Finite element prediction of blanking tool cost caused by wear

The total cost of a blanked part is determined by a large number of factors, including the material cost, manufacturing costs. Predicting the manufacturing costs of a blanked part requires accurate estimation of tool cost caused by wear. The aim of this paper is to develop a finite element model allowing for the numerical prediction of the blanking tool life which allows for the evaluation of the cost rate of blanking tool caused by wear needed to assess the total cost of a blanked part. A wear prediction model has been implemented in the finite element code Abaqus in which the tool wear is a function of the normal pressure and some material parameters. In the present work, the tool is modeled as rigid body hypothesis, and the wear variables are computed in the contacting elements. The altered tool contact surface and contact pressure tool shapes are updated iteratively to simulate wear over a long period of time of about 100,000 cycles. A damage model is used in order to describe crack initiation and propagation into the sheet. The distribution of the tool wear on the tool profile is obtained and compared to industrial observations.     

Numerical and experimental analyses of punch wear in the blanking of copper alloy thin sheet

This research on punch wear resulting from the blanking of copper alloy thin sheet has been conducted by means of experimental and numerical analyses. Firstly, the experimental method has consisted in measuring punch worn profiles from replicas, and secondly in obtaining the wear coefficient by using a specific tribometer. The numerical modelling of blanking process has been developed with the finite element method to compute the mechanical fields necessary to calculate wear. Thus, the Archard formulation for abrasive wear has been programmed to compute the wear depth and the resulting punch geometry. Finally the simulation results of wear prediction have been compared to experimental ones.     

FE modeling of burr size in high- speed micro-milling of Ti6Al4V

This article is focused on the finite element modeling of burr formation in high speed micromilling of Ti6Al4V. Studies show that the burr produced at the up milling side at the exit of the micromilling tool is the biggest among burrs at other locations. Therefore, side exit burr at the up milling side has been modeled through finite element modeling. Johnson cook material constitutive model has been implemented in the formulation of burr formation. Experimental work has been performed to validate the developed model. It is found that the burr height and width obtained from the simulation has been validated experimentally with a maximum error of 15%. It was found from the literature review that the cutting speed is the factor, which influences the burr formation. Therefore, the model has been further extended to study the effect of cutting speed on the burr size. A maximum tool rotation of 200,000 rpm was considered with a tool diameter of 500 μm. It is predicted from the simulation that, the burr size was reduced by 96% (both height and width) if cutting tool speed was increased from 10,000 to 200,000 rpm. Therefore, it is concluded that the cutting speed is the major factor to reduce the burr size in micromilling of Ti6Al4 V. This study shows that the high speed micromachining center can be helpful in producing the micro parts with less or no burrs. It is expected that further extension of the burr formation model can minimize the burr size to zero/near zero size.     

Fracture criteria identification using an inverse technique method and blanking experiment

In order to optimize the blanking processes, it is important to identify the conditions within the deforming workpiece which may lead to fracture initiation and propagation. Within this framework, numerical simulations are widely used in industries to optimize sheet metal forming processes. However, in order to have a confidence in the results of such simulations, an accurate material model is required. The accuracy of a material model is affected by the constitutive equations and the values of the material parameters. In order to reduce the danger of fracture of metal parts during manufacturing processes, advanced optimal design requires knowledge of critical values of some fracture criteria of the material used. Experimental identification of fracture criteria are currently obtained by performing several complicated tests and long duration of experiments.This study presents a computation methodology allowing for the identification of critical values of fracture criteria in order to simulate crack initiation and propagation generated by shearing mechanisms, which are needed for metal blanking processes simulation. The approach is based on inverse technique using circular blanking experiments and finite element calibration model. The critical values of fracture criteria are obtained in such a way that the finite element force–penetration predicted curve fit the experimental plot deduced from blanking tests. The numerical results obtained by the simulation were compared with experimental ones to verify the validity of the proposed technique for fracture criteria identification.     

Suitability of CAE neural networks and FEM for prediction of wear on die radii in hot forging

Prediction of tool wear in hot die forging along the entire arbor radius by wear models known so far is a very difficult task. On these parts of tools significant changes of contact pressure and sliding lengths occur along the die curvature during the plastic flow of material formed. A new approach presented in the paper combines the use of a conditional average estimator neural network (CAE NN) with the exploitation of results obtained by the finite element method (FEM) and also data from other sources. Consequently new parameters as well as the results of experimental work can be taken into account. In this paper a brief overview of models for prediction of tool (die) wear are discussed. The theoretical background of CAE NN, as well as its application to the modeling of the tool wear phenomenon, is presented. Some results of FEM analysis of the hot forging process that serve as input parameters in the CAE NN model are also briefly discussed. Two relevant practical applications are shown. In the first example, tool wear was modeled at a higher number of strokes (blows), by knowing wear at a lower number of strokes. In the second example, the number of strokes was the output parameter—the number of strokes causing predetermined wear at any point of the tool engraving curvature (arbor radius) was predicted. A comparison between the measured and predicted values of wear demonstrated good agreement that was assessed by a corresponding coefficient of determination.     

冲裁毛刺形成过程的有限元分析

采用弹塑性有限元法,运用有限元分析软件,对板料的冲裁过程进行了数值模拟,分析冲裁过程中毛刺的形成机理.设置了一系列的模具间隙、模具圆角参数,来分析模具间隙的增大和模具的磨损变化对毛刺形状的影响.模拟的结果与试验数据相对照,来预测冲裁断面的质量,优化工艺参数.     

基于ANN的冲裁合理间隙的预测研究

冲裁间隙是冲裁工艺中非常重要的工艺参数,其大小会影响冲裁件的表面质量、板料的后续成形和使用性能。不同材料,不同厚度板料的合理冲裁间隙均不同,现有方法是通过实验或查询已有的冲裁实验数据库获得。本课题应用人工神经网络技术和航空材料冲裁间隙数据库,建立板料厚度及冲裁零件的尺寸精度、光亮带比、毛刺高度与合理间隙之间非线性映射的神经网络模型,可以方便地获得不同厚度材料的冲裁合理间隙,用以指导实际生产。本文采用贝叶斯规则化的训练方法,训练好的BP网络较常用的训练方法具有更好的精度和泛化能力。预测数据和实验数据的比较证实,预测模型有效并具有实际推广应用价值。     

Inverse technique identification of material parameters using finite element and neural network computation

Experimental identification of anisotropic behavior law is currently obtained by performing several complicated tests and a long duration of experiments. This paper describes a new technique allowing for the identification of HILL anisotropic parameters by inverse technique method based on deep drawing of a cylindrical cup. The identification approach is based on the artificial neural network (ANN) computation trained from finite element simulation. The results obtained by ANN models and by the finite element method shows a good agreement.     

Heat treatment effects on tribological characteristics for AISI A8 tool steel and development of wear mechanism maps using K means clustering and neural networks

Viking steel classified under AISI A8 cold working tool steel is widely used for heavy duty blanking and forming operations. The excellent combination of wear resistance and toughness make this material superior when compared to other tool steels in such applications. This cold working tool steel is easy to machine and gives excellent mechanical properties upon proper heat treatment. The heat treatment carried out with air and oil as quenching media at various conditions of temperature and time are discussed. Optical microscopy and hardness tests are performed on the heat-treated specimens. The samples with best results from each group are selected for further analysis. The tribological properties of these specimens are analysed with the help of wear tests conducted on pin on disc tribometer under different load and velocity levels. The data obtained from these experiments are used to find out the coefficient of friction and wear rate. The experimental results are used to develop wear mechanism maps with the help of K means clustering analysis and probabilistic neural networks. A novel approach based on clustering analysis is proposed as the analysis of wear data and wear mechanism maps are developed using with neural networks for tool steel. The data analysis is correlated with the scanning electron microscopic observations and the results are discussed.     

Design of micro square endmills for hard milling applications

In experiments of machining hardened tool steels (such as AISI H11, H13, and D2, up to 56 HRC) by commercial ? 0.5?mm square endmills, it is observed that the tested micro endmills showed severe wear at an early stage of the process due to chipping off around cutting edge corners, resulting in unsatisfactory tool life and product appearance (burr formation). Detailed examination of current tool geometry shows that it is mainly inherited from that of macro endmills, making the cutting edge corners the weakest part on the tool. As the micromilling process is characterized by small values of machining parameters, the cutting edge corners of the micro endmill are the most loaded part of the cutting edges. New design rules are studied for improving the stiffness and strength of micro endmills used in micro hard milling applications. Analytical modelling and finite element method analysis are used to aid the design of tool geometry. By using a larger neck angle, optimizing tool core geometry, and choosing a negative rake angle, tool stiffness and cutting edge strength are improved. The new endmill designs, both two-flute and four-flute, are tested in experiments on hardened tool steels and showed considerable lower tool wear and increased tool life. Furthermore, the geometrical accuracy and appearance of the workpiece (burr formation) has been improved drastically.     

Improvement of Tool Life and Exit Burr using Variable Feeds when Drilling Stainless Steel with Coated Drills

Variable feed machining is a signifant method for improving the cutting tool life and exit burr height for hard and difficult to machine materials. Four coated drills were tested in the present study, namely, TiN, TiCN, CrN, and TiALN. The results indicate that the amplitude of variation of feed, a = 0.6, is optimum for maximum tool life or minimum burr height. TiN-coated drills were superior to CrN-coated or TiALN-coated drills when drilling stainless steel. The chisel wear and the outer corner wear were the dominant mechanisms of the drill wear.     

Design of Experiment Based Analysis for Sheet Metal Blanking Processes Optimisation

The blanking of metal parts for electronic components is subjected to a variety of process parameters. In this paper, an experimental investigation into the blanking process was carried out using tools with four different wear states and four different clearances. The aim was to study the effects of the interaction between the clearance, the wear state of the tool and the sheet metal thickness on the evolution of the blanking force and the geometry of the sheared profile. Designed experiments are an efficient and cost-effective way to model and analyse the relationships that describe process variations. The results of the proposed experimental investigation show the strong dependence between the geometrical quality of the blanked part and the magnitude of the force applied on the tool as well as the variations in the process factors .     

Investigations on exit burr formation mechanisms based on digital image correlation and numerical modeling

Abstract

Requirements on burr height and burr amount on machined parts are getting stricter. This leads to method development from manufacturing companies to predict burr distribution and its size along part edges. A deeper understanding of burr formation mechanisms will assist to more accurate model development. This study aims to analyze the exit burr formation, which is formed during orthogonal cutting of a brittle cast aluminum alloy. A customized digital image correlation (DIC) system with the help of a high-speed camera was used to measure the displacements fields. It calculates strain fields during burr initiation and development in orthogonal cutting of T7 heat-treated cast aluminum alloy ENAC-AlSi7Mg0.3 as well. Those results are then qualitatively compared with a numerical model of the burr with chamfer formation developed and simulated using a finite element method, to ensure a good correspondence between experiments and simulation. This model is used to complete the DIC study of burr with chamfer formation mechanisms during crack propagation leading to chamfer formation. The analysis of numerically obtained stress triaxiality fields and of DIC observations from experiments are compared to the assumptions made from analytical models. Finally, necessary improvements of an existing burr formation analytical model are proposed.     

基于神经网络和有限元法的“V”型件翻边成形性能研究

翻边是一种重要的金属成形工艺 ,曲面翻边的成形性能与几何尺寸及材料参数等因素有很大关系。本文将有限元模拟与人工智能相结合 ,建立了基于改进的BP神经网络的“V”型件翻边成形性能与几何尺寸及材料参数关系的数学模型。与单纯采用有限元法相比 ,该模型具有计算时间短和易使用的优点。与数值模拟和实验结果的比较证实了本模型的有效性。     

基于模糊神经网络的深孔加工刀具磨损率预测

刀具的过快磨损不仅增大加工成本,也影响工件的最终加工质量,因此预测和减少刀具磨损率具有重要意义。由于BP神经网络本身容易陷入局部极小值、收敛速度慢等缺陷,且深孔加工过程及其复杂,无法建立加工中刀具磨损率与加工参数之间的准确数学模型,故采用模糊神经网络建立BTA刀具磨损率在线钻削模型。仿真和实验结果表明,该模型能有效预测BTA刀具磨损率,对提高刀具寿命和加工深孔的质量具有一定的意义。     

弹簧上座零件毛刺去除方法探析及设备开发

介绍了机械零件毛刺常见的去除方法,阐述了毛刺去除的重要性,分析了弹簧上座零件毛刺形成的原因,结合生产实践设计了一款结合冲裁和气动控制原理工作的毛刺去除装置并进行实际应用,效果良好。     

An investigation of heat partition and tool wear in hard turning of H13 tool steel with CBN cutting tools

This paper presents results of an investigation into the tool life and the tool wear behaviour of low content CBN cutting tools used in hard turning of hardened H13 tool steel. The approach followed here required both experimental work and finite element thermal modelling. The experiments involved measuring the cutting forces, cutting temperatures, tool wear, and the contact area. Using the measured cutting forces and the contact area in the orthogonal cutting model, we calculated the heat flux on the tool and applied it in the FE thermal analysis. The temperatures history from the analysis was matched with the experimental data to estimate the fraction of heat entering the tool for both conventional and high speeds. The heat partition into the tool was estimated to be around 21–22% for conventional speeds, whereas for high-speed turning, it was around 14%. The tool wear, however, was found to be dominated by chipping for both cutting speeds and could be reduced considerably by reducing the amount of heat entering the tool.     

基于Archard模型的机床导轨磨损模型及有限元分析

机床导轨的磨损是导致机床精度降低或丧失的重要因素。针对机床导轨磨损深度的预测问题,提出了一种基于Archard模型与有限元模拟试验的机床导轨磨损分析方法。该方法基于修正的Archard模型形成一种磨损深度的计算公式,并采用一种离散化计算方法进行求解;结合ANSYS软件,提出机床导轨磨损有限元分析模拟的方法及流程;最后,以车床滑动导轨为例,用有限元试验方法对机床导轨的磨损过程进行模拟分析,得到磨损深度关于磨损次数的计算公式,其结果表明:机床导轨磨损深度的仿真模拟值与实际值具有一致性。     

Hybrid Learning for Tool Wear Monitoring

In automated manufacturing systems such as flexible manufacturing systems (FMSs), one of the most important issues is the detection of tool wear during the cutting process. This paper presents a hybrid learning method to map the relationship between the features of cutting vibration and the tool wear condition. The experimental results show that it can be used effectively to monitor the tool wear in drilling. First, a neural network model with fuzzy logic (FNN), responding to learning algorithms, is presented. It has many advantageous features, compared to a backpropagation neural network, such as less computation. Secondly, the experimental results show that the frequency distribution of vibration changes as the tool wears, so the r.m.s. of the different frequency bands measured indicates the tool wear condition. Finally, FNN is used to describe the relationship between the characteristics of vibration and the tool wear condition. The experimental results demonstrate the feasibility of using vibration signals to monitor the drill wear condition.