Experimental study of surface roughness in slot end milling AL2014-T6

The aim of this work was to analyze the influence of cutting condition and tool geometry on surface roughness when slot end milling AL2014-T6. The parameters considered were the cutting speed, feed, depth of cut, concavity and axial relief angles of the end cutting edge of the end mill. Surface roughness models for both dry cutting and coolant conditions were built using the response surface methodology (RSM) and the experimental results. The results showed that the dry-cut roughness was reduced by applying cutting fluid. The significant factors affecting the dry-cut model were the cutting speed, feed, concavity and axial relief angles; while for the coolant model, they were the feed and concavity angle. Surface roughness generally increases with the increase of feed, concavity and axial relief angles, while concavity angle is more than 2.5°.     

An improved cutting force and surface topography prediction model in end milling

During the milling operation, the cutting forces will induce vibration on the cutting tool, the workpiece, and the fixtures, which will affect the surface integrity of the final part and consequently the product's quality. In this paper, a generic and improved model is introduced to simultaneously predict the conventional cutting forces along with 3D surface topography during side milling operation. The model incorporates the effects of tool runout, tool deflection, system dynamics, flank face wear, and the tool tilting on the surface roughness. An improved technique to calculate the instantaneous chip thickness is also presented. The model predictions on cutting forces and surface roughness and topography agreed well with experimental results.     

Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

In machining of parts, surface quality is one of the most specified customer requirements. Major indication of surface quality on machined parts is surface roughness. Finish hard turning using Cubic Boron Nitride (CBN) tools allows manufacturers to simplify their processes and still achieve the desired surface roughness. There are various machining parameters have an effect on the surface roughness, but those effects have not been adequately quantified. In order for manufacturers to maximize their gains from utilizing finish hard turning, accurate predictive models for surface roughness and tool wear must be constructed. This paper utilizes neural network modeling to predict surface roughness and tool flank wear over the machining time for variety of cutting conditions in finish hard turning. Regression models are also developed in order to capture process specific parameters. A set of sparse experimental data for finish turning of hardened AISI 52100 steel obtained from literature and the experimental data obtained from performed experiments in finish turning of hardened AISI H-13 steel have been utilized. The data sets from measured surface roughness and tool flank wear were employed to train the neural network models. Trained neural network models were used in predicting surface roughness and tool flank wear for other cutting conditions. A comparison of neural network models with regression models is also carried out. Predictive neural network models are found to be capable of better predictions for surface roughness and tool flank wear within the range that they had been trained.Predictive neural network modeling is also extended to predict tool wear and surface roughness patterns seen in finish hard turning processes. Decrease in the feed rate resulted in better surface roughness but slightly faster tool wear development, and increasing cutting speed resulted in significant increase in tool wear development but resulted in better surface roughness. Increase in the workpiece hardness resulted in better surface roughness but higher tool wear. Overall, CBN inserts with honed edge geometry performed better both in terms of surface roughness and tool wear development.     

Active integration of tool deflection effects in end milling. Part 2. Compensation of tool deflection

This study presents a compensation method in milling machining in order to take into account tool deflection during tool-path generation. Tool deflection that occurs during machining, and especially when flexible tools such as end mills are used, can result in dimensional errors on workpieces. The study presented here is part two of a two-part paper. In part one the cutting force models and the surface prediction method have been presented.Here the focus is on tool deflection effects' integration during the generation of the tool path. A strategy is proposed that modifies the nominal tool trajectory, compensates for the machining errors due to tool deflection, without degrading the production performance and the machined accuracy. The methodology allows optimization of the tool path trajectory in order to achieved a specified tolerance. Some experimental results are presented.     

Experimental investigation of effects of cutting parameters on surface roughness in the WEDM process

The experimental study presented in this paper aims to select the most suitable cutting and offset parameter combination for the wire electrical discharge machining process in order to get the desired surface roughness value for the machined workpieces. A series of experiments have been performed on 1040 steel material of thicknesses 30, 60 and 80 mm, and on 2379 and 2738 steel materials of thicknesses 30 and 60 mm. The test specimens have been cut by using different cutting and offset parameter combinations of the “Sodick Mark XI A500 EDW” wire electrical discharge machine in the Middle East Technical University CAD/CAM/Robotics Center. The surface roughness of the testpieces has been measured by using a surface roughness measuring device. The related tables and charts have been prepared for 1040, 2379, 2738 steel materials. The tables and charts can be practically used for WEDM parameter selection for the desired workpiece surface roughness.     

Active integration of tool deflection effects in end milling. Part 1. Prediction of milled surfaces

Tools deflection that occurs during machining, and especially when flexible tools such as end mills are used, can result in dimensional errors on workpieces. The study presented here is part one of a two-part paper: it deals with the estimation of cutting forces and the prediction of milled surface. The second part will focus on a methodology that allows to optimize the production rate by compensating the deflection and meeting the part tolerance.Cutting force models have been and are still the subject of a lot of research. The model used is based on Kline and Devor's [5]: a polynomial approximation whose coefficients are obtained by least square methodology is used for the calculation of cutting forces. The machined surface (two axis machining) is determined using the contact point methodology and some experimental tests are done to validate the models.     

Influence of radial and axial runouts on surface roughness in face milling with round insert cutting tools

In face milling processes, the surface quality of the machined part depends on many factors, including feed, cutting tool geometry and tool errors. In this work, a numerical model for predicting the surface profile and surface roughness as a function of these factors is presented, incorporating a random values generation algorithm that makes it possible to determine the variation in surface roughness from the values that can be adopted by tool errors. This work is focused on round insert cutting tools and the influence of tool errors such as radial and axial runouts. The results that correspond to a number of teeth equal to 4, insert diameter of 12 mm, depth of cut of 0.5 mm, cutting speed of 120 m/min and feed of 0.4–1.4 mm/rev are analysed. Milling experiments are made to verify the validity of the model and the discrepancies between the experimental and theoretical surface profiles are assumed to be a consequence of different factors such as the variation in undeformed chip thickness along the surface profile.     

Statistical analysis of surface roughness and cutting forces using response surface methodology in hard turning of AISI 52100 bearing steel with CBN tool

The present work concerns an experimental study of hard turning with CBN tool of AISI 52100 bearing steel, hardened at 64 HRC. The main objectives are firstly focused on delimiting the hard turning domain and investigating tool wear and forces behaviour evolution versus variations of workpiece hardness and cutting speed. Secondly, the relationship between cutting parameters (cutting speed, feed rate and depth of cut) and machining output variables (surface roughness, cutting forces) through the response surface methodology (RSM) are analysed and modeled. The combined effects of the cutting parameters on machining output variables are 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 machining parameters with respect to objectives (surface roughness and cutting force values). Results show how much surface roughness is mainly influenced by feed rate and cutting speed. Also, it is underlined that the thrust force is the highest of cutting force components, and it is highly sensitive to workpiece hardness, negative rake angle and tool wear evolution. Finally, the depth of cut exhibits maximum influence on cutting forces as compared to the feed rate and cutting speed.     

Roughness and texture generation on end milled surfaces

Plane surface generation mechanism in flat end milling is studied in this research. The bottom of a flat end mill has an end cutting edge angle that plays an important role in surface texture. Surface texture is produced by superposition of conical surfaces generated by the end cutting edge rotation. The machined surface is cut once again by the trailing cutting edge. This back cutting phenomenon is frequently observed on surfaces after finishing. Tool run-out and tool setting error including tool tilting and eccentricity between tool center and spindle rotation center are considered together with tool deflection caused by cutting forces. Tool deflection affects magnitude of back cutting and the surface form accuracy. As a result, the finished surface possesses peaks and valleys with form waviness. Surface topography parameters such as RMS deviation, skewness and kurtosis are used for evaluating the generated surface texture characteristics. Through a set of cutting tests, it is confirmed that the presented model predicts the surface texture and roughness parameters precisely including back cutting effect.     

An in-process surface recognition system based on neural networks in end milling cutting operations

An in-process based surface recognition system to predict the surface roughness of machined parts in the end milling process was developed in this research to assure product quality and increase production rate by predicting the surface finish parameters in real time. In this system, an accelerometer and a proximity sensor are employed as in-process surface recognition sensors during cutting to collect the vibration and rotation data, respectively. Using spindle speed, feed rate, depth of cut, and the vibration average per revolution (VAPR) as four input neurons, an artificial neural networks (ANN) model based on backpropagation was developed to predict the output neuron-surface roughness R_a values. The experimental results show that the proposed ANN surface recognition model has a high accuracy rate (96–99%) for predicting surface roughness under a variety of combinations of cutting conditions. This system is also economical, efficient, and able to be implemented to achieve the goal of in-process surface recognition by retrieving the weightings (which were generated from training and testing by the artificial neural networks), predicting the surface roughness R_a values while the part is being machined, and giving feedback to the operators when the necessary action has to be taken.     

Optimization of feedrate in a face milling operation using a surface roughness model

Optimization of feedrate is valuable in terms of providing high precision and efficient machining. The surface roughness is particularly sensitive to the feedrate and the runout errors of the inserts in a face-milling operation. This paper analyzes the effects of the insert runout errors and the variation of the feedrate on the surface roughness and the dimensional accuracy in a face-milling operation using a surface roughness model. The validity of the developed model was proved through cutting experiments, and the model was used to predict the machined surface roughness from the information of the insert runouts and the cutting parameters. From the estimated surface roughness value, the optimal feedrate that gave a maximum material removal rate under the given surface roughness constraint could be selected by a bisection method.     

A study of back cutting surface finish from tool errors and machine tool deviations during face milling

The surface finish of mechanical components produced by face milling is given by factors such as cutting conditions, workpiece material, cutting geometry, tool errors and machine tool deviations. The contribution of the different tool teeth to imperfections in the machined surface is strongly influenced by tool errors such as radial and axial runouts. The surface profile of milled parts is not only affected by chip removal due to front cutting, but also by back cutting, which must be taken into account when predicting surface roughness. In the present work, the influence of back cutting on the surface finish obtained by face milling operations is studied. Final part surface roughness is modelled from tool runouts and height deviations that affect the surface marks provoked by back cutting. Round insert cutting tools and surface positions defined by cutter axis trajectory are considered, and milling experiments are developed for a spindle speed of 750 rpm, depth of cut of 0.5 mm and feeds from 0.4 to 1.0 mm/rev. Experimental observations are compared with the theoretical predictions provided by the surface roughness model, and good agreement is found between both results. Surface imperfections caused by front and back cutting are analysed, and discrepancies between experiments and numerical predictions are explained by undeformed chip thickness variations along the tool tooth cutting edge, the tearing of the workpiece material, and fluctuations in the feedrate and height deviation during machine tool axis displacement.     

Experimental evaluation on the effect of minimal quantities of lubricant in milling

Implementation of stricter Environmental Protection Agency (EPA) regulations associated with the use of ample amount of flood coolant has led to this study on minimal quantities of lubrication (MQL) technique on milling of ASSAB 718 HH steel at 35 HRc with uncoated carbide inserts while the MQL amount and flood coolant flow rate were 8.5 ml h⁻¹ and 42,000 ml min⁻¹, respectively. Unlike fracture in flood cooling or flaking in dry cutting the MQL used aided inserts were still in serviceable condition despite the presence of higher width of flank wear. Analyses of the cutting force, surface roughness, chip shape and EDX findings reveal that MQL may be considered as an economical and environmentally compatible lubrication technique for low speed, feed rate and depth of cut.     

基于神经网络的表面粗糙度智能预测系统

表面粗糙度趋势分析及预测技术是计算机集成制造系统故障诊断技术发展的迫切需要。本文在讨论神经网络非线性、多因素预测原理及其拓扑结构的基础上,基于神经网络方法设计了智能型的工件表面粗糙度监测预测系统,将非线性预测和多因素预测引入表面粗糙度预测模型中,即在进行工件表面租糙度预测时兼顾了刀具磨损,从而使本系统拥有可靠和高精度的预测效果。     

The estimation of cutting forces and specific force coefficients during finishing ball end milling of inclined surfaces

The majority of cutting force models applied for the ball end milling process includes only the influence of cutting parameters (e.g. feedrate, depth of cut, cutting speed) and estimates forces on the basis of coefficients calibrated during slot milling. Furthermore, the radial run out phenomenon is predominantly not considered in these models. However this approach can induce excessive force estimation errors, especially during finishing ball end milling of sculptured surfaces. In addition, most of cutting force models is formulated for the ball end milling process with axial depths of cut exceeding 0.5 mm and thus, they are not oriented directly to the finishing processes. Therefore, this paper proposes an accurate cutting force model applied for the finishing ball end milling, which includes also the influence of surface inclination and cutter's run out. As part of this work the new method of specific force coefficients calibration has been also developed. This approach is based on the calibration during ball end milling with various surface inclinations and the application of instantaneous force signals as an input data. Furthermore, the analysis of specific force coefficients in function of feed per tooth, cutting speed and surface inclination angle was also presented. In order to determine geometrical elements of cut precisely, the radial run out was considered in equations applied for the calculation of sectional area of cut and active length of cutting edge. Research revealed that cutter's run out and surface inclination angle have significant influence on the cutting forces, both in the quantitative and qualitative aspect. The formulated model enables cutting force estimation in the wide range of cutting parameters, assuring relative error's values below 16%. Furthermore, the consideration of cutter's radial run out phenomenon in the developed model enables the reduction of model's relative error by the 7% in relation to the model excluding radial run out.     

Impact of the cutting dynamics of small radial immersion milling operations on machined surface roughness

This paper deals with a numerical and experimental study of the dynamics of flank milling operations at low cutting rates. It focuses on both properties of the cutting vibratory phenomena and their impacts on the roughness of the machined surface. The study is based on a one degree of freedom model of the mechanical machining system. The system is of the rigid cutter–flexible workpiece type. The cutting force model is based on the regenerative mechanism. The roughness of the surface machined at high speed revolutions has been studied for both forced vibrations occurring during stable cutting and self-excited vibrations occurring during unstable cutting. It is shown that forced vibrations have only a very slight impact (roughness remains quite similar to that obtained with a fully rigid mechanical system), while unstable cutting mainly impacts roughness. The stable milling zones can be shown on a roughness map. The study of the roughness shows that the boundary between stable and unstable cutting conditions, in the case of interrupted cutting, is a wide zone characterised by a doubling of the tooth passing period. In this zone, only one tooth over two is removing material due to the vibratory motion. A discussion explains the phenomenon.     

Predictive modeling of surface roughness in grinding

The surface roughness is a variable often used to describe the quality of ground surfaces as well as to evaluate the competitiveness of the overall grinding system. This paper presents the prediction of the arithmetic mean surface roughness based on a probabilistic undeformed chip thickness model. The model expresses the ground finish as a function of the wheel microstructure, the process kinematic conditions, and the material properties. The analysis includes a geometrical analysis of the grooves left on the surface by ideal conic grains. The material properties and the wheel microstructure are considered in the surface roughness prediction through the chip thickness model. A simple expression that relates the surface roughness with the chip thickness was found, which was verified using experimental data from cylindrical grinding.     

基于高质量和低振动的铣削参数优化方法研究

针对数控铣床在切削过程中产生的振动对工件表面质量的影响,提出以低振动和高表面质量为优化目标,对切削参数进行优化。以VDF-850A铣床为研究对象对45号钢进行铣削正交试验,通过建立振动采集系统,采集振动信号提取振动特征值并测量工件表面粗糙度值,应用最小二乘法拟合数据建立了振动和粗糙度数学模型。利用层次分析法确定两目标函数权重,使用平方和加权法对两目标函数加权拟合成综合目标评价函数,运用粒子群算法优化切削参数。试验结果表明:应用粒子群算法优化后的切削参数进行加工可有效的降低振动和提高表面质量。     

An experimental study of tool wear and cutting force variation in the end milling of Inconel 718 with coated carbide inserts

Inconel 718 is a difficult-to-cut nickel-based superalloy commonly used in aerospace industry. This paper presents an experimental study of the tool wear propagation and cutting force variations in the end milling of Inconel 718 with coated carbide inserts. The experimental results showed that significant flank wear was the predominant failure mode affecting the tool life. The tool flank wear propagation in the up milling operations was more rapid than that in the down milling operations. The cutting force variation along with the tool wear propagation was also analysed. While the thermal effects could be a significant cause for the peak force variation within a single cutting pass, the tool wear propagation was believed to be responsible for the gradual increase of the mean peak force in successive cutting passes.     

An investigation of workpiece temperature variation in end milling considering flank rubbing effect

Better prediction about the magnitude and distribution of workpiece temperatures has a great significance for improving performance of metal cutting process, especially in the aviation industry. A thermal model is presented to describe the cyclic temperature variation in the workpiece for end milling. Owing to rapid tool wear in the machining of aeronautical components, flank rubbing effect is considered. In the proposed heat source method for milling, both the cutting edge and time history of process are discretized into elements to tackle geometrical and kinematical complexities. Based on this concept, a technique to calculate the workpiece temperature in stable state, which supposes the tool makes reverse movement, is developed. And a practicable solution is provided by constructing a periodic temperature rise function series. This investigation indicates theoretically and experimentally the impact of different machining conditions, flank wear widths and cutter locations on the variation of workpiece temperature. The model results have been compared with the experimental data obtained by machining 300M steel under different flank wear widths and cutting conditions. The comparison indicates a good agreement both in trends and values. With the alternative method, an accurate simulation of workpiece temperature variation can be achieved and computational time of the algorithm is obviously shorter than that of finite element method. This work can be further employed to optimize cutting conditions for controlling the machined surface integrity.