微磨料气射流成形加工表面粗糙度的研究

通过微磨料气射流成形加工玻璃试验,研究了工艺参数及其交互作用对加工表面粗糙度的影响,建立了表面粗糙度的回归模型。结果表明,气压对表面粗糙度的影响最显著,其次是靶距和喷嘴横移速度的交互作用、气压和靶距的交互作用以及靶距,而气压和喷嘴横移速度的交互作用、喷嘴横移速度对表面粗糙度的影响相对较小。表面粗糙度随着气压的增加而增大,随着靶距和喷嘴横移速度的增加先减小后增大。选用中低气压和较大靶距的组合有利于降低表面粗糙度。方差分析和残差检验的结果表明回归模型可以有效地预测表面粗糙度。     

Application of Taguchi design for quality characterization of abrasive water jet machining of TRIP sheet steels

In the present paper, the influence of sheet thickness, nozzle diameter, standoff distance, and traverse speed during abrasive water jet machining (AWJM) of transformation-induced plasticity (TRIP) sheet steels on surface quality characteristics (kerf geometry and surface roughness) was investigated. The experiments were designed using Taguchi methodology and carried out by AWJ Machining TRIP 700 CR-FH and TRIP 800 HR-FH steel sheets. As response variables, mean kerf width and average surface roughness were selected. The experimental results were analyzed using analysis of means and analysis of variance methods in order to correlate the AWJM process parameters the response variables. In addition, regression models were obtained using the experimental results and validated with six independent experiments. The reported results indicate that the proposed methodology can satisfactorily analyze the surface roughness and the mean kerf in AWJM; moreover, it can be considered as valuable tools for process planning in workshop.     

磨料水射流切割玻璃钢复合材料试验研究

磨料水射流对材料具有极强的冲蚀作用,并在冲蚀过程中不改变材料的力学、物理和化学性能,适于切割热敏、压敏、脆性和复合材料。文中选择压强、靶距、横移速度和砂流量四因素,试验研究了各因素对玻璃钢切割断面深度比值q的影响。在磨料流量为0．060kg／min、切割速度为650mm／min、靶距为5mm条件下,切割玻璃钢样件,没有出现分层和起鳞现象,切割表面光滑,充分证实了磨料水射流切割复合材料的优势。     

Effects of process parameters on surface roughness in abrasive waterjet cutting of aluminium

Abrasive waterjet cutting is a novel machining process capable of processing wide range of hard-to-cut materials. Surface roughness of machined parts is one of the major machining characteristics that play an important role in determining the quality of engineering components. This paper shows the influence of process parameters on surface roughness (R_a) which is an important cutting performance measure in abrasive waterjet cutting of aluminium. Taguchi’s design of experiments was carried out in order to collect surface roughness values. Experiments were conducted in varying water pressure, nozzle traverse speed, abrasive mass flow rate and standoff distance for cutting aluminium using abrasive waterjet cutting process. The effects of these parameters on surface roughness have been studied based on the experimental results.     

磨料水射流铣削陶瓷材料的表面形状研究

对磨料水射流铣削氧化铝陶瓷的铣削表面形状进行了试验研究,并分析了铣削工艺参数对铣削表面形状的影响。结果表明对应于不同的工艺参数,铣削表面的结构形状随加工参数的改变而变化。随着水压力的增大,铣削所得的凹槽深度增加,而喷嘴横移速度增加时,凹槽深度减小,水压力和喷嘴横移速度对凹槽宽度的影响不大。随着靶距和横向进给量的增加,凹槽深度都减小,宽度增加。     

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.     

磨料水射流切割陶瓷材料的加工表面质量研究

对磨料水射流切割氧化铝陶瓷的加工表面质量进行了试验研究,对磨料水射流切割陶瓷的切口结构形状进行了论述与分析,并分析了工艺参数对加工表面质量的影响。结果表明切口断面从上往下很明显地分成三部分:光滑区、波纹区和破碎区,光滑区的加工表面粗糙度随着水压力的增加、喷嘴横移速度和磨料粒度的减小而减小;靶距增加,表面粗糙度先减小,后增加。     

磨料射流加工的主要参数对冲蚀体积的影响

试验研究了前混合磨料射流加工的主要参数（压力、磨料重量比浓度、靶距、横移速度、磨料粒径大小等）对冲蚀体积率的影响。试验结果表明：压力增大，冲蚀体积率增大；在其它参数不变时为获得最大的冲蚀体积率则有一最佳的磨料重量比浓度和横移速度及靶距；磨料粒径大小对冲蚀体积影响不大。     

Abrasive Waterjet Machining Simulation by Coupling Smoothed Particle Hydrodynamics / Finite Element Method

In dealing with abrasive waterjet machining(AWJM) simulation,most literatures apply finite element method(FEM) to build pure waterjet models or single abrasive particle erosion models.To overcome the mesh distortion caused by large deformation using FEM and to consider the effects of both water and abrasive,the smoothed particle hydrodynamics(SPH) coupled FEM modeling for AWJM simulation is presented,in which the abrasive waterjet is modeled by SPH particles and the target material is modeled by FEM.The two parts interact through contact algorithm.Utilizing this model,abrasive waterjet with high velocity penetrating the target materials is simulated and the mechanism of erosion is depicted.The relationships between the depth of penetration and jet parameters,including water pressure and traverse speed,etc,are analyzed based on the simulation.The simulation results agree well with the existed experimental data.The mixing multi-materials SPH particles,which contain abrasive and water,are adopted by means of the randomized algorithm and material model for the abrasive is presented.The study will not only provide a new powerful tool for the simulation of abrasive waterjet machining,but also be beneficial to understand its cutting mechanism and optimize the operating parameters.     

Abrasive waterjet machining simulation by SPH method

Abrasive waterjet machining (AWJM) is a non-conventional process. The mechanism of material removing in AWJM for ductile materials and existing erosion models are reviewed in this paper. To overcome the difficulties of fluid–solid interaction and extra-large deformation problem using finite element method (FEM), the SPH-coupled FEM modeling for abrasive waterjet machining simulation is presented, in which the abrasive waterjet is modeled by SPH particles and the target material is modeled by FE. The two parts interact through contact algorithm. The creativity of this model is multi-materials SPH particles, which contain abrasive and water and mix together uniformly. To build the model, a randomized algorithm is proposed. The material model for the abrasive is first presented. Utilizing this model, abrasive waterjet penetrating the target materials with high velocity is simulated and the mechanism of erosion is depicted. The relationship between the depth of penetration and jet parameters, including water pressure and traverse speed, etc., are analyzed based on the simulation. The results agree with the experimental data well. It will be a benefit to understand the abrasive waterjet cutting mechanism and optimize the operating parameters.     

Prediction of abrasive wear rate of in situ Cu–Al2O3 nanocomposite using artificial neural networks

In this work, artificial neural networks (ANNs) technique was used in the prediction of abrasive wear rate of Cu–Al₂O₃ nanocomposite materials. The abrasive wear rates obtained from series of wear tests were used in the formation of the data sets of the ANN. The inputs to the network are load, sliding speed, and alumina volume fraction. Correlation coefficients between the experimental data and outputs from the ANN confirmed the feasibility of the ANNs for effectively model and predict the abrasive wear rate. The comparison between the ANNs and the multi-variable regression analysis results showed that using ANNs technique is more effective than multi-variable regression analysis for the prediction of abrasive wear rate of Cu–Al₂O₃ nanocomposite materials. Optimization of the training process of the ANN using genetic algorithm (GA) is performed and the results are compared with the ANN trained without GA. Sensitivity analysis is also done to find the relative influence of factors on the wear rate. It is observed that load and alumina volume fraction effectively influence the wear rate.     

Experimental and Prediction of Abrasive Wear Behavior of Sintered Cu-SiC Composites Containing Graphite by Using Artificial Neural Networks

Experimental investigations were undertaken to determine the abrasive wear behavior of various percentages of Cu-SiC-Gr hybrid composites. Wear tests were carried out using a pin-on-disc type machine using various input parameters like load, sliding distance, and sliding velocity with various SiC abrasive papers of grit size 80, 220, and 400, having an average particle size of 192, 102, and 45 μm. Neural networks are employed to study the tribological behavior of sintered Cu-SiC-Gr hybrid composites. Optical microscope, scanning electron microscope (SEM), X-ray diffraction (XRD), and energy-dispersive spectral observations are used to evaluate the characteristics. The proposed neural network model used the measured parameters, namely, the weight percentage of graphite, abrasive size, sliding speed, load, and sliding distance, to predict the wear loss of the composite. In order to improve the accuracy and obtain better results, an artificial neural network (ANN) with a genetic algorithm (GA) function was used. Optimization of the training process of the ANN using a GA is performed and the results are compared with the ANN trained without a GA. The predicted values from the proposed networks coincide with the experimental values.     

Modelling the erosion rate in micro abrasive air jet machining of glasses

Micro abrasive jet machining (MAJM) is an economical and efficient technology for micro-machining of brittle material like glasses. The erosion of brittle materials by solid micro-particles is a complex process in which material is removed from the target surface by brittle fractures. The rate of material removal is one of the most important quantities for a machining process. Predictive mathematical models for the erosion rates in micro-hole drilling and micro-channel cutting on glasses with an abrasive air jet are developed. A dimensional analysis technique is used to formulate the models as functions of the particle impact parameters, target material properties and the major process parameters that are known to affect the erosion process of brittle materials. The predictive capability of the models is assessed and verified by an experimental investigation covering a range of the common process parameters such as air pressure, abrasive mass flow rate, stand-off distance and machining time (for hole machining) or traverse speed (for channel machining). It shows that model predictions are in good agreement with the experimental results.     

超声辅助微细磨料水射流加工陶瓷试验研究

针对陶瓷材料难加工的特性,提出了超声辅助微细磨料水射流加工技术。基于响应曲面法对工程陶瓷进行切槽试验,测量加工沟槽底部表面粗糙度,通过建立表面粗糙度预测模型,分析了系统压力、超声振幅及靶距对加工质量的影响规律。当系统压力为32.8MPa、振幅为16μm、靶距为10mm时,获得最低表面粗糙度为0.746μm。通过试验验证了该预测模型的准确性和有效性。     

磨料射流切割机的试验研究

本文介绍了前混合式磨料射流切割机的结构,工作原理以及用该机对金属和非金属材料进行的切割试验,指出射流工作压力、喷射靶距、喷嘴横移速度、磨料重量比浓度等是影响工作能力的主要因素,喷嘴直径及其内腔结构、磨料粒子尺寸等也是影响割缝宽度及表面形态的因素.试验对此表明,它比纯高压水射流和后混合式磨料射流切割机性能优越,是一种新型的、有广阔应用前景的工业切割设备.     

Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks

In the present study, SiC nanoparticles were added to as-cast AZ91 magnesium alloy through friction stir processing (FSP) and an AZ91/SiC surface nanocomposite layer was produced. A relation between the FSP parameters and grain size and hardness of nanocomposite using artificial neural network (ANN) was established. Experimental results showed that distribution of nanoparticles in the stirred zone (SZ) was not uniform and SZ was divided into two regions. In the ANN modeling, the inputs included traverse speed, rotational speed, and region types. Outputs were hardness and grain size. The model can be used to predict hardness and grain size as functions of rotational and traverse speeds and region types. To check the adequacy of the ANN model, the linear regression analyses were carried out to compute the correlation coefficients. The calculated results were in good agreement with experimental data. Additionally, a sensitivity analysis was conducted to determine the parametric impact on the model outputs.     

磨料水射流切割混凝土的深度预测模型

在对磨料水射流切割混凝土分析基础上,应用BP人工神经网络理论,建立磨料水射流切割基于射流压力、靶距、磨料粒径、磨料流量、磨料喷嘴直径、磨料喷嘴长度及横移速度等射流参数的深度模型,通过模型预测结果与实验结果的比较,验证模型具有一定的精度,为实际运用和进一步研究提供参考。     

Development of multi-objective optimization models for electrochemical machining process

Owing to the complexity of electrochemical machining (ECM), it is very difficult to determine optimal cutting parameters for improving cutting performance. Hence, optimization of operating parameters is an important step in machining, particularly for unconventional machining procedures like ECM. A suitable selection of machining parameters for the ECM process relies heavily on the operator’s technologies and experience because of their numerous and diverse range. Machining parameters provided by the machine tool builder cannot meet the operator’s requirements. Since for an arbitrary desired machining time for a particular job, they do not provide the optimal conditions. To solve this task, multiple regression model and ANN model are developed as efficient approaches to determine the optimal machining parameters in ECM. In this paper, current, voltage, flow rate and gap are considered as machining parameters and metal removal rate and surface roughness are the objectives. Then by applying grey relational analysis, we calculate the grey grade for representing multi-objective model. Multiple regression model and ANN model have been developed to map the relationship between process parameters and objectives in terms of grade. The experimental data are divided into training and testing data. The predicted grade is found and then the percentage deviation between the experimental grade and predicted grade is calculated for each model. The average percentage deviations for the training data of the linear regression model, logarithmic transformation model, excluding interaction terms and ANN model, are 12.7, 25.6 and 3.03, respectively. The average percentage deviations for the testing data of the three models are 9.83, 26.8 and 2.67. While examining the average percentage deviations of three models, ANN is having less percentage deviation. So ANN is considered as the best prediction model. Based on the testing results of the artificial neural network, the operating parameters are optimized. Finally, ANOVA is used to identify the significance of multiple regression model and ANN model.     

工艺参数对磨料水射流加工性能的影响

影响高压磨料水射流加工效果的主要因素是水压力、磨料参数、喷嘴的横向移动速度和喷嘴悬高等。采用新的工艺可以有效提高加工效率和加工质量，这些新工艺主要包括：多次切割、倾角切割和喷嘴往复摆动切割。     

Development of RSM- and ANN-based models to predict and analyze the effects of process parameters of laser-hardened commercially pure titanium on heat input and tensile strength

Laser transformation hardening (LTH) is an innovative and advanced laser surface modification technique as compared to conventional transformation hardening processes and has been employed in aerospace, marine, chemical applications, heat exchangers, cryogenic vessels, components for chemical processing and desalination equipment, condenser tubing, airframe skin, and nonstructural components which introduces the advantageous residual stresses into the surface, improving the mechanical properties like wear, resistance to corrosion, tensile strength, and fatigue strength. In the present study, LTH of commercially pure titanium, nearer to ASTM grade 3 of chemical composition was investigated using continuous wave 2 kW, Nd: YAG laser. The effect of laser process variables such as laser power, scanning speed, and focused position was investigated using response surface methodology (RSM) and artificial neural network (ANN) keeping argon gas flow rate of 10 lpm as fixed input parameter. This paper describes the comparison of the heat input (HI) and ultimate tensile strength (σ) (simply called as tensile strength) predictive models based on ANN and RSM. The paper also presents the effect of laser process variables on the HI and ultimate σ. The research work also emphasizes on the effect of HI on σ. The experiments were conducted based on a three-factor, three-level Box–Behnken surface statistical design. Quadratic polynomial equations were developed for proper process parametric study for its optimal performance characteristics. The experimental results under optimum conditions were compared with the simulated values obtained from the RSM and ANN model. Adequacy of the developed models was tested by analysis of variance technique. A multilayer feed-forward neural network with a Levenberg–Marquardt back-propagation algorithm was adopted to develop the relationships between the laser hardening process parameters, HI, and ultimate σ. The performance of the developed ANN models were compared with the second-order RSM mathematical models of HI and σ. There was good agreement between the experimental and simulated values of RSM and ANN. The comparison clearly indicates that the ANN models provide more accurate prediction compared to the RSM models. It has been found that there is a trend of increased tensile strength with the decrease of hardening heat input and a trend of increased tensile strength with the increase of hardening cooling rate. As heat input decreases, there will be a faster cooling rate. Considering the effect of HI on ultimate σ, it was found that the lower the heat input, the faster cooling rate. The details of experimentation, model development, testing, validation of models, effect of laser process variables on heat input and ultimate σ, effect of HI on σ, and performance comparison of RSM and ANN models are presented in the paper. The results of Box–Behnken design of RSM and ANN models also indicate that the proposed models predict the responses adequately within the limits of input parameters being used. It is suggested that regression equations can be used to find optimum conditions for HI and σ of laser-hardened commercially pure titanium material.