washington State. Strong relationships between LIDAR-derived metrics and field-based fuel estimates were found for all parameters [sqrt(crown fuel weight): R²=0.86; ln(crown bulk density): R²=0.84; canopy base height: R²=0.77; canopy height: R²=0.98]. A cross-validation procedure was used to assess the reliability of these models. LIDAR-based fuel prediction models can be used to develop maps of critical canopy fuel parameters over forest areas in the Pacific Northwest.     

Influence of lake morphology and clarity on water surface temperature as measured by EOS ASTER

Relationships between lake morphometric parameters and nighttime lake surface temperatures were investigated in North American temperate lakes using the ASTER kinetic temperature (AST08) product. Nighttime ASTER kinetic temperature measurements were found to be a good analogue for nighttime surface temperatures. Linear regression between ASTER and buoy-measured temperatures in a test lake were better during the evening (R² = 0.98) than the day (R² = 0.90), presumably due to the greater influence of radiation and latent heat fluxes during daylight hours. Nighttime lake surface temperatures measured in three ASTER scenes were significantly correlated to logarithm of lake area, maximum lake depth, Secchi depth (a measure of lake clarity) and lake order (a measure of lake connection with surface drainage), during October and November. Nighttime lake surface temperatures were significantly correlated only with lake area in July. We hypothesize that morphology was more strongly related to surface temperature in the fall months due to lake turnover during that season. This study suggests that satellite derived thermal data may be useful for calculation of lake heat budgets and evaporation rates, provided surface temperatures are measured in well-mixed lakes.     

Predicting the performance measures of a message-passing multiprocessor architecture using artificial neural networks

In this paper, we develop multi-layer feed-forward artificial neural network (MFANN) models for predicting the performance measures of a message-passing multiprocessor architecture interconnected by the simultaneous optical multiprocessor exchange bus (SOME-Bus), which is a fiber-optic interconnection network. OPNET Modeler is used to simulate the SOME-Bus multiprocessor architecture and to create the training and testing datasets. The performance of the MFANN prediction models is evaluated using standard error of estimate (SEE) and multiple correlation coefficient (R). Also, the results of the MFANN models are compared with the ones obtained by generalized regression neural network (GRNN), support vector regression (SVR), and multiple linear regression (MLR). It is shown that MFANN models perform better (i.e., lower SEE and higher R) than GRNN-based, SVR-based, and MLR-based models for predicting the performance measures of a message-passing multiprocessor architecture.     

The experimental investigation of the effects of uncoated,PVD- and CVD-coated cemented carbide inserts and cutting parameters on surface roughness in CNC turning and its prediction using artificial neural networks

In this study the machining of AISI 1030 steel (i.e. orthogonal cutting) uncoated, PVD- and CVD-coated cemented carbide insert with different feed rates of 0.25, 0.30, 0.35, 0.40 and 0.45 mm/rev with the cutting speeds of 100, 200 and 300 m/min by keeping depth of cuts constant (i.e. 2 mm), without using cooling liquids has been accomplished. The surface roughness effects of coating method, coating material, cutting speed and feed rate on the workpiece have been investigated. Among the cutting tools—with 200 mm/min cutting speed and 0.25 mm/rev feed rate—the TiN coated with PVD method has provided 2.16 μm, TiAlN coated with PVD method has provided 2.3 μm, AlTiN coated with PVD method has provided 2.46 μm surface roughness values, respectively. While the uncoated cutting tool with the cutting speed of 100 m/min and 0.25 mm/rev feed rate has yielded the surface roughness value of 2.45 μm. Afterwards, these experimental studies were executed on artificial neural networks (ANN). The training and test data of the ANNs have been prepared using experimental patterns for the surface roughness. In the input layer of the ANNs, the coating tools, feed rate (f) and cutting speed (V) values are used while at the output layer the surface roughness values are used. They are used to train and test multilayered, hierarchically connected and directed networks with varying numbers of the hidden layers using back-propagation scaled conjugate gradient (SCG) and Levenberg–Marquardt (LM) algorithms with the logistic sigmoid transfer function. The experimental values and ANN predictions are compared by statistical error analyzing methods. It is shown that the SCG model with nine neurons in the hidden layer has produced absolute fraction of variance (R²) values about 0.99985 for the training data, and 0.99983 for the test data; root mean square error (RMSE) values are smaller than 0.00265; and mean error percentage (MEP) are about 1.13458 and 1.88698 for the training and test data, respectively. Therefore, the surface roughness value has been determined by the ANN with an acceptable accuracy.