Sampling error in conversion of the surface profile signal

A fundamental problem in a/d conversion of a measurment signal of surface profile id discussed from the viewpoint of a limit of sampling error in the time domain. A comparison is also made of the results of autocorrelation function, power spectrum and profile parameters such as R_max, R_a etc, obtained from the sampled surface profile in the time domain and in the longitudinal domain. It is suggested that the minimization of sampling error may be attained by the use of a digital displacement transducer attached to the usual surface measurement instrument.     

Surface roughness and roundness of bearing raceway machined by floating abrasive polishing and their effects on bearing’s running noise

As the two most important indexes of bearing raceway, surface roughness and roundness have significant influence on bearing noise. Some researchers have carried out studies in this field, however, reason and extent of the influence of raceway surface geometric characteristics on bearing running noise are not perfectly clear up to now. In this paper, the raceway of 6309 type bearing＇s inner and outer ring is machined by floating abrasive polishing adopting soft abrasive pad. Surface roughness parameters, arithmetical mean deviation of the profile Ra, the point height of irregularities Rz, maximum height of the profile Rmax and roundness fof raceways, are measured before and after machining, and the change rules of the measured results are studied. The study results show that the floating abrasive polishing can reduce the surface geometric errors of bearing raceway evidently. The roundness error is reduced by 25%, Rm~x value is reduced by 35.5%, Rz value is reduced by 22% and Ra value is reduced by 5%. By analyzing the change of the geometrical parameters and the shape difference of the raceway before and after machining, it is found that the floating abrasive polishing method can affect the roundness error mainly by modifying the local deviation of the raceway＇s surface profile. Bearings with different raceway surface geometrical parameter value are assembled and the running noise is tested. The test results show that Ra has a little, Rmax and Rz have a measurable, and the roundness error has a significant influence on the running noise. From the viewpoint of controlling bearings＇ running noise, raceway roundness error should be strictly controlled, and for the surface roughness parameters, R,n~x and Rz should be mainly controlled. This paper proposes an effective method to obtain the low noise bearing by machining the raceway with floating abrasive polishing after super finishing.     

Micro/meso ultra precision grinding of fibre optic connectors

The present paper reports on the development of a micro/meso grinding technology using inclined resin bond diamond cup wheels for machining spherical end faces of fibre optic connectors. The ground spherical end faces obtained using the wheel of grit size of 4 μm have an average roughness (R_a) value of 7 nm in the fibre area and a profile error of below 0.4 μm. The corresponding average return and insertion losses are 55 and 0.1 dB, respectively. The results are competitive to those obtained from the polishing process that is currently used in industries. Furthermore, this study has revealed the relationships between the ground surface quality characterized by roughness and profile accuracy and the optic performance evaluated by insertion and return losses.     

Fiber optic displacement sensor for imaging of tooth surface roughness

A simple and inexpensive method using fiber optic displacement sensor is proposed for measurements of tooth surface roughness based on the intensity modulation technique. A light beam was launched onto a tooth surface via a bundled fiber. The reflected light from the surface was collected and measured as a function of lateral distance to estimate the roughness of the surface. The system’s roughness measurement capability was successfully tested on teeth surfaces of varying surface texture. In the measurement, the average surface roughness, R_a for the canine, molar, hybrid composite resin and artificial teeth surfaces were estimated to be approximately 121, 62.6, 39 and 37.6 μm, respectively. The experimental results indicated the capability of implementation of the displacement sensor for the imaging of the tooth surface profile as well as a micron-size roughness estimator with a measurement error of less than 2.35%.     

Analytical Surface Roughness Parameters of a Theoretical Profile Consisting of Elliptical Arcs

The closed‐form solutions of surface roughness parameters for a theoretical profile consisting of elliptical arcs are presented. Parabolic and simplified approximation methods are commonly used to estimate the surface roughness parameters for such machined surface profiles. The closed‐form solution presented in this study reveals the range of errors of approximation methods for any elliptical arc size. Using both implicit and parametric methods, the closed‐form solutions of three surface roughness parameters, R _t , R _a , and R _q , were derived. Their dimensionless expressions were also studied and a single chart was developed to present the surface roughness parameters. This research provides a guideline on the use of approximate methods. The error is smaller than 1.6% when the ratio of the feed and major semi‐axis of the elliptical arc is smaller than 0.5. The closed‐form expressions developed in this study can be used for the surface roughness modeling in CAD/CAM simulations.     

The study of machined surface roughness by random analysis

The effects of various parameters on surface roughness were studied by measuring R_a (c.l.a. value) or R_t (peak-to-valley height). The effect of cutting speed, feed, rake angle and depth of cut on the randomness of the surface profile were studied from the auto-correlation function of the surface profile.     

Study of surface roughness effects in elastohydrodynamic lubrication of rolling line contacts using a deterministic model

A sinusoidal surface roughness model is adopted for the analysis of the effects of roughness amplitude and wavelength on pressure profile, film shape, minimum film thickness and coefficient of friction in a steady state EHL line contact. The influence coefficients used for the evaluation of surface displacements are calculated by utilizing a numerical method based on Fast Fourier Transform. Significant reduction is observed in the minimum film thickness due to surface roughness. Such reduction is quantified by roughness correction factor, C_R, and a relationship between C_R and non-dimensional surface roughness amplitude A is derived as: C_R=1−0.7823A^0.8213. This equation may prove to be of interest from designer's viewpoint. The friction coefficient is found to increase appreciably with increasing amplitude and decreasing wavelength of surface roughness.     

Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression

In this study, models for predicting the surface roughness of AISI 1040 steel material using artificial neural networks (ANN) and multiple regression (MRM) are developed. The models are optimized using cutting parameters as input and corresponding surface roughness values as output. Cutting parameters considered in this study include cutting speed, feed rate, depth of cut, and nose radius. Surface roughness is characterized by the mean (R _a) and total (R _t) of the recorded roughness values at different locations on the surface. A total of 81 different experiments were performed, each with a different setting of the cutting parameters, and the corresponding R _a and R _t values for each case are measured. Input–output pairs obtained through these 81 experiments are used to train an ANN is achieved at the 200,00th epoch. Mean squared error of 0.002917120% achieved using the developed ANN outperforms error rates reported in earlier studies and can also be considered admissible for real-time deployment of the developed ANN algorithm for robust prediction of the surface roughness in industrial settings.     

Modeling surface roughness for polishing process based on abrasive cutting and probability theory

The surface roughness is a variable used to describe the quality of polished surface. This article presents a surface roughness model based on abrasive cutting and probability theory, which considers the effects of abrasive grain shape, grit and distribution feature, pressure on surface roughness. The abrasive grain protrusion heights are thought to close to Gaussian distribution, and then the relationship between the indentation depth and the pressure based on Hertz contact theory is obtained. Surface roughness prediction model is established by calculating indentation depth of the abrasive grains on workpiece surface. The maximum surface profile height (R_y) is approximately equal to the maximum indentation depth of the abrasive grain. The arithmetic average surface roughness (R_a) is equal to the average indentation depth of the abrasive grain. The effects of process parameters such as pressure and grit on R_y and R_a were simulated and analyzed in detail.     

Groove wear,built-up edge and surface roughness in turning

The variation of groove wear profile, built up edge adhering to the machined surface and surface roughness have been studied. The correlation between the surface roughness (R_a and R_t), groove wear and built up edge is discussed.     

RTDS implementation of an improved sliding mode based inverter controller for PV system

This paper proposes a novel approach for testing dynamics and control aspects of a large scale photovoltaic (PV) system in real time along with resolving design hindrances of controller parameters using Real Time Digital Simulator (RTDS). In general, the harmonic profile of a fast controller has wide distribution due to the large bandwidth of the controller. The major contribution of this paper is that the proposed control strategy gives an improved voltage harmonic profile and distribute it more around the switching frequency along with fast transient response; filter design, thus, becomes easier. The implementation of a control strategy with high bandwidth in small time steps of Real Time Digital Simulator (RTDS) is not straight forward. This paper shows a good methodology for the practitioners to implement such control scheme in RTDS. As a part of the industrial process, the controller parameters are optimized using particle swarm optimization (PSO) technique to improve the low voltage ride through (LVRT) performance under network disturbance. The response surface methodology (RSM) is well adapted to build analytical models for recovery time (R_t), maximum percentage overshoot (MPOS), settling time (T_s), and steady state error (E_ss) of the voltage profile immediate after inverter under disturbance. A systematic approach of controller parameter optimization is detailed. The transient performance of the PSO based optimization method applied to the proposed sliding mode controlled PV inverter is compared with the results from genetic algorithm (GA) based optimization technique. The reported real time implementation challenges and controller optimization procedure are applicable to other control applications in the field of renewable and distributed generation systems.     

Abrasive flow machining applied to plastic gear matrix polishing

The topic of this paper is the application of abrasive flow machining (AFM) to gear tool inserts polishing. Polished surface on plastic gear teeth improves surface geometry stability, and it increases the lifespan, which was proved on the gear testing rig. Experiments have shown that it is an efficient alternative to the hand polishing procedure. Besides significant cost and processing time savings, AFM generates constant surface quality. The achieved roughness is homogeneous on the entire machined surface; it is reduced from R _a?=?0.68 μm to R _a?=?0.08 μm in 120 s. At the same time, the tooth geometry profile is not damaged. The first time, surface polishing should be done at request because of individually manufactured tool inserts. Processing parameters depend on the type of the abrasive machine, the polishing paste and part geometry. Computer-aided abrasive flow analyses and practical experiments assist in setting optimum AFM process parameters. The paper presents a working set of parameters and a detailed report on machined surface measurement data. On the base of better understanding of AFM process, the surface roughness prediction model and thickness of removed material model was setup. It has high accuracy and reliability for specific applications. The use of plastic gears for various applications is widespread; the presented process improvement is an important innovation for injection molding tools manufacturers.     

Improved measure of workpiece surface deterioration during turning using non-contact vision method

The surface finish quality of a machined workpiece is commonly measured using the average roughness parameter, R_a. This parameter, however, is insensitive to the lateral changes undergone by the surface in the feed direction as a consequence of tool wear. In this work, the effectiveness of four methods of workpiece surface analysis, namely autocorrelation, cross-correlation, and two new methods, called lateral material shift (LMS) ratio and profile slope ratio (PSR) analyses are investigated. Dry machining experiments were carried out on 316 stainless steel. Images of tool nose and workpiece profiles were captured using digital camera, and the edges were extracted using sub-pixel edge detection. In the autocorrelation approach, each workpiece profile was correlated with a shifted version of the same profile. In the cross-correlation approach, the workpiece profiles at different stages of machining were correlated with a reference profile generated using the unworn tool edge. In the LMS ratio method, the material shift ratios were determined from each waveform on the workpiece profile at various stages of tool wear, while in the PSR method the slopes at the right and left part of the waveform were compared. Among the four methods, the LMS ratio method produced the best correlation with tool flank wear with the maximum R-squared value of 0.9461, while average roughness R_a showed no correlation at all with both major and nose flank wear.     

巴特沃思小波在表面形貌信号分离中的应用

为了快速准确地分析工程表面,提出了一种基于巴特沃思小波的滤波方法。首先,讨论了小波滤波器的低通幅度传输特性,并以此作为选择适合表面形貌分析小波滤波器的依据。然后,介绍了巴特沃思小波滤波器的构造原理,并给出了它的快速实现算法;综合其优良的传输特性和高效的实现算法,以巴特沃思小波作为表面分析的滤波器,对实际表面轮廓进行了多层分解。最后,利用巴特沃思小波建立了表面轮廓评定的基准线,并给出了确定小波分解层数的方法。实验结果表明,巴特沃思小波能够快速准确地实现表面轮廓的多尺度分析,稳定可靠地提取表面中线;在普通计算机上提取11200点数据表面中线仅耗时60ms,利用该中线计算所得R值相对误差仅比利用高斯中线所得R值相对误差大0.12%。     

红外线聚光非球面透镜的单点金刚石镜面切削方法

根据硬脆性材料的延性域加工机理和面形误差补偿加工方法,研究了圆弧形和平头形刀具的单点金刚石延性域切削方法,在加工中直接获得了镜面切除面;并利用数控技术进行误差补偿,克服了因加工试验、刀具磨损、机械振动、热变形等造成的加工误差导致的非球面的面形精度降低和表面粗糙度恶化.并将该方法用于采用圆弧形刀具对红外线聚光的φ70mm非球面锗透镜进行单点金刚石切削实验中.试验结果表明面形误差补偿加工方法可以进一步消除加工误差,将非球面的面形精度PV值从微米级(1.23μm)提高到亚微米级(0.36μm)的程度,表面粗糙度R_a从亚微米级(0.27μm)改善到超亚微米级(0.04μm)的范围.     

Simulation of surface generated during abrasive flow finishing of Al/SiCp-MMC using neural networks

Abrasive flow machining (AFM) is a multivariable finishing process which finds its use in difficult to finish surfaces on difficult to finish materials. Near accurate prediction of generated surface by this process could be very useful for the practicing engineers. Conventionally, regression models are used for such prediction. This paper presents the use of artificial neural networks (ANN) for modeling and simulation of response characteristics during AFM process in finishing of Al/SiC_p metal matrix composites (MMCs) components. A generalized back-propagation neural network with five inputs, four outputs, and one hidden layer is designed. Based upon the experimental data of the effects of AFM process parameters, e.g., abrasive mesh size, number of finishing cycles, extrusion pressure, percentage of abrasive concentration, and media viscosity grade, on performance characteristics, e.g., arithmetic mean value of surface roughness (R _a, micrometers), maximum peak–valley surface roughness height (R _t, micrometers), improvement in R _a (i.e., ΔR _a), and improvement in R _t (i.e., ΔR _t), the networks are trained for finishing of Al/SiC_p-MMC cylindrical components. ANN models are compared with multivariable regression analysis models, and their prediction accuracy is experimentally validated.     

Corrosion monitoring of the RC structures in time domain: Part I. Response analysis of the electrochemical transfer function based on complex function approximation

To finally obtain the overall and fast recognition method for the electrochemical corrosion of reinforced concrete (RC) structures, the complex function algorithm is established to approximate the corrosion transfer function in frequency domain, and then the influence of Z_CPE, Z_W, R_ct and R_c in the universal equivalent circuit (EC) R_c((R_ctZ_W)Z_CPE) on the time response is discussed in detail. The results indicate that the polynomial complex function can successfully approximate the corrosion transfer function with the given accuracy. The response of R_c((R_ctZ_W)Z_CPE) in time domain can be achieved by the inverse Laplace transform of the polynomial complex approximation function.     

Effect of graphene addition and tribological performance of Al 6061/graphene flake composite

Graphene, no wonder has attracted a significant research interest due to its extensive physical properties at its single atomic thickness and 2D morphology. The current studies focus on the role of graphene in reducing the wear and frictional coefficient of Al 6061–graphene-reinforced metal–matrix composites (MMC’s). Reinforcement chosen is 0.3, 0.6 and 0.9 wt% of graphene to investigate the self-lubricating property under dry wear condition and processed through the ultrasonic liquid processor. The dry frictional wear test was carried out using pin-on-disc tribometer to evaluate the effect of graphene content in the composites under various normal load (5, 10, 15 N) and disc sliding speed (0.4, 0.8, 1.2 m/s) conditions. The results show that there is a significant increase in the hardness and wear resistance and a reduction in the coefficient of friction (μ) values compared to pure alloys. Arithmetic mean surface roughness values (R_a), max profile peak (R_p) and max valley depth (R_v) are found to be comparatively lower than the pure alloy. Due to the tribological potential coupled with improved strength and surface roughness values, Al 6061–graphene composite are excellent candidates for all applications where it is subjected to Friction and wear.     

Evaluation of discrete displacement transducer by spectral analysis of surface roughness

A discrete displacement transducer using an optical technique and attached to a commonly used stylus instrument is described. The proposed transducer plays an important role in generating a digital output of surface profile from the viewpoint of sampling error. The working accuracy is discussed with reference to the spectral analysis of surface roughness.