Tool flank wear prediction in CNC turning of 7075 AL alloy SiC composite

Flank wear occurs on the relief face of the tool and the life of a tool used in a machining process depends upon the amount of flank wear; so predicting of flank wear is an important requirement for higher productivity and product quality. In the present work, the effects of feed, depth of cut and cutting speed on flank wear of tungsten carbide and polycrystalline diamond (PCD) inserts in CNC turning of 7075 AL alloy with 10 wt% SiC composite are studied; also artificial neural network (ANN) and co-active neuro fuzzy inference system (CANFIS) are used to predict the flank wear of tungsten carbide and PCD inserts. The feed, depth of cut and cutting speed are selected as the input variables and artificial neural network and co-active neuro fuzzy inference system model are designed with two output variables. The comparison between the results of the presented models shows that the artificial neural network with the average relative prediction error of 1.03% for flank wear values of tungsten carbide inserts and 1.7% for flank wear values of PCD inserts is more accurate and can be utilized effectively for the prediction of flank wear in CNC turning of 7075 AL alloy SiC composite. It is also found that the tungsten carbide insert flank wear can be predicted with less error than PCD flank wear insert using ANN. With Regard to the effect of the cutting parameters on the flank wear, it is found that the increase of the feed, depth of cut and cutting speed increases the flank wear. Also the feed and depth of cut are the most effective parameters on the flank wear and the cutting speed has lesser effect.     

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.     

Assessment of some factors influencing tool wear on the machining of glass fibre-reinforced plastics by coated cemented carbide tools

Glass fibre-reinforced plastics (GFRP) composite materials are used in many different engineering fields. The need for machining of GFRP composites has not been eliminated fully. The tool wear reduction is an important aspect during machining. In the present work, an attempt has been made to assess the factors influencing tool wear on the machining of GFRP composites. Experimental design concept has been used for experimentation. The machining experiments are carried out on lathe using two levels of factors. The factors considered are cutting speed, fibre orientation angle, depth of cut and feed rate. A procedure has been developed to assess and optimize the chosen factors to attain minimum tool wear by incorporating (i) response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that cutting speed is a factor, which has greater influence on tool flank wear, followed by feed rate. Also the determined optimal conditions really reduce the tool flank wear on the machining of GFRP composites within the ranges of parameters studied.     

金属陶瓷刀具切削铸铁的磨损机理研究

利用真空烧结工艺制备了纳米复合Ti(C,N)基金属陶瓷刀片。进行铸铁的单因素切削试验,并利用SEM、EPMA对金属陶瓷刀具的磨损失效机理进行了详细的研究。结果表明:纳米复合Ti(C,N)基金属陶瓷刀片只适宜小切削用量下铸铁的切削加工,切削铸铁时主要以磨损的形式失效,其主要的磨损失效机理是冲击磨损和崩碎切屑的研磨。     

Predicting tool flank wear using spindle speed change

A unique technique is developed for on-line prediction of the tool flank wear in turning using the spindle speed change. A mathematical model is presented for the approach. The speed sensing element is an optical encoder mounted on the spindle shaft and interfaced to an IBM compatible microcomputer employing custom designed electronics. The changes in spindle speed are compensated for lathe transmission ratio, electrical configuration of the the lathe's motor, and the torque speed relationship of the machine. Using the surface response methodology (which reduces the number of cutting sessions required for accurate results), a series of cutting tests are performed with various combinations of cutting speed, feedrate, depth of cut and material hardness. Predictions from the model correlate well with actual flank wear measurements.     

Cutting performance of diamond tools during ultra-precision turning of electroless-nickel plated die materials

This study is an attempt (a) to observe the wear characteristic of diamond tool with 200 km cutting distance and to study the effects of wear on the surface roughness and cutting forces and (b) to optimize various cutting parameters such as depth of cut, feed rate, spindle speed and phosphorus content. The experimental results showed that tool wear was not so significant although some defects on rake face were observed after cutting 15.6 km. Further cutting showed that the surface roughness increases with cutting distance, and that the cutting forces were larger than thrust force at the beginning of cutting, but after cutting 130 km, thrust force became larger and increased rapidly. It was also observed that forces increase with the increase of depth of cut, spindle speed and feed rate, and decrease with the increase of phosphorus content of the plating. Depth of cut has no significant effect on surface roughness, while it increases with increase of feed rate and decreases with the increase of percentage of phosphorus content in the workpieces. In case of spindle speed, surface roughness decreases with the increase of spindle speed up to a certain value and then starts to increase with the increase of spindle speed.     

Role of temperature and surface finish in predicting tool wear using neural network and design of experiments

The present work focuses on the two of the techniques, namely design of experiments and the neural network for predicting tool wear. In the present work, flank wear, surface finish and cutting zone temperature were taken as response (output) variables measured during turning and cutting speed, feed and depth of cut were taken as input parameters. Predictions for all the three response variables were obtained with the help of empirical relation between different responses and input variables using design of experiments (DOE) and also through neural network (NN) program. Predicted values of the responses by both techniques, i.e. DOE and NN were compared with the experimental values and their closeness with the experimental values was determined. Relationship between the surface roughness and the flank wear and also between the temperature and the flank wear were found out for indirect measurement of the flank wear through surface roughness and cutting zone temperature.     

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.     

Tool wear measurement in turning using force ratio

The aim of this work was to develop a reliable method to predict flank wear during the turning process. The present work developed a mathematical model for on-line monitoring of tool wear in a turning process. Force signals are highly sensitive carriers of information about the machining process and, hence, they are the best alternatives for monitoring tool wear. In the present work, determination of tool wear has been achieved by using force signals. The relationship between flank wear and the ratio of force components was established on the basis of data obtained from a series of experiments. Measurement of the ratio between the feed force and the cutting force components (F_f/F_c) has been found to provide a practical method for an in-process approach to the quantification of tool wear. A series of experiments was conducted to study the effects of tool wear as well as other cutting parameters on the cutting force signals, and to establish a relationship between the force signals, tool wear and other cutting parameters. The flank wear and the ratio of forces at different working conditions were collected experimentally to develop a mathematical model for predicting flank wear. The model was verified by comparing the experimental values with the predicted values. The relationship was then used for determination of tool flank wear.     

基于响应曲面法的钛合金干式车削工艺参数优化

为了提高钛合金干式车削加工质量,采用响应曲面法对主要车削工艺参数进行了优化,以工件表面粗糙度Ra和刀具磨损量VC作为评价指标,设计了切削速度、背吃刀量和进给量三因素的Box-Behnken实验模型。利用方差和拟合残差概率分布分析三因素的显著性及交互作用,并结合实验检验所建表面粗糙度和刀具磨损二阶响应预测模型的有效性。响应曲面法优化后的最佳工艺参数为:切削速度20 m/min、背吃刀量0.1788 mm、进给量0.1 mm/r,此时得到的表面粗糙度和刀具磨损量为1.031μm和155.6μm,与预测值的误差分别为:9.93%和1.58%。结果表明:基于响应曲面法的钛合金干式车削表面粗糙度和刀具磨损量预测模型准确有效。     

Surface Integrity in Turning of Annealed Brass: Hardness Prediction

The purpose of this study was to obtain a comprehensive understanding of the effects of cutting parameters (depth of cut, feed rate, and cutting speed) on the surface integrity of, in terms of superficial hardening, annealed brass during a turning process. The results indicate that no significant phase transformations occurred for any of the turning conditions evaluated; however, microstructural changes were observed, as well as changes in the superficial hardness were measured. It was found that when the studied cutting parameters increase, the superficial hardness increases, with the cutting speed having less influence (2.56%), and feed rate having the greatest effect (22.67%). Finally, a mathematical expression is proposed, which relates the cutting parameters to the maximum hardness obtained for a given cutting condition.     

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.     

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.     

Prediction of surface roughness profiles for milled surfaces using an artificial neural network and fractal geometry approach

Artificial neural networks (ANNs) models were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the “ANNs” models are the cutting parameters: rotational speed, feed, depth of cut, pre-tool flank wear and vibration level. The output parameters of the model are the corresponding calculated fractal parameters: fractal dimension “D” and vertical scaling parameter “G”. The model consists of three-layered feed-forward back-propagation neural network. ANNs models were utilized successfully for modeling and predicting the fractal parameters “D” and “G” in face milling operations. Moreover, W–M fractal function was integrated with the developed ANNs models in order to generate an artificially fractal predicted profiles at different cutting conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens.     

Experimental and numerical studies on the performance of alloyed carbide tool in dry milling of aerospace material

Titanium alloy is widely used in the aerospace industry for applications requiring high strength at elevated temperature and high mechanical resistance. The difficulty of dislocation motion through the microstructure is responsible for its high yield strength. However, the main problems encountered when machining titanium alloy are the low material removal rate and the short tool life.This study investigated the suitability of uncoated cemented carbide tools in ball-end milling of the aerospace titanium alloy Ti-6242S. The experiments were carried out under dry cutting condition. Cutting speeds in the range of 60–150 m/min were considered. The axial and radial depths of cut were kept constant at 2.0 and 8.8 mm, respectively, and the feed rate values of 0.1 and 0.15 mm/tooth were selected. SEM analysis has been carried out on the worn tools and shows that flank wear and excessive chipping on the flank edge are the main tool failure modes. For both feed rates, the results demonstrate that the higher the cutting speed the better is the surface finish. The FEM simulation provides good results on modelling of chip formation and can be helpful to calculate the contact parameters and to understand the tool wear mechanisms when dry machining aerospace titanium alloys.     

二维超声加工淬硬钢的建模与参数优化

利用正交设计方法研究了硬质合金刀具二维超声加工(UEVC)淬硬钢Cr12Mo V时切削用量的三个因素对加工表面粗糙度和切削力的影响,并利用信噪比、方差及贡献率等方法对各因素间的相互作用进行了分析。以切削参数为独立变量,以切削力和表面粗糙度为响应,利用回归分析建立数学模型。实验结果表明:进给量是对表面粗糙度(Ra、Rz)影响最大的因数,贡献率分别为91.8%和88.8%;其次是切削深度,贡献率分别为3.72%和9.77%;对切削力(Fz)影响最大的二个因素是进给量和切削深度,贡献率分别为56.69%和38.46%;切削速度对表面粗糙度、切削力的贡献率均最小。此外,建立的回归方程对Ra、Rz和Fz均有很高的可决系数,分别为91.8%、94.3%和88.2%,说明所建线性回归模型的准确性。     

Wear behavior of Al2O3/Ti(C,N)/SiC new ceramic tool material when machining tool steel and cast iron

Design, fabrication and application of ceramic cutting tools are one of the important research topics in the field of metal cutting and advanced ceramic materials. In the present study, wear resistance of an advanced Al₂O₃/Ti(C,N)/SiC multiphase composite ceramic tool material have been studied when dry machining hardened tool steel and cast iron under different cutting conditions. Microstructures of the worn materials were observed with scanning electronic microscope to help analyze wear mechanisms. It is shown that when machining hardened tool steel at low speed wear mode of the kind of ceramic tool material is mainly flank wear with slight crater wear. The adhesion between tool and work piece is relatively weak. With the increase of cutting speed, cutting temperature increases consequently. As a result, the adhesion is intensified both in the crater area and flank face. The ceramic tool material has good wear resistance when machining grey cast iron with uniform flank wear. Wear mechanism is mainly abrasive wear at low cutting speed, while adhesion is intensified in the wear area at high cutting speed. Wear modes are dominantly rake face wear and flank wear in this case.     

Some experimental investigations in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates

In this paper, a concept of delamination factor F_d (i.e. the ratio of the maximum diameter D_max in the damage zone to the hole diameter D) is proposed to analyze and compare easily the delamination degree in the drilling of carbon fiber-reinforced plastic (CFRP) composite laminates. Experiments were performed to investigate the variations of cutting forces with or without onset of delamination during the drilling operations. The effects of tool geometry and drilling parameters on cutting force variations in CFRP composite materials drilling were also experimentally examined. The experimental results show that the delamination-free drilling processes may be obtained by the proper selections of tool geometry and drilling parameters. The effects of drilling parameters and tool wear on delamination factor are also presented and discussed.Cutting temperature has long been recognized as an important factor influencing the tool wear rate and tool life. An experimental investigation of flank surface temperatures is also presented in this paper. Experimental results indicated that the flank surface temperatures increase with increasing cutting speed but decreasing feed rate. Optimal cutting conditions are proposed to avoid damage from burning during the drilling processes.     

滑动齿套拨叉槽的立方氮化硼刀具切削工艺试验研究

为了确定硬态切削代替磨削加工滑动齿套拨叉槽时各参数对其表面质量的影响,采用立方氮化硼刀具对20CrMnTi棒料进行切削,利用正交试验法对加工表面粗糙度进行了直观分析和方差分析,得出切削速度、进给量、背吃刀量对拨叉槽表面粗糙度的影响规律,并给出拨叉槽加工时合理的切削用量;同时也对加工过程中刀具的磨损进行分析,为滑动齿套拨叉槽的立方氮化硼切削工艺参数的选取提供了依据。