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.     

Linear regression models of floor surface parameters on friction between Neolite and quarry tiles

For slips and falls, friction is widely used as an indicator of surface slipperiness. Surface parameters, including surface roughness and waviness, were shown to influence friction by correlating individual surface parameters with the measured friction. A collective input from multiple surface parameters as a predictor of friction, however, could provide a broader perspective on the contributions from all the surface parameters evaluated. The objective of this study was to develop regression models between the surface parameters and measured friction. The dynamic friction was measured using three different mixtures of glycerol and water as contaminants. Various surface roughness and waviness parameters were measured using three different cut-off lengths. The regression models indicate that the selected surface parameters can predict the measured friction coefficient reliably in most of the glycerol concentrations and cut-off lengths evaluated. The results of the regression models were, in general, consistent with those obtained from the correlation between individual surface parameters and the measured friction in eight out of nine conditions evaluated in this experiment. A hierarchical regression model was further developed to evaluate the cumulative contributions of the surface parameters in the final iteration by adding these parameters to the regression model one at a time from the easiest to measure to the most difficult to measure and evaluating their impacts on the adjusted R² values. For practical purposes, the surface parameter R_a alone would account for the majority of the measured friction even if it did not reach a statistically significant level in some of the regression models.     

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.     

Observation of the floor surface topography changes in pedestrian slip resistance measurements

This study is concerned with the changes of the floor surface topography in the early stage of repetitive wear rubbings and the relationships between slip resistance properties and operationally defined geometric characteristics of the floor surfaces. It was assumed that: (1) alterations in surface topography will be associated with changes in the DFC; and (2) wear process will be accompanied by changes in surface topography. For the analysis of initial characterization on the surface topography, specially prepared, dry and clean metal and perspex specimens were chosen. The surface profiles of the fresh and rubbed flooring specimens were recorded using a laser scanning confocal microscope. From the profile ordinate data read at 1 μm intervals, a number of surface roughness parameters – centre line average, root-mean-square roughness, maximum height, maximum mean peak height, maximum mean depth, and absolute average asperity slope were calculated using a computer program. The skew and the kurtosis of the statistical distribution of each surface profile were also computed. The results indicate that the asperity height and the maximum mean depth were significantly reduced after the friction tests. The average slope of asperities was the parameter that most highly correlated with the dynamic friction coefficient. The analyses also showed that the surface parameters underwent large variations initially, but subsequently these changes were less marked, which was explained by the transition from unsteady-state friction to steady-state friction. These results found that slip resistance properties between the shoes and the floor counterfaces were greatly influenced by the manner in which the geometry of the floor surface was modified. It was suggested that measurement of changes in the surface geometry provides additional information on the analysis of slip resistance and could usefully be reported with friction measurements.

Relevance to industry

Slipping and falling accidents are a major ergonomic and safety concern in the workplace and the general community. Prevention of slip hazard has focused on designing “slip resistant” footwear and floor surfaces. This study is primarily concerned with the understanding of friction and wear mechanisms from a tribological point of view. A tribological approach may provide additional useful information about slip resistance performance.     

Laser power based surface characteristics models for 3-D printing process

Selective laser melting (SLM) is one of the important 3-D Printing processes that builds components of complex 3D shapes directly from the metal powder. It is widely used in manufacturing industries and is operated on significant amount of laser power drawn from the electric grid. The literature reveals that the properties such as surface roughness, waviness, tensile strength and dimensional accuracy of an SLM fabricated parts, depend on the laser power and can be improved by its appropriate adjustment. Determination of accurate values of laser power and the other inputs could lead to an improvement in energy efficiency and thus contributing to a clean and healthy environment. For determining the accurate value of laser power in achieving the required surface characteristics, the formulation of generalized mathematical models is an essential pre-requisite. In this context, an artificial intelligence approach of multi-gene genetic programming (MGGP) which develops the functional expressions between the process parameters automatically can be applied. The present work introduces an ensemble-based-MGGP approach to model the SLM process. Experiments on the SLM process with measurement of surface characteristics, namely surface roughness and waviness, based on the variations of laser power and other inputs are conducted, and the proposed ensemble-based-MGGP approach is applied. Statistical evaluation concludes that the performance of the proposed approach is better than that of the standardized MGGP approach. Sensitivity and parametric analysis conducted reveals the hidden relationships between surface characteristics and the laser power, which can be used to optimize the SLM process both economically and environmentally.     

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.     

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.     

Relationship between profile length and roughness variables for natural surfaces

Surface roughness variables such as the rms height and the correlation length are commonly used as inputs for electromagnetic models to predict the backscattering coefficient. The objective of this study is to investigate the influence of the roughness profile length on the estimation of roughness variables. Surface profiles of 25 m long obtained by a laser profiler have been examined. A clear dependence of exponential type was observed between the roughness variables and the profile length.     

Characterisation of surface roughness and sediment texture of intertidal flats using ERS SAR imagery

High resolution, synoptic information on sediment characteristics of intertidal flats is required for coastal management, e.g., for habitat mapping and dredging studies. This study aims to derive such information from space-borne Synthetic Aperture Radar (SAR). Estimates of the backscattering coefficient were extracted from ERS-1 SAR and ERS-2 SAR PRI imagery of four intertidal flats in the Westerschelde (southwest Netherlands). They were related to field measurements of surface roughness, moisture conditions and sediment texture. The field data were also used as input to the backscattering model IEM. The data and model predictions show that on the intertidal flats, backscattering depends mainly on vertical surface roughness, with rougher surfaces associated with more backscattering. Surface roughness, mainly determined by the ripple structure of the bed, decreased with the amount of mud in the sediment. This resulted in a significant negative correlation between backscattering and mud content, and a significant positive correlation between backscattering and median grain-size of the sediment. Sediment texture was also correlated with the volumetric moisture content of the sediment, with finer sediments being associated with higher moisture contents. However, moisture contents were generally high, and therefore the backscatter signal was not sensitive to differences in moisture content. The relationships allowed the development of regression models for the prediction of surface characteristics from SAR imagery, from which maps of, for example, mud content, have been derived.     

Predictive Modeling of the Ti6Al4V Alloy Surface Roughness

The prediction of surface roughness is important for all materials that undergo manufacturing processes. The Ti6Al4V titanium alloy is commonly used in aerospace, automotive and power generation industries but also in the manufacturing of medical implants, mainly because of its biocompatibility. Here we study the relationship of Ti6Al4V’s surface roughness with critical machining parameters and conditions based on experimental input (machining parameters)-output (surface roughness) data derived during the turning operation. The experimental findings are converted into polynomial models through the Response Surface Methodology (RSM) and into a fuzzy logic system through the Adaptive Neuro-Fuzzy Inference System (ANFIS). The ability of these two methodologies to predict Ti6Al4V’s surface roughness when machined is presented and compared. It is observed that the ANFIS predicts surface roughness with less error mainly when the data used for evaluation are not completely different to those used for training.     

Adhesion and contact modeling and experiments in microelectromechanical systems including roughness effects

The intermolecular adhesive forces in microelectromechanical systems (MEMS) applications are very significant and can hinder normal operation of sensors and actuators as well as micro-engines where catastrophic adhesion and high friction could be promoted. It has been experimentally shown that surface texturing (roughening) decreases the effect of these forces. In this paper, a model that predicts the effects of roughness, on the adhesion and contact forces in MEMS interfaces is presented. The three key parameters used to characterize the roughness, the asymmetry and the flatness of a surface topography are the root-mean-square roughness (RMS), skewness and kurtosis, respectively. It is predicted that surfaces with high RMS, high kurtosis and positive skewness exhibit lower adhesion and are thus less prone to collapsing when they come into contact or near contact. Moreover, polysilicon films with different levels of roughness, asymmetry and peakiness (sharpness) were fabricated. Experiments were conducted to evaluate the adhesive pull-off forces associated with these films. The roughness characteristics of these films were also used in the model to predict the adhesive pull-off forces. Good agreement was obtained between the theoretical and experimental results. Such a model could be used to determine the critical characteristics of a microstructure prior to fabrication to prevent adhesion and lower friction in terms of surface roughness, mechanical properties and environment.     

基于纹理分析的表面粗糙度等级识别

提出了一种利用图象纹理分析技术进行机械加工表面粗糙度检测的非接触检测方法,该方法首先根据统计方差对待测工件的表面粗糙度进行粗分类,然后,利用基于Ｇａｂｏｒ滤波器的纹理分类器,识别待测工件表面粗糙度等级。该新方法可简单、快速地实现表面粗糙度等级的自动识别,而且对图象旋转具有不变性,由于其纹理分类器的参数少,并且新方法成本低,参数标定方便,因而便于现场检测,如果与机床的控制系统相连,还可以实现加工的实     

Profile-based roughness discrimination with pen-type texture sensor

Tactile discriminations of surface roughness using artificial sensors have been challenging. The modeling methods and parameters that have been using to describe the mechanical properties of rough surface are insufficient for haptic roughness. This paper proposes a method to characterize surface roughness based on the profiles of the surface. A compact handheld pen-type texture sensor with a right probe is developed for the measurement of surface profiles. Based on the contact force and the motion of the senor, profiles in the paths of scanning are estimated. The height variations of a profile are converted to a series of tactile stimuli to represent the contact stimulations in haptic explorations. The mean and the standard deviation of the amplitudes of stimuli are identified as haptic features that indicate the required tangential force to slide on the rough surface and how rough the surface is, respectively. Experiments show that the roughness on four kinds of sandpapers can be clearly distinguished by the proposed discrimination method.     

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.     

Modeling and optimization of tool vibration and surface roughness in boring of steel using RSM,ANN and SVM

In this paper, statistical models were developed to investigate effect of cutting parameters on surface roughness and root mean square of work piece vibration in boring of stainless steel. A mixed level design of experiments was prepared with process variables of nose radius, cutting speed and feed rate. According to design of experiments, eighteen experiments were conducted on AISI 316 stainless steel with PVD coated carbide tools. Surface roughness, tool wear and vibration of work piece were measured in each experiment. A laser Doppler vibrometer was used to measure vibration of work piece in the form of acousto optic emission signals. These signals were processed and transformed in to different frequency zones using a fast Fourier transformer. Analysis of variance was used to identify significant cutting parameters on surface roughness and root mean square of work piece vibration. Predictive models like response surface methodology, artificial neural network and support vector machine were used to predict the surface roughness and root mean square of work piece vibration. Cutting parameters were optimized for minimum surface roughness and root mean square of work piece vibration using a multi response optimization technique.     

The influence of surface profiles on leakage in room temperature seal-bonding

We have developed a characterization method based on measured surface profiles for room temperature seal-bonding using surface activated bonding (SAB) technique and investigated the quantitative influence of surface profiles on leakage. We used the model for the bonding between the perfect flat surface which is not deformed and the metal surface with sub-nanometer rms surface roughness based on AFM surface profiles. The leak rates of the model were calculated by the Monte–Carlo method. Specific critical contact ratio P_sc gives the necessary contact ratio to disappear leak paths at a bonding interface. The P_sc was also calculated by the simulation with a basic plastic deformation law. Surface profiles of as-sputtered Au and chemical mechanical polished Cu surfaces were used as the metal surfaces. Investigating the relationship between the contact ratio and the leak rate, it was found that leak rates decrease drastically at a contact ratio close to the P_sc and leak rates decrease as the surface roughness decreases. Because of the drastic decrease of the leak rate, the vacuum sealing using SAB technique needs the contact ratio which is higher than P_sc. We also found that in the case of the as-sputtered Au deposited by the same conditions (only the deposition time change), P_sc has the tendency to decrease as the surface roughness decreases: if the rms roughness of the as-sputtered Au surface decreased from 1.5 to 0.5, the P_sc decreased from 0.6 to 0.52.     

有真实感图形的纹理映射算法

本文讨论了计算机生成真实感图形的纹理映射技术，根据模拟现实物体的表面细节特征提出了三种纹理映射方法。     

An investigation of ball burnishing process on CNC lathe using finite element analysis

The paper deals with finite element analysis of burnishing process on the D3 tool steel material using CNC lathe. The input parameters are speed, burnishing force, and feed. The output parameters are surface roughness, residual stress, micro-hardness and out of roundness. Surface roughness generated after the turning operation is used to model the surface roughness pattern which is further used to simulate ball burnishing process using finite element based software DEFORM-2D. For tool steel, improvement in the surface roughness values achieved after ball burnishing process is 86.2%. The surface roughness and residual stress results of FEM simulations are compared with experimental results. The minimum and maximum deviation between the experimental and simulation values of surface roughness is 3.22 % and 8.69%, experimental residual stress is 0.63% and 3.94% and theoretical values of residual stress are 1.23% and 3.57%, respectively.     

C-band radar backscatter of Baltic sea ice: theoretical predictions compared with calibrated SAR measurements

Abstract

Airborne Synthetic Aperture Radar (SAR) data have been analysed together with in situ measurements of sea ice during the Bothnian Experiment in Preparation for ERS-1 (BEPERS) in March 1988. Based on the physical properties of the snow-covered level ice, a scattering model is used to predict the C-band like-polarization backscattering coefficient in an experiment area. Both the average backscattering coefficient and the SAR image texture were found to be in good agreement with the scattering model predictions. The backscatter signature of the level ice was found to be dominated by the ice surface r.m.s. height and autocorrelation function. These parameters were determined from profiles of the ice surface height, which were measured using a laser profiler device with sub-mm accuracy. The present model is expected to be accurate when the backscattering is dominated by scattering from the cm-scale snow or ice surface roughness.     

Determining turbulent flow friction coefficient using adaptive neuro-fuzzy computing technique

In the analysis of water distribution networks, the main required design parameters are the lengths, diameters, and friction coefficients of rough-pipes, as well as nodal demands and water levels in the reservoirs. Although some of these parameters such as the pipe lengths are precisely known and would remain the same at different points of the networks whereas some parameters such as the pipe diameters and friction coefficients would changed during the life of network and therefore they can be treated as imprecise information. The primary focus of this study is to investigate the accuracy of a fuzzy rule system approach to determine the relationship between pipe roughness, Reynolds number and friction factor because of the imprecise, insufficient, ambiguous and uncertain data available. A neuro-fuzzy approach was developed to relate the input (pipe roughness and Reynolds number) and output (friction coefficient) variables. The application of the proposed approach was performed for the data derived from the Moody’s diagram. The performance of the proposed model was compared with respect to the conventional procedures using some statistic parameters for error estimation. The comparison test results reveal that through fuzzy rules and membership functions, the friction factor can be identified, precisely.