基于有限元分析的二轴柔性滚弯过程影响因素的研究

利用弹性介质对钣金件进行二轴柔性滚弯成形是一种先进的钣金制造工艺,将弹性介质(聚氨酯橡胶)的冲压优势和传统滚弯原理结合,成为钣金成形领域的一个新的发展方向。本文利用有限元软件MARC建立二轴柔性滚弯过程的有限元分析模型,成功的模拟了板料滚弯成形及回弹的加工过程,对工件滚弯成形过程的主要影响因素进行了分析,给出了压入深度、柔性层厚度、刚性滚轴半径、材料性能与回弹后曲率半径的关系。分析结果表明,有限元模拟对滚弯过程的工艺参数选取有着一定的指导意义。     

双轴柔性滚弯技术的有限元分析

利用有限元软件 ANSYS建立了双轴柔性滚弯技术的分析模型 ,成功模拟了工件滚弯成形和回弹的实际过程。利用有限元计算结果 ,对影响工件回弹成形的参数进行了分析研究 ,验证了实验中得出的柔性滚弯技术的重要特征。与实验结果对比 ,本文有限元分析具有较高的精度 ,有一定的实际应用价值     

单轴柔性滚弯成形的有限元分析研究

利用弹性介质(聚氨酯橡胶)对钣金件进行单轴柔性滚弯成形是一种先进的钣金制造工艺。论文介绍了单轴柔性滚弯的工作原理,同时,将大变形弹塑性有限元引入单轴柔性滚弯成形领域,利用高级非线性有限元软件MSC/MARC,建立了单轴柔性滚弯挤压矩形管成形过程的三维有限元模型,成功模拟了板料滚弯成形及回弹的实际过程,对工件滚弯成形过程的主要影响因素进行了分析,获得了滚轴的压入量、橡胶硬度、滚轴直径与回弹后工件的曲率半径之间的关系曲线。     

2024铝合金框板四轴滚弯有限元数值分析

四轴滚弯是常用来成形大尺寸型材的有效方法。针对典型2024铝合金框板零件,利用大型非线性有限元软件ABAQUS对其四轴滚弯过程进行了有限元数值分析,获得了滚弯过程中的工艺参数。结果表明,当左右滚轮上升量为35 mm时,可成形该框板零件;随着左右滚轮上升量的增加,成形曲率半径减小。滚弯过程中,框板应力中性层位于腹板部位且框板经历先加载后卸载的2个阶段。     

聚氨酯轮硬度对小直径开缝衬套成形性能的影响

通过不同硬度聚氨酯材料单轴拉伸试验,建立了聚氨酯轮的Mooney-Rivlin超弹性材料本构模型;利用ABAQUS有限元软件对小直径开缝衬套双轴柔性滚弯制备过程进行有限元模拟,探究了聚氨酯轮硬度对开缝衬套成形直径的影响趋势;结合仿真参数进行了小直径开缝衬套双轴柔性滚弯工艺试验,并用MATLAB软件的直径拟合程序识别出仿真和试验中的带材成形直径。结果表明,相同挤压量下,随着聚氨酯轮硬度的增加,带材的成形直径减小,尤其在制备小直径开缝衬套时,使用高硬度的聚氨酯轮制备出的开缝衬套成形质量好,曲率一致,基本没有出现直边现象。     

型材变曲率滚弯过程有限元模拟

采用大型有限元分析软件ANSYS建立了型材滚弯成形弹塑性有限元模型,成功地完成了型材变曲率滚弯过程的数值模拟,数值模拟结果与生产实际情况符合较好。利用有限元模型,分析了变曲率型材零件滚弯成形后的残余应力分布,研究了滚弯成形后型材剖面角度的变化,可以为实际生产过程中参数的选择和滚弯工装的设计提供有力的帮助。     

小直径开缝衬套双轴柔性滚弯成形的三维有限元分析

基于双轴柔性滚弯技术原理,建立了采用0Cr15Ni7Mo2Al材料制备小直径开缝衬套过程的三维有限元模型,以指导实际小直径开缝衬套的制备。利用该模型分析了衬套应力应变分布、刚性轴受力变形情况、衬套成形曲率半径与刚性轴直径的关系及衬套成形曲率半径与带材尺寸的关系,并用滚弯试验验证了该模型的准确性。结果说明:带材滚弯变形随着进给量的增加而更均匀;刚性轴受力随其直径的减小而减小,刚性轴变形程度随其直径的减小而增大;衬套成形曲率半径随着刚性轴直径的增大而增大;衬套成形曲率半径随着带材宽度和厚度的增大而增大。     

大截面Z型材数控滚弯成形精度的数值模拟研究

为了提高型材滚弯成形外形轮廓的精度,研究了Z型材滚弯成形回弹变形规律,建立了型材成形半径与滚弯机侧滚轮位移的关系,给出了考虑回弹变形的型材滚弯成形工艺参数。基于ABAQUS有限元软件,建立了型材滚弯成形过程的三维模型,分析了主滚轮尺寸、夹紧力和摩擦系数对型材沿长度方向各段曲率变化的影响。结果表明:主滚轮尺寸越大,曲率分布越均匀;并确定出该型材滚弯成形时合理的夹紧力和摩擦系数范围。应用模拟分析确定的最佳工艺参数完成该型材的滚弯成形加工,验证了该参数对滚弯型材外形精度的提高作用。     

不同厚度3/4硬301型不锈钢带材双轴柔性滚弯成形性能研究

利用3/4硬301型不锈钢带材制备直筒开缝衬套,研究不同厚度带材的双轴柔性滚弯成形性能。首先,通过拉伸实验以及文献中的性能参数,建立3/4硬301型不锈钢带材的本构模型,利用有限元分析软件ABAQUS对不同厚度带材在同一挤压量情况下的双轴柔性滚弯成形过程进行了仿真分析;其次,基于挤压量单因素试验对不同厚度带材的双轴柔性滚弯成形实验进行了研究,获得了一系列成形试样;最后,利用MATLAB拟合程序识别出实验及仿真结果的带材成形半径,获得了带材厚度对其双轴柔性滚弯成形性能的影响规律,且揭示了其影响机制。     

柔性滚弯成形技术研究进展

柔性滚弯成形技术主要用于制造形状多样化、生产批量小的复杂薄壁曲面制件,广泛运用于飞机、船舶、火箭等运载工具。总结了柔性滚弯成形技术在国内外的研究现状,介绍了刚性辊滚压橡胶垫的单轴柔性滚弯成形技术;刚性辊和柔性辊相互作用或者双柔性辊相互作用的双轴柔性滚弯成形技术;可弯曲、可调节的三柔性辊相互作用的曲面连续成形技术。对新型工艺技术的基本原理以及工艺特点进行了分析和总结。     

Mill-cut: a neural network system for the prediction of thermo-mechanical loads induced in end-milling operations

This paper presents the design and implementation issues of a generalized system called mill-cut, developed for the prediction of cutting forces and temperature in end-milling operations. Based on an ANN approach, mill-cut predicts all the three components of cutting forces and average shear plane temperature for a given set of machining parameters broadly categorized into three groups viz. (i) cutting tool geometrical parameters (ii) cutting parameters and (iii) workpiece material properties. In the present work, for representing overall machining condition, 15 machining parameters having major impact on the cutting forces and cutting temperature were chosen. The feed-forward back-propagated ANN architecture has been incorporated, which was initially trained with analytical data before incorporating it as part of an integrated system. Results obtained from the proposed model show good agreement with the experimental/numerical (FEM based) results available in the literature.     

Development of an intelligent process model for EDM

This paper reports the development of an intelligent model for the electric discharge machining (EDM) process using finite-element method (FEM) and artificial neural network (ANN). A two-dimensional axisymmetric thermal (FEM) model of single-spark EDM process has been developed based on more realistic assumptions such as Gaussian distribution of heat flux, time- and energy-dependent spark radius, etc. to predict the shape of crater cavity, material removal rate, and tool wear rate. The model is validated using the reported analytical and experimental results. A neural-network-based process model is proposed to establish relation between input process conditions (discharge power, spark on time, and duty factor) and the process responses (crater geometry, material removal rate, and tool wear rate) for various work—tool work materials. The ANN model was trained, tested, and tuned using the data generated from the numerical (FEM) simulations. The ANN model was found to accurately predict EDM process responses for chosen process conditions. It can be used for the selection of optimum process conditions for EDM process.     

基于响应曲面法的珩磨工件圆柱度预测模型和工艺参数优化研究

建立与珩磨工艺参数相关的内孔圆柱度预测模型,有利于快速确定优化的工艺参数,控制加工精度。采用二次通用旋转组合设计方法拟定试验方案,在试验基础上建立了与冲程长度、珩磨压力、旋转速度及每次珩磨量等工艺参数相关的内孔圆柱度预测模型,用Design-Expert 8.0对预测模型进行分析,优化了圆柱度预测模型。分析珩磨工件圆柱度与工艺参数的二阶响应曲面图,获得了优化的珩磨工艺参数组合。验证试验表明,基于响应曲面法的珩磨工件圆柱度预测模型是准确的,优化工艺参数是有效的。     

基于AR模型的转台位置预测方法研究

文中以时栅传感器作为CNC系统的位置检测元件,为了解决时栅数控转台在高精度伺服控制过程中的动态位置反馈误差问题,研究了利用AR模型对转台位置进行预测测量的方法。介绍了预测测量的原理、预测方法及其模型系数的求解方法,并搭建了一套实验装置。经实验表明,基于AR模型的转台位置预测测量方法正确可行,通过修正后预测误差为±2″.     

车削中三维复杂断屑槽刀具断屑仿真研究

为有效缩短现有断屑槽刀具的设计周期、降低设计成本,采用有限元方法模拟了切削过程中切屑折断过程。利用Solid Works软件建立了三种刀具的三维模型,并在Deform 3D软件中对车削45钢工件过程进行了三维切削仿真。其中,工件材料采用了Johnson-Cook模型和Cockroft-Latham韧性断裂准则,仿真模型采用了有效参数设置以保证数值计算精度与效率。通过仿真研究了不同切削参数下的切屑形态、断屑过程及主切削力等。研究结果表明,仿真结果与试验结果吻合良好,该仿真模型及方法能有效应用于断屑槽刀具断屑性能研究,是三维复杂断屑槽刀具设计和切削参数优化的一种新方法。     

ANN-based prediction of surface and hole quality in drilling of AISI D2 cold work tool steel

This paper focuses on artificial neural network (ANN)-based modeling of surface and hole quality in drilling of AISI D2 cold work tool steel with uncoated titanium nitride (TiN) and titanium aluminum nitride (TiAlN) monolayer- and TiAlN/TiN multilayer-coated-cemented carbide drills. A number of drilling experiments were conducted at all combinations of different cutting speeds (50, 55, 60, and 65 m/min) and feed rates (0.063 and 0.08 mm/rev) to obtain training and testing data. The experimental results showed that the surface roughness (Ra) and roundness error (Re) values were obtained with the TiN monolayer- and TiAlN/TiN multilayer-coated drills, respectively. Using some of the experimental data in training stage, an ANN model was developed. To evaluate the performance of the developed ANN model, ANN predictions were compared with the experimental results. It was found that the determination coefficient values are more than 0.99 for both training and test data. Root mean square error and mean error percentage values were very low. ANN results showed that ANN can be used as an effective modeling technique in accurate prediction of the Ra and Re.     

Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation (BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body. Genetic algorithm (GA) is adopted to optimize the back-propagation neural network connection weights. The training and validating samples are made by the BTM(R) Tog-L-Loc system with different technologic parameters. The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network (ANN) for training. The validating samples' experimental data is used for checking up the prediction outputs. The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network. The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints. The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.     

On the FSW of AA2024-T4 and AA7075-T6 T-joints: an industrial case study

The paper presents an artificial neural network-optimization hybrid model to predict and optimize penetration depth of CO₂ LASER-MIG hybrid welding used for 5005 Al–Mg alloy. The input welding parameters are power, focal distance from the work piece surface, torch angle, and the distance between the laser and the welding torch. The model combines single hidden layer back propagation artificial neural networks (ANN) with Bayesian regularization for prediction and quasi-Newton search algorithm for optimization. In this method, training and prediction performance of different ANN architectures are initially tested, and the architecture with the best performance is further used for optimization. Finally, the best ANN architecture is found to show much better prediction capability compared to a regression model developed from the experimental data.     

Artificial neural network and regression models for performance prediction of abrasive waterjet in rock cutting

An experimental study is carried out for modeling the rock cutting performance of abrasive waterjet. Kerf angle (KA) is considered as a performance criteria and modeled using artificial neural network (ANN) and regression analysis based on operating variables. Three operating variables, including traverse speed, standoff distance, and abrasive mass flow rate, are studied for obtaining different results for the KA. Data belonging to the trials are used for construction of ANN and regression models. The developed models are then tested using a test data set which is not utilized during construction of models. Additionally, the regression model is validated using various statistical approaches. The results of regression analysis are also used to determine the significant operating variables affecting the KA. Furthermore, the performances of derived models are compared for showing the accuracy levels in prediction of the KA. As a result, it is concluded that both ANN and regression models can give adequate prediction for the KA with an acceptable accuracy level. The compared results reveal also that the corresponding ANN model is more reliable than the regression model. On the other hand, the standoff distance and traverse speed are statistically determined as dominant operating variables on the KA, respectively.     

An optimization method for radial forging process using ANN and Taguchi method

In this study, the artificial neural network (ANN) and the Taguchi method are employed to optimize the radial force and strain inhomogeneity in radial forging process. The finite element analysis of the process verified by the microhardness test (to confirm the predicted strain distribution) and the experimental forging load published by the previous researcher are used to predict the strain distribution in the final product and the radial force. At first, a combination of process parameters are selected by orthogonal array for numerical experimenting by Taguchi method and then simulated by FEM. Then the optimum conditions are predicted via the Taguchi method. After that, by using the FEM results, an ANN model was trained and the optimum conditions are predicted by means of ANN (using genetic algorithm as global optimization procedure) and compared with those achieved by the Taguchi method. The optimum conditions are verified by FEM, and good agreement is found between the two sets of results.