Multiperformance Optimization in Turning of Free-Machining Steel Using Taguchi Method and Utility Concept

This article presents the application of Taguchi method and the utility concept for optimizing the machining parameters in turning of free-machining steel using a cemented carbide tool. A set of optimal process parameters, such as feed rate, cutting speed, and depth of cut on two multiple performance characteristics, namely, surface roughness and metal removal rate (MRR) is developed. The experiments were planned as per L ₉ orthogonal array. The optimal level of the process parameters was determined through the analysis of means (ANOM). The relative importance among the process parameters was identified through the analysis of variance (ANOVA). The ANOVA results indicated that the most significant process parameter is cutting speed followed by depth of cut that affect the optimization of multiple performance characteristics. The confirmation tests with optimal levels of machining parameters were carried out to illustrate the effectiveness of Taguchi optimization method. The optimization results revealed that a combination of higher levels of cutting speed and depth of cut along with feed rate in the medium level is essential in order to simultaneously minimize the surface roughness and to maximize the MRR.     

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.     

Predictive machinability models for a selected hard material in turning operations

In this paper, empirical models for tool life, surface roughness and cutting force are developed for turning operations. Process parameters (cutting speed, feed rate, depth of cut and tool nose radius) are used as inputs to the developed machinability models. Two important data mining techniques are used; they are response surface methodology and neural networks. Data of 28 experiments when turning austenitic AISI 302 have been used to generate, compare and evaluate the proposed models of tool life, cutting force and surface roughness for the considered material.     

基于人工智能算法的最优加工表面粗糙度预测研究

以切削速度、进给量、切削深度、刀尖圆弧半径为设计变量,采用正交试验法进行了立方氮化硼(CBN)刀具干式车削冷作模具钢Cr12MoV的试验研究。利用神经网络的非线性拟合能力和遗传算法的全局寻优能力,建立了加工表面粗糙度预测模型并获得了使表面粗糙度达到最优的切削用量与刀尖圆弧半径组合。利用遗传算法获得的最优表面粗糙度值比田口方法和切削试验所获得的最佳表面粗糙度值分别降低了7.1%和17.2%。文中所采用的方法也为切削加工中刀具磨损、切削力和残余应力等问题的建模与参数优化提供理论参考。     

Experimental study on ultrasonic elliptical vibration cutting of hardened steel using PCD tools

Diamond tools cannot usually be applied for machining hardened steels while applying conventional cutting technique. As an alternative, ultrasonic elliptical vibration cutting (UEVC) technique was successfully applied for obtaining mirror surface on such steels using single crystal diamond (SCD) tools. In order to reduce production cost without compromising mirror surface quality, polycrystalline diamond (PCD) tools may be tested against highly expensive SCD tools. However, study on machining of hardened steel using PCD tools applying the UEVC technique has not yet been reported. The current research presents an experimental study on UEVC of hardened stainless steel (a typical Stavax, hardness 49 HRC) using the PCD tools. Face turning experiments were carried out to investigate the effects of three machining parameters: nominal depth of cut, feed rate, and nominal cutting speed on output performances such as cutting force, tool flank wear, surface roughness, and chip formation. Experimental results show that nominal cutting speed has very strong influence on the output performances, compared to the other two parameters. The surface roughness improves with a decrease in cutting speed. A mirror-like surface of approximately 804 mm² with a roughness value R_a of 11 nm was achieved at a lower cutting speed. Theoretical explanations have been given to support the results drawn from the UEVC experiments. It can be concluded that, while applying the UEVC technique, the inexpensive PCD tools instead of the SCD tools can be effectively applied to obtain optical surface for producing precise molds from the hardened steel.     

Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network

An artificial neural network (ANN) model was developed for the analysis and prediction of the relationship between cutting and process parameters during high-speed turning of nickel-based, Inconel 718, alloy. The input parameters of the ANN model are the cutting parameters: speed, feed rate, depth of cut, cutting time, and coolant pressure. The output parameters of the model are seven process parameters measured during the machining trials, namely tangential force (cutting force, F_z), axial force (feed force, F_x), spindle motor power consumption, machined surface roughness, average flank wear (VB), maximum flank wear (VB_max) and nose wear (VC). The model consists of a three-layered feedforward backpropagation neural network. The network is trained with pairs of inputs/outputs datasets generated when machining Inconel 718 alloy with triple (TiCN/Al₂O₃/TiN) PVD-coated carbide (K 10) inserts with ISO designation CNMG 120412. A very good performance of the neural network, in terms of agreement with experimental data, was achieved. The model can be used for the analysis and prediction of the complex relationship between cutting conditions and the process parameters in metal-cutting operations and for the optimisation of the cutting process for efficient and economic production.     

Cutting temperature of ceramic tools in high speed machining of difficult-to-cut materials

This paper deals with an experimental and analytical investigation into the different factors which influence the temperature distribution on Al₂O₃---TiC ceramic tool rake face during machining of difficult-to-cut materials, such as case hardened AISI 1552 steel (60–65 Rc) and nickel-based superalloys (e.g. Inconel 718). The temperature distribution was predicted first using the finite element analysis. Temperature measurements on the tool rake face using a thermocouple based technique were performed and the results were verified using the finite element analysis. Experiments were then performed to study the effect of cutting parameters, different tool geometries, tool conditions, and workpiece materials on the cutting edge temperatures. Results presented in this paper indicate that for turning case hardened steel, increasing the cutting speed, feted, and depth of cut will increase the cutting edge temperature. On the other hand, increasing the tool nose radius, and angle of approach reduces the cutting edge temperature, while increasing the width of the tool chamfer will slightly increase the cutting ege temperature. As for the negative rake angle, it was found that there is an optimum value of rake angle where the cutting edge temperature was minimum. For the Inconel 718 material, it was found that the cutting edge temperature reached a minimum at a speed of 510 m/min, and feed of 1.25 mm/rev. However, the effect of the depth of cut and tool nose radius was almost the same as that determined in the turning of case hardened steel. It was also observed in turning Inconel 718 with ceramic tools that, cutting forces and different types of tool wear were reduced with increasing the feed.     

Investigations on the surface roughness produced by ball burnishing

Burnishing, a plastic deformation process, can be used to finish surfaces. Experimental work was conducted on a vertical machining centre to establish the effects of various ball burnishing parameters: depth of penetration, feed, ball material, burnishing speed and lubricant, on the surface roughness of AISI 1045 specimens. The ball materials used were WC and SUJ2. It was found that all the parameters studied affect the surface finish to varying degrees. The surface roughness parameter R_tm first decreases and then increases with increasing depth of penetration. The effects of feed and burnishing forces on the surface finish also showed similar trends. The effect of speed depends on the type of lubricant used. Grease is a better lubricant than cutting oil for the speed range of 450 mm min⁻¹ to 1200 mm min⁻¹. With appropriate selection of the process parameters, a pre-machined surface roughness of about 4 μm can be finished to approximately 0.7 μm.     

Mathematical modeling of surface roughness for evaluating the effects of cutting parameters and coating material

The work presented in this paper examines the effects of cutting parameters (cutting speed, feed rate and depth of cut) onto the surface roughness through the mathematical model developed by using the data gathered from a series of turning experiments performed. An additional investigation was carried out in order to evaluate the influence of two well-known coating layers onto the surface roughness. For this purpose, the experiments were repeated for two CNMG 120408 (with an ISO designation) carbide inserts having completely the same geometry and substrate but different coating layers, in a manner that identical cutting conditions would be ensured. The workpiece material machined was cold-work tool steel AISI P20. Of the two types of inserts employed; Insert 1 possesses a coating consisting of a TiCN underlayer, an intermediate layer of Al₂O₃ and a TiN outlayer, all deposited by CVD; whilst Insert 2 is PVD coated with a thin TiAlN layer (3 ± 1 μm). The total average error of the model was determined to be 4.2% and 5.2% for Insert 1 and Insert 2, respectively; which proves the reliability of the equations established.     

58SiMn高强度钢车削表面完整性的试验研究

目的研究58Si Mn高强度钢表面完整性评价指标受切削参数影响的变化规律。方法分别设计单因素和正交试验,采用涂层硬质合金刀具对58Si Mn高强度钢进行车削加工试验,通过采集相关数据,分别讨论了切削深度、进给速度和切削速度变化对表面粗糙度、残余应力、显微硬度和表层微观组织变化等方面的影响。结果进给速度对表面粗糙度的影响最显著,切削速度次之,切削深度的变化对表面粗糙度无直接影响。已加工表面的残余应力随切削速度和进给量的增大而增大。显微硬度随切削深度的增大而减小,随进给量的增大而增大,层深上的显微硬度则呈现先减小后增大的趋势。表层微观组织受切削速度影响不大,未出现明显的相变和晶粒歪曲。结论降低进给速度是减小工件表面粗糙度最直接有效的方法,提高切削速度并不能使表面粗糙度明显减小。工件表面的轴向和切向残余应力均为拉应力,为提高零件使用性能,应采取相应的措施使之转化为压应力。     

一种不锈钢材料车削的表面粗糙度试验与研究

针对不锈钢材料切削加工的难题,采用回归正交表设计试验方案。采用回归方法对试验数据进行分析,建立表面粗糙度模型。利用自编程序迅速、准确地完成试验数据的选取、处理,并进行结果分析。结果表明：切削速度、进给量、车刀刀尖半径对表面粗糙度的影响明显,而切削深度对表面粗糙度的影响不明显;增大切削速度、车刀刀尖半径可降低表面粗糙度,增大进给量会使零件表面粗糙度变大。试验结果对不锈钢的加工有重要的参考价值     

Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R _a ) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip’s width, and chip’s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed.     

Effects of random aspects of cutting tool wear on surface roughness and tool life

The effects of random aspects of cutting tool flank wear on surface roughness and on tool lifetime, when turning the AISI 1045 carbon steel, were studied in this investigation. It was found that standard deviations corresponding to tool flank wear and to the surface roughness increase exponentially with cutting time. Under cutting conditions that correspond to finishing operations, no significant differences were found between the calculated values of the capability indexC _p at the steady-state region of the tool flank wear, using the best-fit method or the Box-Cox transformation, or by making the assumption that the surface roughness data are normally distributed. Hence, a method to establish cutting tool lifetime could be established that simultaneously respects the desired average of surface roughness and the required capability index.     

Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis

This paper presents the findings of an experimental investigation into the effects of cutting speed, feed rate, depth of cut, nose radius and cutting environment in CNC turning of AISI P-20 tool steel. Design of experiment techniques, i.e. response surface methodology (RSM) and Taguchi's technique, have been used to accomplish the objective of the experimental study. L₂₇ orthogonal array and face centered central composite design have been used for conducting the experiments. Taguchi's technique as well as 3D surface plots of RSM revealed that cryogenic environment is the most significant factor in minimizing power consumption followed by cutting speed and depth of cut. The effects of feed rate and nose radius were found to be insignificant compared to other factors. Though both the techniques predicted near similar results, RSM technique seems to have an edge over the Taguchi's technique.     

干式切削20Cr表面粗糙度和残余应力研究

针对低渗碳钢20Cr材料制作齿轮轴等零件表面质量要求,如表面粗糙度低于1.6μm,零件表面耐疲劳性能良好。试验采用干式切削20Cr钢材方式,在背吃刀量固定的工序中,研究切削速度和进给量对20Cr材料表面粗糙度的影响,同时结合有限元技术,分析切削速度和进给量对20Cr表面残余应力的影响。干式切削试验采用单因素方法,进行多组干式切削20Cr工件,对比分析各组工件表面粗糙度,结果表明当进给量较小时,切削速度对工件表面粗糙度有显著影响,表现为表面粗糙度随切削速度增加而变大;当切削速度一定时,进给量增加导致表面粗糙度变大,并且进给量对表面粗糙度的影响大于切削速度;对于工件表面残余应力,增加切削速度和进给量均导致残余应力变大,因而较小的切削速度和进给量可以降低工件表面残余应力,改善应力分布状态。     

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.     

微细铣削不锈钢310S表面完整性试验研究

目的揭示微细铣削下的切削深度ap、进给量f、切削速度v对不锈钢310S表面完整性的影响规律,为优化不锈钢310S的切削工艺提供参考。方法基于响应曲面方法,采用涂层硬质合金微直径铣刀,对不锈钢310S进行了铣削加工试验,对表面粗糙度、表面形貌和显微硬度的数据和信息进行采集并分析,进行多元非线性回归,建立了表面粗糙度Ra与切削参数之间的映射关系,对多元回归方程进行了显著性检验。结果得到切削参数ap、v、f显著度分别为0.099、0.620、0.011。基于曲面响应法的试验数据及数学模型,直观地绘制了ap、v、f对表面粗糙度Ra、表面形貌和显微硬度的影响规律图。结论在一定的切削加工参数范围内,进给量f对微细铣削不锈钢310S表面粗糙度Ra的影响最显著,其次是切削深度ap,切削速度v的影响最小。表面留有摆线状加工痕迹,顺铣侧的残留物分布多于逆铣侧。切削深度ap对310S试件表层显微硬度的影响最显著,其次是切削速度v。减小进给量f是降低不锈钢310S表面粗糙度的有效加工方法。     

Performance of single Si3N4 and mixed Si3N4+PCBN wiper cutting tools applied to high speed face milling of cast iron

In this work two face milling cutter systems were used in high speed cutting of gray cast iron under cutting condition encountered in the shop floor. The first system, called ‘A’, has 24 Si₃N₄ ceramic inserts all with square wiper edges. The second system, called ‘B’, is a mixed tool material system, having 24 wiper inserts, 20 of them are Si₃N₄ intercalated by four PCBN inserts. Cutting speed (v_c), depth of cut (doc) and feed rate per tooth (f_z) were kept constant. Surface roughness (R_a and R_t) and waviness (W_t), tool life (based on flank wear, VB_Bmax) and burr formation (length of the burr, h) were the parameters considered to compare the two systems. System ‘B’ presented better performance according to all parameters, although only end of life criterion based on R_t parameter has been reached.     

300 M超高强钢车削加工表面质量

目的研究切削参数对300M超高强度钢加工表面质量的影响。方法选用硬质合金刀具车削加工300M超高强度钢,研究切削参数对表面加工硬化、残余应力及表面粗糙度的影响。通过HXD-1000显微硬度检测仪、X-350A型X射线应力测试系统、TR240表面粗糙度测量仪对实验过程进行检测分析。通过单因素试验研究影响表面粗糙度的主次因素,并通过正交试验,以进给量f、切削速度v、刀尖圆弧半径rε、背吃刀量a_p为变量建立表面粗糙度的预测模型。结果背吃刀量a_p=0.2 mm,切削速度v为60~120 m/min,进给量f为0.1~0.25 mm/r时,300M钢经切削加工后,维氏硬度在467~550HV范围内变化。切削速度从60 m/min增大至200 m/min时,表面残余应力从压应力-59.13 MPa变为拉应力257.33 MPa,次表层残余应力的最大残余压应力从-147.46 MPa增大到-422.65 MPa,并且层深至50μm左右处,工件材料的加工变质层结束。结论表面硬度随着进给量和切削速度的增大而减小,并且越往里层,硬度越低,直至达到基体的硬度。影响表面粗糙度的最主要因素为进给量,其次是刀尖圆弧半径,再次为切削速度,背吃刀量对表面粗糙度的影响最小。建立的表面粗糙度预测模型通过了试验验证,具有很高的加工精度。     

数控车削45调质钢粗糙度影响因素研究

以加工表面粗糙度与切削用量的关系为研究对象,采用单因素试验方法,利用硬质合金刀具对45调质钢进行湿式车削试验,测量得到选定参数条件下的加工表面粗糙度值,对试验结果进行分析。结果表明:在试验采用的切削参数范围内,表面粗糙度随进给量的增加而近似成线性增加;背吃刀量从0.05 mm增加到0.10 mm时,表面粗糙度减小,从0.10mm到0.20 mm时,表面粗糙度增加;切削速度从500 r/min到1 000 r/min时,加工表面粗糙度呈减小趋势,从1 000r/min到1 400 r/min时出现略为增加的趋势;该研究对实际加工45调质钢具有一定的指导意义,也可为合理选择切削用量提供理论参考。