径向基函数网络在虚拟加工对象重构中的应用

针对虚拟制造中无加工对象数据的加工过程仿真,提出适用于离散无规则边界测量数据的径向基函数网络方法,以重构加工对象曲面,阐述了径向基函数网络曲面重构的基本理论,分析了径向基函数网络宽度参数的确定方法;给出了径向基函数网络曲面重构函数的具体实现过程,计算了重构的误差.以实例验证了该方法的可行性,并与传统曲面拟合方法相比较,得出该方法的重构精度高,改进了传统曲面拟合在处理非均匀截面数据点时曲面形状的失真和运算的不稳定现象,从而显示了径向基函数网络重构曲面的优越性.     

Modeling of abrasive flow rotary machining process by artificial neural network

Abrasive flow machining (AFM) is one of the non-traditional machining processes applicable to finishing, deburring, rounding of edges, and removing defective layers from workpiece surface. Abrasive material, used as a mixture of a polymer with abrasive material powder, has reciprocal motion on workpiece surface under pressure during the process. In the following study, a new method of AFM process called henceforth abrasive flow rotary machining (AFRM) will be proposed, in which by elimination of reciprocal motion of abrasive material and the mere use of its stirring and rotation of workpiece, the amount of used material would be optimized. Furthermore, AFRM is executable by simpler tools and machines. In order to investigate performance of the method, experimental tests were designed by the Taguchi method. Then, the tests were carried out and the influence of candidate effective parameters was determined and modeled by artificial neural network (ANN) method. To evaluate the ANN results, they were compared with reported results of AFM. An agreement between our ANN results on predictions of AFRM material removal value and surface roughness was observed with AFM data. The results showed through AFRM, in addition to saving of abrasive material, surface finish is achievable same as AFM’s.     

Parameter estimation for abrasive water jet machining process using neural networks

The abrasive water jet machining process, a material removal process, uses a high velocity jet of water and an abrasive particle mixture. The estimation of appropriate values of the process parameters is an essential step toward an effective process performance. This has led to the development of numerous mathematical and empirical models. However, the complexity of the process confines the use of these models for limited operating conditions; e.g., some of these models are valid for special material combinations while others are based on the selection of only the most critical variables such as pump pressure, traverse rate, abrasive mass flow rate and others that affect the process. Furthermore, these models may not be generalized to other operating conditions. In this respect, a neural network approach has been proposed in this paper. Two neural network approaches, backpropagation and radial basis function networks, are proposed. The results from these two neural network approaches are compared with that from the linear and non-linear regression models. The neural networks provide a better estimation of the parameters for the abrasive water jet machining process.     

Prediction of surface roughness during abrasive flow machining

An analytical model is proposed to simulate and predict the surface roughness for different machining conditions in abrasive flow machining (AFM). The kinematic analysis is used to model the interaction between grain and workpiece. Fundamental AFM parameters, such as the grain size, grain concentration, active grain density, grain spacing, forces on the grain, initial topography, and initial surface finish (R _a value) of the workpiece are used to describe the grain-workpiece interaction. The AFM process is studied under a systematic variation of grain size, grain concentration and extrusion pressure with initial surface finish of the workpiece. Simulation results show that the proposed model gives results that are consistent with experimental results.     

Intelligent recognition of gas-oil-water three-phase flow regime and determination of volume fraction using radial basis function

The problem of how to accurately measure the flow rate of oil–gas–water mixtures in a pipeline remains one of the key challenges in the petroleum industry. This paper proposes a new methodology for identifying flow regimes and predicting volume fractions in gas-oil-water multiphase systems using dual energy fan-beam gamma-ray attenuation technique and artificial neural networks. The novelty of this study in comparison with previous works, is using just 4 extracted features (photo peaks of ²⁴¹Am and ¹³⁷Cs in 2 detectors) from the gamma ray spectrums instead of using the whole gamma ray spectrum, which reduces the undesired noises and also improves the speed of recognition in real situations. Radial basis function was used for developing the neural network model in MATLAB software in order to classify the flow patterns (annular, stratified and homogenous) and predict the value of volume fractions. The ideal and static theoretical models for flow regimes have been developed using MCNP-X code. The proposed networks could correctly recognize all the three different flow regimes and also determine volume fractions with mean absolute error of less than 5.68% according to the recognized regime.     

模具结构化表面SAF精密光整技术

针对模具结构化表面难以采用传统抛光工具实现精密光整加工的问题,提出了一种基于软性磨粒流(SAF)的模具结构化表面无工具精密加工新技术.该技术通过约束模块与结构化表面组合构成特定形状的磨粒流流道,利用SAF在流道中的湍流流动使磨粒对结构化表面进行微力微量切削,进而实现光整加工.介绍了SAF加工技术原理、SAF流体力学特征...     

Particle movement patterns and their prediction in abrasive flow machining

Abrasive particle movement pattern is an important factor in estimating the wear rate of materials, especially, as it is closely related to the burring, buffing and polishing efficiency of the abrasive flow machining (AFM) process. There are generally two kinds of particle movement patterns in the AFM process, i.e. sliding–rubbing and rolling. In mechanism, AFM grain–workpiece interaction is taking place in any one or a combination of the possible modes: elastic/plastic deformation by sliding–rubbing grain movement; elastic/plastic deformation by rolling grain movement; chip formation (micro‐cutting) by rubbing grain movement; ridges formation by rubbing and rolling grain movement; and low‐cycle fatigue wear. Therefore, the machining efficiency of a machine part is predominantly dependent upon the particle movement patterns. In this paper, normal load, particle size and hardness of machine parts were investigated to understand the involved parameters of particle movement patterns and propose a computer statistic prediction of particle movement patterns. It has been found that there are two cases. In case of large‐size particles, the ratio of rolling particles is increased with increasing normal load. For small‐size particles, the ratio of grooving particles is increased with increasing normal load and vice versa. When normal load is light, the particle size cannot usually give an effect on movement patterns. That influence will be predominant under heavy normal load. Most of the particles will tend to groove when the particle size is below a certain value. Hardness of the material and their hardness difference for tribological pairs are other important monitors in predicting particle movement patterns. In this research, increasing hardness of materials results in more rolling particles, which results in much less cutting particles. Copyright © 2007 John Wiley & Sons, Ltd.     

Deburring of burrs in spring collets by abrasive flow machining

Micro burrs occurring inside the small and large diameters adversely affect the properties of products. Manual deburring of micro burrs in particular damages the processed surface and reduces production efficiency. In this study, spring collets made of chrome-molybdenum are used to test the deburring of the surface of collets including crossed micro grooves by abrasive flow machining. This revised version was published online in October 2004 with a correction to the issue number.     

Study of the rheological properties and the finishing behavior of abrasive gels in abrasive flow machining

Abrasive flow machining (AFM) is an effective method to finish the smooth surface in the complex holes. Abrasive media are key elements which dominate the polished results in AFM. But it is hard to develop the machining model of these abrasive gels because of its complicated mechanism. In this research, a non-Newtonian flow is used to set up the abrasive mechanism of the abrasive media in AFM. Power law is a main equation of the non-Newtonian flow to describe the motion of the abrasive media. Viscosities vs. shear rates of different abrasive gels are used to establish the power law in CFD-ACE⁺ software first. And the working parameters of AFM were applied as input to study the properties of the abrasive gels in AFM. Finally, the relationships between the simulations and the experiments were found. And the abrasive mechanism of the abrasive gels was set up in AFM. The simulated results show that the abrasive gel with high viscosity can entirely deform in the complex hole than the abrasive gel with low viscosity. And the abrasive gel with high viscosity generates a larger shear force than the abrasive gel with low viscosity in the same area. Moreover, the strain rate is seriously changed when the abrasive gel cross over the narrow cross-section of the complex hole. It also means that abrasive gel will produce large finish force in that area. And these results indeed consist with the experiments in AFM.     

游离磨粒超精加工机理和应用

利用游离磨粒进行超精密加工,可获得高的表面质量和小的加工损伤层,因此被广泛应用,且衍生出较多的加工方法.从去除材料的机理角度,将游离磨粒超精加工分为:通过被加工材料的变形去除材料;通过磨粒与被加工材料的化学反应去除材料;通过加工液与被加工材料的化学反应去除材料.并对各种材料去除机理相对应的典型加工方法进行了综述.     

Micro machining of high-hardness materials using magnetic abrasive grains

If processing speed is increased and processing time is lengthened, a magnetic abrasive system can be used as a tool both for finishing and precise dimensional control of manufactured products at the micro-level, and for mirror face processing. In this study, a micro machining system that can change its high rotational speed was developed. Using micro machining with a high speed magnetic abrasive system, the diameter of difficult-to-cut materials with high hardness could be controlled almost linearly by changing the rotational speed, the frequency of magnetic poles, and the size of diamond particles. By changing machining conditions, the surface roughness of the mirror face level could be obtained. To improve roundness, a higher rotational speed improved processing time and dimensional precision, and 20,000 rpm was the optimum speed in this experiment. Roundness was obtained up to 0.15 μm. Before and after the processing, there was almost no change in the WC(tungsten carbide) and Ni components of the material, and there were no remains of mixed-type particles such as iron and diamond on the material.     

共轨管微小孔磨粒流加工装备的设计与数值模拟

通过对磨粒流加工技术的研究,提出了解决共轨管零件微小孔结构的精密加工方法.设计了一种磨粒流加工装备,可实现对共轨管零件微小孔的精加工,并对该装备的关键部件进行了有限元分析.利用流体力学软件Fluent对磨粒流微小孔加工的加工状态进行了数值模拟,通过比较分析,得到了理想的磨粒流加工方案.     

Optimization of abrasive waterjet machining using multi-objective cuckoo search algorithm

Abrasive waterjet (AWJ) machining is widely applied in the fields of civil and mechanical engineering. In this study, a general and theoretical analysis procedure was presented before computing application. It mainly focused on the kinetic energy model and wear rate model in machining process. Then, the multi-objective cuckoo algorithm was employed for optimization design of AWJ cutting head model, making sure to maximize the output energy and minimize the nozzle erosion rate while keeping the other factors constant. To demonstrate the effectiveness of the above strategy, a practical AWJ machining system was selected for investigation purpose. The proposed model was compared with experimental data for investigating the difference between the initial design and the optimized model. The results showed that the multi-objective cuckoo algorithm has great ability in prediction of outlet power and wear rate. Meanwhile, the optimized parameters were also superior to the original design, compared with experimental test data. The developed model can be used as a systematic approach for prediction in an advanced manufacturing process.     

Cylinder pressure reconstruction based on complex radial basis function networks from vibration and speed signals

Methods to measure and monitor the cylinder pressure in internal combustion engines can contribute to reduced fuel consumption, noise and exhaust emissions. As direct measurements of the cylinder pressure are expensive and not suitable for measurements in vehicles on the road indirect methods which measure cylinder pressure have great potential value. In this paper, a non-linear model based on complex radial basis function (RBF) networks is proposed for the reconstruction of in-cylinder pressure pulse waveforms. Input to the network is the Fourier transforms of both engine structure vibration and crankshaft speed fluctuation. The primary reason for the use of Fourier transforms is that different frequency regions of the signals are used for the reconstruction process. This approach also makes it easier to reduce the amount of information that is used as input to the RBF network. The complex RBF network was applied to measurements from a 6-cylinder ethanol powered diesel engine over a wide range of running conditions. Prediction accuracy was validated by comparing a number of parameters between the measured and predicted cylinder pressure waveform such as maximum pressure, maximum rate of pressure rise and indicated mean effective pressure. The performance of the network was also evaluated for a number of untrained running conditions that differ both in speed and load from the trained ones. The results for the validation set were comparable to the trained conditions.     

An investigation of the hole machining processes on woven carbon-fiber reinforced polymers (CFRPs) using abrasive waterjets

An investigation of the hole cutting and drilling processes on woven carbon-fiber reinforced polymer sheets using abrasive waterjet (AWJ) is presented. The drilling process uses a stationary AWJ to impinge a target material to make a hole, while the cutting process requires an AWJ to penetrate the workpiece before moving in a circular path to cut a hole. It is found that the holes machined by both the processes exhibit similar geometrical features, where the diameter at the top is greater than at the bottom. It is further found that the holes from the drilling process have a better roundness than those from cutting process primarily due to the jet instability during cutting movement. Plausible trends of the hole characteristics (e.g., diameter and wall inclination) and defects (e.g., delamination) with respect to the process parameters are discussed. It is shown that water pressure is the major parameter affecting hole defects. The hole drilling process yields more severe defects than the cutting process because of the initial impact of the jet. Predictive models for machined hole diameter in both processes are developed. The model predictions are in good agreement with the experimental data under the corresponding conditions.     

Morphology and integrity of surfaces finished by centrifugal force assisted abrasive flow machining

Centrifugal force assisted abrasive flow machining (CFAAFM) process has recently been tried as a hybrid machining process with the aim towards performance improvement of assisted abrasive flow machining (AFM) process by applying centrifugal force on the abrasive-laden media with a rotating centrifugal force generating (CFG) rod introduced in the workpiece passage. In the CFAAFM process, the surfaces are generated by erosion from random attack of abrasive grains. CFAAFMed surfaces are unidirectional but random in nature due to transient media flow conditions. In the present paper, surface morphology, surface micro-hardness, X-ray analysis, and surface compressive residual stress produced in the finished surface layer by CFAAFM process is described. The CFAAFM process was performed under different rotational speeds of CFG rod while keeping other input parameters constant during the experiments. The increase in surface microhardness and compressive residual stress of the workpiece with an increase in the rotational speed of CFG rod is attributed to the work-hardening surface that possibly occurs due to ‘throw’ of abrasive particles upon specimen surface.     

Finishing effect of abrasive flow machining on micro slit fabricated by wire-EDM

This experimental research use the method of abrasive flow machining (AFM) to evaluate the characteristics of various levels of roughness and finishing of the complex shaped micro slits fabricated by wire electrical discharge machining (Wire-EDM). An investigative methodology based on the Taguchi experimental method for the micro slits of biomedicine was developed to determine the parameters of AFM, including abrasive particle size, concentration, extrusion pressure and machining time. The parameters that influenced the machining quality of the micro slits were also analyzed. Furthermore, in the shape precision of the micro slit fabricated by wire-EDM and subsequently fine-finished by AFM was also elucidated using a scanning electron microscope (SEM). The significant machining parameters and the optimal combinations of the machining parameters were identified by ANOVA (analysis of variation) and the S/N (-to-noise) ratio response graph. ANOVA was proposed to obtain the surface finishing and the shape precision in this study.     

Optimizing the diamond machining of ceramics on the basis of systemic analysis using neural networks

A generalized algorithm is presented for estimating the efficiency of diamond machining of ceramics. The algorithm is based on systematic analysis of the influence of the technological media and the conditions and employs up-to-date computational techniques.