Finite element simulation and experimental investigation of tool temperature during ultrasonically assisted turning of aerospace aluminum using multicoated carbide inserts

This paper summarizes the results of thermal finite element simulation and experimental studies of tool temperature in ultrasonic-assisted turning (UAT) of aerospace aluminum using multicoated carbide inserts. At first, mathematical models were developed in order to study the effects of tool coating, rake angle, cutting speed, and feed rate on the friction coefficient. Then with respect to the kinematics of the process, the cutting velocity model would be presented. This velocity model is used in combination with the mathematical model to define the friction coefficient during UAT. The mentioned frictional model is used to write a user subroutine to incorporate the effect of friction coefficient as a function of cutting parameters in the finite element program Abaqus. The results of this simulation make it possible to determine cutting temperature patterns accurately. It is also used to study the effect of cutting parameters (cutting speed, feed rate, rake angle, and vibration amplitude) on UAT. Finally, the simulation results are compared with experimental measurements of cutting temperatures from ultrasonic-assisted turning tests. The results show that ultrasonic-assisted turning is able to lower the maximum cutting temperature in cutting tool, about 29 %, in low feed rates (≈0.14 mm/rev), with a vibration amplitude of ≈10 μm and work velocity of ≈0.5 m/s.     

Optimisation of machining parameters for turning operations based on response surface methodology

Design of experiments has been used to study the effect of the main turning parameters such as feed rate, tool nose radius, cutting speed and depth of cut on the surface roughness of AISI 410 steel. A mathematical prediction model of the surface roughness has been developed in terms of above parameters. The effect of these parameters on the surface roughness has been investigated by using Response Surface Methodology (RSM). Response surface contours were constructed for determining the optimum conditions for a required surface roughness. The developed prediction equation shows that the feed rate is the main factor followed by tool nose radius influences the surface roughness. The surface roughness was found to increase with the increase in the feed and it decreased with increase in the tool nose radius. The verification experiment is carried out to check the validity of the developed model that predicted surface roughness within 6% error.     

Finite element optimization of diamond tool geometry and cutting-process parameters based on surface residual stresses

In this paper, a coupled thermo-mechanical plane-strain large-deformation orthogonal cutting FE model is proposed on the basis of updated Lagrangian formulation to simulate diamond turning. In order to consider the effects of a diamond cutting tool’s edge radius, rezoning technology is integrated into this FE based model. The flow stress of the workpiece is modeled as a function of strain, strain rate, and temperature, so as to reflect its dynamic changes in physical properties. In this way, the influences of cutting-edge radius, rake angle, clearance angle, depth of cut, and cutting velocity on the residual stresses of machined surface are analyzed by FE simulation. The simulated results indicate that a rake angle of about 10° and a clearance angle of 6° are the optimal geometry for a diamond tool to machine ductile materials. Also, the smaller the cutting edge radius is, the less the residual stresses become. However, a great value can be selected for cutting velocity. For depth of cut, the ‘size effect’ will be dependent upon it. Residual stresses will be reduced with the decrement of depth of cut, but when the depth of cut is smaller than the critical depth of cut (i.e., about 0.5 μm according to this work) residual stresses will decrease accordingly.     

Analysis and prediction of tool wear, surface roughness and cutting forces in hard turning with CBN tool

The main of the present study is to investigate the effects of process parameters (cutting speed, feed rate and depth of cut) on performance characteristics (tool life, surface roughness and cutting forces) in finish hard turning of AISI 52100 bearing steel with CBN tool. The cutting forces and surface roughness are measured at the end of useful tool life. The combined effects of the process parameters on performance characteristics are investigated using ANOVA. The composite desirability optimization technique associated with the RSM quadratic models is used as multi-objective optimization approach. The results show that feed rate and cutting speed strongly influence surface roughness and tool life. However, the depth of cut exhibits maximum influence on cutting forces. The proposed experimental and statistical approaches bring reliable methodologies to model, to optimize and to improve the hard turning process. They can be extended efficiently to study other machining processes.     

Tool wear estimation in machining based on the flank wear inclination angle changes using the FE method

Abstract

In this study, a FEM-based tool wear approach with a focus on the geometry of the worn tool, especially the changes of flank wear land inclination angle, was developed. The relationship between the variables of the wear rate equation and the average nodal temperature on the flank wear land through integrating FE-simulations of the cutting process and Response Surface Methodology (RSM) was determined in order to define the temperature as a function of wear rate model parameters. Then, that data was used to calibrate the wear rate equation which was obtained by establishing the relationship between the Usui wear rate equation and the geometry of the worn tool, using a MATLAB program. This approach was validated by comparing the predicted flank wear rates and experimental measurements. The estimated flank wear shows some improvement compare to the model with a constant inclination angle.     

On the prediction of surface roughness in the hard turning based on cutting parameters and tool vibrations

This research work concerns the elaboration of a surface roughness model in the case of hard turning by exploiting the response surface methodology (RSM). The main input parameters of this model are the cutting parameters such as cutting speed, feed rate, depth of cut and tool vibration in radial and in main cutting force directions. The machined material tested is the 42CrMo4 hardened steel by Al₂O₃/TiC mixed ceramic cutting tool under different conditions. The model is able to predict surface roughness of Ra and Rt using an experimental data when machining steels. The combined effects of cutting parameters and tool vibration on surface roughness were investigated while employing the analysis of variance (ANOVA). The quadratic model of RSM associated with response optimization technique and composite desirability was used to find optimum values of cutting parameters and tool vibration with respect to announced objectives which are the prediction of surface roughness. The adequacy of the model was verified when plotting the residuals values. The results indicate that the feed rate is the dominant factor affecting the surface roughness, whereas vibrations on both pre-cited directions have a low effect on it. Moreover, a good agreement was observed between the predicted and the experimental surface roughness. Optimal cutting condition and tool vibrations leading to the minimum surface roughness were highlighted.     

带卷屑槽刀具最大前角的数学模型与求解

由于现代数控刀具都设计有卷屑槽,从而使其前刀面不是平面,而是曲面。本文针对前刀面为圆柱面的一部分时,采用向量矩阵法建立最大前角计算的数学模型,应用该模型可以在给定条件下,计算曲面型前刀面车刀的最大前角和其它重要的几何参数。最后,给出了计算实例。     

OPTIMAL MQL AND CUTTING CONDITIONS DETERMINATION FOR DESIRED SURFACE ROUGHNESS IN TURNING OF BRASS USING GENETIC ALGORITHMS

The evolving concept of minimum quantity of lubrication (MQL) in machining is considered as one of the solutions to reduce the amount of lubricant to address the environmental, economical and ecological issues. This paper investigates the influence of cutting speed, feed rate and different amount of MQL on machining performance during turning of brass using K10 cemented carbide tool. The experiments have been planned as per Taguchi's orthogonal array and the second order surface roughness model in terms of machining parameters was developed using response surface methodology (RSM). The parametric analysis has been carried out to analyze the interaction effects of process parameters on surface roughness. The optimization is then carried out with genetic algorithms (GA) using surface roughness model for the selection of optimal MQL and cutting conditions. The GA program gives the minimum values of surface roughness and the corresponding optimal machining parameters.     

Finite element modeling and simulation in dry hard orthogonal cutting AISI D2 tool steel with CBN cutting tool

The influences of cutting parameters on temperature, stress, and shear angle during dry hard orthogonal cutting (DHOC) of D2 tool steel (62?±?1 HRC) are investigated in this paper. Temperature and stress are considered the most important aspects to be taken into account in dry hard machining; however, dry hard machining is a complex process, and the temperature fields and residual stress are the most difficult to be measured. Up to now, only very few studies have been reported on influences of cutting parameters on shear angle, temperature, and stress of AISI D2 tool steel (62?±?1 HRC). In this paper, the Johnson–Cook model is utilized to propose a finite element (FE) model. The FE model is properly calibrated by means of an iterative procedure based on the comparison between experimental resultant forces obtained from literatures and simulated resultant forces. At last, this FE model is utilized to predict the influences of cutting speed and depth of cut on temperature fields and residual stress within a workpiece, cutting tool edge temperature, and shear angle during DHOC hardened AISI D2 tool steel (62?±?1 HRC) and validated by experimental results. As shown in this investigation, it is also possible to properly analyze the influences of cutting parameters on the cutting mechanism for industrial application.     

Finite element simulation of diamond tool geometries affecting the 3D surface topography in fly cutting of KDP crystals

Diamond tool has significant influences on the finished surface quality in fly cutting of potassium dihydrogen phosphate (KDP) crystals. In this work, the nanoindentation and dimensional analysis are employed to establish the material constitutive equation of KDP crystals, i.e., the variation curve of flow stress vs. plastic strain. As expected, a novel 3D finite element (FE) model is developed for diamond fly cutting of KDP crystals, and the generation of 3D surface topography is simulated by multi-run cutting calculations, in which the movements of diamond tool are configured to be identical to the actual feed rate and cutting velocity. Subsequently, the coordinates of the nodes on the topmost surface as freshly machined are collected to evaluate the surface roughness, which enables the detailed analyses of the effect of diamond tool geometries on the achieved surface roughness of KDP crystals. The results suggest an optimal selection of tool geometries, i.e. ?25° rake angle and 8° clearance angle. With the increment of tool nose radius, surface roughness decreases correspondingly. Moreover, the larger defect or sharpness of tool cutting edge produces the worse surface roughness. Diamond fly cutting experiments are carried out with different rake angles, in which the cutting parameters are the same as the values used in FE simulations. The measured surface roughness has a satisfied consistency with the simulated data, which demonstrates that the developed 3D FE cutting model and the related simulations are reliable.     

基于实验Inconel718正交切削有限元仿真分析

为研究犁削效应和前刀面粘压对Inconel718切削过程的影响.基于正交切削实验建立Inconel718有限元切削模型,模型结果同实验值对比以验证模型可靠性.通过改变刀具圆角半径和负前角参数,提取并比较不同的切削力时域曲线和刀具温度,分析犁削效应和前刀面粘压.研究表明犁削效应提高进给力数值,刀具圆角半径由0变为5μm,Inconel718切削进给力均值提高7％：前刀面粘压提高刀具和切屑温度,有利于切屑分离.但刀具负前角为-20＆#176;,切削加工不稳定.     

A study on the cutting force and chip shrinkage coefficient in high-speed milling of A6061 aluminum alloy

In this paper, finite element (FE) simulation for high-speed milling of aluminum alloy was performed using a ductile fracture model with Mohr–Coulomb criterion proposed by Bai and Wierzbicki (BW). To verify the model, predicted cutting forces were compared to experimental results in the same cutting conditions. Then, further simulations were performed to estimate the cutting forces and chip shrinkage coefficients subjected to different cutting parameters such as cutting speeds, cutting depths, and clearance angles of a cutting tool. The obtained results were also used to determine optimal cutting parameters using the Taguchi method. The analysis of variance (ANOVA) was employed to investigate the influence percentage of each cutting parameter on cutting force and chip shrinkage coefficient. The simulation results showed that inclusion of strain rate in numerical model significantly improved the accuracy of estimated cutting force in comparison to experiment. The optimum values obtained for high-milling process were cutting speed 1000 m/min, cutting depth 1 mm, clearance angle 15°, and rake angle 4°.     

Experimental characterization and finite element modeling of critical thrust force in cfrp drilling

Composite laminates are used in many applications in ae-rospace/defense industries due to their high strength-to-weight ratio and corrosion resistance properties. In general, composite materials are hard-to-machine materials which exhibit low drilling efficiency and drilling-induced delamination damage at exit. Hence, it is important to understand the drilling processes for composite materials. This article presents a comprehensive study involving experimental characterization of drilling process to understand the cutting mechanism and relative effect of cutting parameters on delamination during drilling of carbon fiber reinforced plastic (CFRP). Thrust force and torque data are acquired for analyzing the cutting mechanism, initiation and propagation of delamination, and identification of critical thrust force below which no damage occurs. An FE model for prediction of critical thrust force has been developed and validated with experimental results. A [0/90] composite laminate is modeled simulating the last two plies in exit condition and a thin interface layer is inserted in between the plies to capture delamination extent. The tool geometry is modeled as “rigid body” with geometric features of twist drill used in experiments. The tool is indented on the workpiece to simulated tool feeding action into the workpiece. The FE model predicts the critical thrust force within 5% of the experimentally determined mean value.     

高速车削镍基高温合金GH4169的切削力仿真研究

基于Deform 3D仿真软件建立了GH4169高温合金高速车削的有限元模型,采用四因素三水平正交试验方法研究了切削用量和刀具几何参数对切削力的影响规律,并建立了切削力经验公式。研究结果表明:在高速车削GH4169的过程中,对切削力影响最大的参数是切削深度,其次是进给量和前角,最后是刀尖圆弧半径;切削力随切削深度和进给量的增大而增大,随前角的增大呈现先降低又升高的趋势,而刀尖圆弧半径增大时切削力变化不大;最佳参数组合为:进给量0.2mm/r,切削深度0.4mm,前角10°,刀尖圆弧半径0.2mm。     

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.     

Modeling and optimization of hard turning of X38CrMoV5-1 steel with CBN tool: Machining parameters effects on flank wear and surface roughness

The present study, aims to investigate, under turning conditions of hardened AISI H11 (X38CrMoV5-1), the effects of cutting parameters on flank wear (VB) and surface roughness (Ra) using CBN tool. The machining experiments are conducted based on the response surface methodology (RSM). Combined effects of three cutting parameters, namely cutting speed, feed rate and cutting time on the two performance outputs (i.e. VB and Ra), are explored employing the analysis of variance (ANOVA). Optimal cutting conditions for each performance level are established and the relationship between the variables and the technological parameters is determined using a quadratic regression model. The results show that the flank wear is influenced principally by the cutting time and in the second level by the cutting speed. Also, it is that indicated that the feed rate is the dominant factor affecting workpiece surface roughness.     

Autonomous cutting parameter regulation using adaptive modeling and genetic algorithms

In this research, a turning process is modeled adaptively by a backpropagation, multilayered neural network with an iterative learning method, and cutting parameters of the process model are optimized through genetic algorithms (GAs). Some constraints were given on the input conditions and the process outputs to provide for the desired surface integrity and to protect the machine tool. Introducing penalty values, which are included in the fitness evaluation of the GAs, we can solve such a constrained problem. Experimental results show that the neural network has the ability to model the turning process on-line, and such cutting conditions as spindle speed and feed rate can be adaptively regulated for maximizing the material removal rate using the GAs.     

Mathematical model of micro turning process

In recent years, significant advances in turning process have been achieved greatly due to the emergent technologies for precision machining. Turning operations are common in the automotive and aerospace industries where large metal workpieces are reduced to a fraction of their original weight when creating complex thin structures. The analysis of forces plays an important role in characterizing the cutting process, as the tool wear and surface texture, depending on the forces. In this paper, the objective is to show how our understanding of the micro turning process can be utilized to predict turning behavior such as the real feed rate and the real cutting depth, as well as the cutting and feed forces. The machine cutting processes are studied with a different model compared to that recently introduced for grinding process by Malkin and Guo (2006). The developed two-degrees-of-freedom model includes the effects of the process kinematics and tool edge serration. In this model, the input feed is changing because of current forces during the turning process, and the feed rate will be reduced by elastic deflection of the work tool in the opposite direction to the feed. Besides this, using the forces and material removal during turning, we calculate the effective cross-sectional area of cut to model material removal. With this model, it is possible for a machine operator, using the aforementioned turning process parameters, to obtain a cutting model at very small depths of cut. Finally, the simulated and experimental results prove that the developed mathematical model predicts the real position of the tool tip and the cutting and feed forces of the micro turning process accurately enough for design and implementation of a cutting strategy for a real task.     

Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches

Optimization techniques using evolutionary algorithm (EA) are becoming more popular in engineering design and manufacturing activities because of the availability and affordability of high-speed computers. In this work, an attempt was made to solve multi-objective optimization problem in turning by using multi-objective differential evolution (MODE) algorithm and non-dominated sorting genetic algorithm(NSGA-II). Optimization in turning means determination of the optimal set of machining parameters to satisfy the objectives within the operational constraints. These objectives may be minimum tool wear, maximum metal removal rate or any weighted combination of both. The main machining parameters which are considered as variables of the optimization are cutting speed, feed rate, and depth of cut. The optimum set of these three input parameters is determined for a particular job-tool combination of EN24 steel and tungsten carbide during a single-pass turning which minimizes the tool wear and maximizes the metal removal rate after satisfying the constraints of temperature and surface roughness. The regression models, developed for tool wear, temperature, and surface roughness were used for the problem formulation. The non-dominated solution set obtained from MODE was compared with NSGA-II using the performance metrics and reported     

In-process modeling of dynamic tool-tip temperatures of a tunable vibration turning device operating at ultrasonic frequencies

The development of a model used to describe the mechanism by which vibration assisted machining reduces tool temperature is discussed, and correlations to resulting reduction in tool wear are presented. This model is applied to a newly developed ultrasonic, vibration assisted diamond turning device that allows for variation of vibration frequency and vibration amplitude via a direct drive actuator. It accommodates a wide range of vibration parameters, including vibration frequencies up to 40 kHz and amplitudes up to 8 μm, where the tool operates. The model uses the finite element method to predict cutting temperatures under conventional turning conditions (i.e., without vibration assistance). The results from the finite element analysis are then used in conjunction with a model developed for vibration assisted machining to predict the new temperature profiles. The modeling techniques and temperature histories for various vibration conditions are presented as well as experimental results that show the thermal advantages of applying tool vibration.