Hard turning of an alloy steel on a machine tool with a polymer concrete bed

Finish turning of 39NiCrMo3 alloy steel in the hardened state has been widely investigated under dry, minimum quantity of lubricant and wet cutting conditions, using inserts in ceramic and PCBN materials, on turning centers equipped with polymer concrete and cast iron beds. The progress of workpart surface roughness and tool wear with cutting time has been measured and the results analysed and discussed in detail. It has been observed that dry cutting leads to the lowest values of tool wear and surface roughness, whilst the minimal quantity of lubricant technique does not provide advantages regarding to dry turning. Furthermore, the PCBN inserts are characterised by a longer tool life than the one exhibited by the ceramic inserts. Finally, the outstanding damping and high rigidity of the polymer concrete bed has a beneficial effect on both tool wear and workpiece surface finish.     

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.     

Influence of grinding parameters on the quality of high content PCBN cutting inserts

Plunge-face grinding is commonly used to finish PCBN cutting inserts. In order to reach an adequate process design, an investigation of the influence of the grinding parameters on the quality of high content PCBN inserts is carried out in this work. For this, the inserts are ground with different grinding wheels (including a variation of grain size and bonding), dressing feed rates, feed and cutting speeds and the edge chipping and flank face roughness are measured. It was found that a reduction of the abrasive grain size as well as an increase of the dressing feed rate lead to an improvement of the insert edge and surface quality. Moreover, a variation of the cutting and feed speeds has only a small influence on the PCBN insert quality.     

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.     

Cutting performance of PVD-coated carbide and CBN tools in hardmilling

In this study, cutting performance of CBN tools and PVD-coated carbide tools in end-milling of hardened steel was investigated. In high-speed dry hardmilling, two types of CBN tools were applied: the CBN-rich type and an ordinary one. In the case of relatively low-speed milling, on the other hand, a few coated carbide tools were selected where four kinds of coating films, TiN, TiCN, TiAlN and multi-layered TiAlN/AlCrN, were deposited on the K10 and P30 grade carbide. The cutting performance was mainly evaluated by tool wear, cutting temperature, cutting force and surface roughness. In dry cutting of hardened carbon steel with the ordinary CBN tool, the cutting tool temperature rose rapidly with increase in cutting speed; and tool temperature reached approximately 850 °C at the cutting speed of 600 m/min. In the case of the CBN-rich tool, the cutting temperature decreased by 50 °C or more because of its high thermal conductivity. It is remarkable that tool wear or damage on a cutting tool was not observed even when the cutting length was 156 m in both CBN tools. In the case of coated carbide tools, the temperatures of TiN-, TiCN- and TiAlN-coated carbide tools rose as cutting proceeded because of the progress of tool wear, but that of TiAlN/AlCrN-coated carbide tool hardly rose due to little tool wear. When the base material was K10 grade carbide, tool temperature was lower than that of P30 with any coating. The tool flank wear depends considerably on hardness and oxidizing temperature of the coating film.     

Effect of austempering temperature on machinability of Cu–Ni–Mo alloyed austempered ductile iron for heavy section parts

Abstract

The machinability of an austempered ductile iron with a suitable chemical composition for heavy sections has been assessed. Austempering heat treatment was carried out at three temperatures, 300, 340 and 375°C, after austenitising at 870°C for 100?min. Drilling tests, tool wear and surface roughness measurements were used to evaluate the machinability. Drilling operation failure, severe tool wear and the poorer surface roughness of the specimens austempered at lower temperatures indicated that austempering at higher temperatures clearly resulted in better machinability. The machinability of testpieces austempered at 375°C, which contained higher fractions of retained austenite, was superior to that of testpieces autenitised at lower temperatures, indicating that hardness is an important factor in assessing machinability in addition to high carbon austenite content.     

Chip formation, cutting forces, and tool wear in turning of Zr-based bulk metallic glass

The chip light emission and morphology, cutting forces, surface roughness, and tool wear in turning of Zr-based bulk metallic glass (BMG) material are investigated. Machining results are compared with those of aluminum 6061-T6 and AISI 304 stainless steel under the same cutting conditions. This study demonstrates that the high cutting speeds and tools with low thermal conductivity and rake angle activate the light emission and chip oxidation in BMG machining. For the BMG chip without light emission, serrated chip formation with adiabatic shear band and void formation is observed. The cutting force analysis further correlates the chip oxidation and specific cutting energy and shows the significant reduction of cutting forces for machining BMG at high cutting speeds. The machined surface of BMG has better surface roughness than that of the other two work materials. Some tool wear features, including the welding of chip to the tool tip and chipping of the polycrystalline cubic boron nitride (PCBN) tool edge, are reported for turning of BMG. This study concludes that BMG can be machined with good surface roughness using conventional cutting tools.     

A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool

The present work compares the performance of a mixed oxide ceramic tool in dry and air-cooled turning of grey cast iron. First, the study was done in the range of process parameters where dry turning provided satisfactory performance. The contours of surface roughness and tool life were generated with the help of trained neural networks. A novel procedure of neural network training is used in this work. The study was extended to the range in which dry turning performed poorly in terms of tool life. Tool wear, surface roughness of the machined job and forces and vibration during the cutting were studied. It was observed that air-cooling significantly reduces the tool wear at high cutting speed. At higher cutting speeds, where the dry turning performs very poorly, the air-cooled turning provides an improved surface finish also apart from the reduction in tool wear. In all the cases, the cutting and feed forces get reduced in air-cooling. Thus, air-cooled turning of grey cast iron with mixed oxide ceramic tools offers a promising environment-friendly option.     

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。     

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.     

The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 17-4PH stainless steel

The purpose of this paper is to develop a predictive model for the prediction of tool flank wear and an optimization model for the determination of optimum cutting conditions in machining 17-4PH stainless steel. The back-propagation neural network (BPN) was used to construct the predictive model. The genetic algorithm (GA) was used in the optimization model. The Taguchi method (TM) was used to find the optimum parameters for both models, respectively. Two steps of experiments have been carried out by machining 6 mm length and 90 mm length of the workpiece, respectively. The experimental scheme was arranged by using an orthogonal array of TM. It has been shown that the predictive model is capable of predicting the tool flank wear in an agreement behavior. The optimization model has also been proved that it is a convenient and efficient method to find the optimum cutting conditions associated with the maximum metal removal rate (MMRR) under different constraints. The constraint is the tool flank wear that can be determined from the predictive model. Furthermore, the systematic procedure to develop the models in this paper can be applied to the usage of the predictive or optimized problems in metal cutting.     

Properties and performances of innovative coated tools for turning inconel

Three innovative nanostructured coatings have been developed to be applied on cutting tools for continuous cutting of nickel-based super-alloys, in Minimum Quantity Lubrication (MQL) or dry conditions.The coatings, TiN+AlTiN, TiN+AlTiN+MoS₂ and CrN+CrN:C+C, were applied by PVD techniques on WC-Co inserts, developing nanostructured layers, characterised by superior performances, as confirmed both by laboratory tests and machining experiments.Coatings surface qualification included SEM observations with EDS analysis, ball erosion test, nanoindentation and scratch tests, classic tribological evaluation by ball-on-disc set-up, surface texture analysis.Results were analysed in light of the outcome of machining experiments performed mainly in dry and MQL turning of Inconel 718. Ball-on-disc and scratch tests, as well as machining experiments, agreed in classifying the coatings in the following decreasing performance order: TiN+AlTiN+MoS₂, followed by TiN+AlTiN, and by CrN+CrN:C+C.     

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.     

Refrigerated cooling air cutting of difficult-to-cut materials

One approach to enhance machining performance is to apply cutting fluids during cutting process. However, the use of cutting fluids in machining process has caused some problems such as high cost, pollution, and hazards to operator's health. All the problems related to the use of cutting fluids have urged researchers to search for some alternatives to minimize or even avoid the use of cutting fluids in machining operations. Cooling gas cutting is one of these alternatives. This paper investigates the effect of cooling air cutting on tool wear, surface finish and chip shape in finish turning of Inconel 718 nickel-base super alloy and high-speed milling of AISI D2 cold work tool steel. Comparative experiments were conducted under different cooling/lubrication conditions, i.e. dry cutting, minimal quantity lubrication (MQL), cooling air, and cooling air and minimal quantity lubrication (CAMQL). For this research, composite refrigeration method was adopted to develop a new cooling gas equipment which was used to lower the temperature of compressed gas. The significant experimental results were: (i) application of cooing air and CAMQL resulted in drastic reduction in tool wear and surface roughness, and significant improvement in chip shape in finish turning of Inconel 718, (ii) in the high-speed milling of AISI D2, cooling air cutting presented longer tool life and slightly higher surface roughness than dry cutting and MQL. Therefore, it appears that cooling air cutting can provide not only environment friendliness but also great improvement in machinability of difficult-to-cut materials.