Effects of cutting conditions on microhardness and microstructure in high-speed milling of H13 tool steel

In the present study, high-speed side milling experiments of H13 tool steel with coated carbide inserts were conducted under different cutting parameters. The microhardness and microstructure changes of the machined surface and subsurface were investigated. A finite element model, taking into account the actual milling process, was established based on the commercial FE package ABAQUS/Explicit. Instantaneous temperature distributions beneath the machined surface were analyzed under different cutting speeds and feed per tooth based on the model. It was found that the microhardness on the machined surface is much higher than that in the subsurface, which indicates that the surface materials experienced severe strain hardening induced by plastic deformation during the milling process. Furthermore, the hardness of machined surface decreases with the increase of cutting speed and feed per tooth due to thermal softening effects. In addition, optical and scanning electron microscope (SEM) was used to characterize the microstructures of cross sections. Elongated grains due to material plastic deformation can be observed in the subsurface, and white and dark layers are not obvious under present milling conditions. The thickness of plastic deformation layer beneath the machined surface increases from 3 to 10 μm with the increase of cutting speed and feed per tooth. The corresponding results were found to be consistent and in good agreement with the depth of heat-affected zone in finite element analysis (FEA).     

Surface quality in milling of hardened H13 steel

In the final milling process of free-form surfaces, commonly employed in the production of molds and dies, knowledge of the cutting conditions and a strategy for choosing adequate processing routes can provide a significant reduction in the manufacturing times. The objective of this work is to evaluate quantitatively and qualitatively the surface quality behavior of a steel used in the production of dies and molds. The analysis was carried out for hardened AISI H13 tool steel inclined at an angle of 60° and for different cutting path orientations. The roughness of milled surfaces was measured and verified by scanning electron microscopy in order to compare the strategies and the surface textures obtained. Four different cutting path strategies were employed in these experiments, with horizontal and vertical single-direction rastering, both upward and downward. The conclusion is that, in the vertical upward strategy, the surface displays greater roughness and an irregularity with regard to plastic deformation. The other strategies showed lower roughness and similar regularity. Magnetic Barkhausen noise, used for sub-surface characterization, was found to be largest when measured along the cutting direction of the inserts. These directions coincided with the directions of greatest plastic deformation in the sub-surface region.     

Development and analysis of an online tool condition monitoring and diagnosis system for a milling process and its real-time implementation

This paper addresses the development of an online tool condition monitoring and diagnosis system for a milling process. To establish a tool condition monitoring and diagnosis system, three modeling algorithms–an Adaptive neuro fuzzy inference system (ANFIS), a Back-propagation neural network (BPNN) and a Response surface methodology (RSM)–are considered. In the course of modeling, the measured milling force signals are processed, and critical features such as Root mean square (RMS) values and node energies are extracted. The RMS values are input variables for the models based on ANFIS and RSM, and the node energies are those for the BPNN-based model. The output variable is the confidence value, which indicates the tool condition states–initial, workable and dull. The tool condition states are defined based on the measured flank wear values of the endmills. During training of the models, numerical confidence values are assigned to each tool condition state: 0 for the initial, 0.5 for the workable and 1 for the dull. An experimental validation was conducted for all three models, and it was found that the RSM-based model is best in terms of lowest root mean square error and highest diagnosis accuracy. Finally, the RSM-based model was used to build an online system to monitor and diagnose the tool condition in the milling process in a real-time manner, and its applicability was successfully demonstrated.     

On the machining characteristics of H13 tool steel in different hardness states in ball end milling

This paper investigates and compares the machining characteristics of AISI H13 tool steel in hardness states of 41 and 20 HRC in the ball end milling process. The machining characteristics are illustrated through three types of process outputs from the milling experiments: the milling force, the chip form, and the surface roughness. Characteristic differences in these process outputs are shown to reflect the hardness effect of the tool steel on the ball end milling process. The mechanistic phenomena of the milling process are revealed by the six shearing and ploughing cutting constants extracted from the milling forces. The experimental results show that all the cutting constants of the softer tool steel are greater than those of the hard steel, indicating that higher cutting and frictional energies are required in the chip shearing as well as in the nose ploughing processes of the softer tool steel. The higher cutting energy is also attested by the more severely deformed, shorter, and thicker chips of the softer steel. Surface roughness of the hard steel is shown to be considerably better than that of the soft steel at all cutting speeds and feed rates and is independent of cutting speed, whereas the surface roughness of the softer steel is significantly improved with increasing cutting speed.     

Design of multisensor fusion-based tool condition monitoring system in end milling

Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for the effective monitoring of tool conditions, which is the most crucial feedback information to the process controller. Interestingly, the abundance of data collected from multiple sensors allows us to employ various techniques such as feature extraction, selection, and classification methods for generating such crucial information. While the use of multiple sensors has improved the accuracy in the classification of tool conditions, design of tool condition monitoring system (TCM) for reduced complexity and increased robustness has been rarely studied. Therefore, this paper studies the design of effective multisensor-based TCM when machining 4340 steel by using a multilayer-coated and multiflute carbide end mill cutter. Multiple sensors tested in this paper include force, vibration, acoustic emission, and spindle power sensor for the time and frequency domain data. In addition, two feature selection methods and three classifiers with a machine ensemble technique are considered as design components. Importantly, different fusion methods are evaluated in this paper: (1) decision level fusion and (2) feature level fusion. The experimental results show that the design of TCM based on the feature level fusion can significantly improve the accuracy of the tool condition classification. It is also shown that the highest accuracy can be achieved by using force, vibration, and acoustic emission sensor together with correlation-based feature selection method and majority voting machine ensemble.     

Cutting forces, chip formation, and tool wear in high-speed face milling of AISI H13 steel with CBN tools

High-speed face milling experiments of AISI H13 steel (46–47 HRC) with cubic boron nitride (CBN) tools were conducted in order to identify the characteristics of cutting forces, chip formation, and tool wear in a wide range of cutting speed (200–1,200 m/min). The velocity effects are focused on in the present study. It was found that, at the cutting speed of 800 m/min, which can be considered as a critical value, relatively low mechanical load, relatively low degree of chip segmentation, and relatively long tool life can be obtained at the same time. Both the cutting forces and the degree of chip segmentation firstly decrease and then increase with the cutting speed, while the tool life exhibits the opposite trend. By means of analyzing the wear mechanisms of tools tested under different cutting speeds, it was found that, as the cutting speed increases, the influences of fracture and chipping resulting from mechanical load on tool wear were reduced, while the influences of adhesion, oxidation, and thermal crack accelerated by high cutting temperature became greater. There exist obvious correlations among cutting forces, chip formation, and tool wear.     

An investigation of heat partition and tool wear in hard turning of H13 tool steel with CBN cutting tools

This paper presents results of an investigation into the tool life and the tool wear behaviour of low content CBN cutting tools used in hard turning of hardened H13 tool steel. The approach followed here required both experimental work and finite element thermal modelling. The experiments involved measuring the cutting forces, cutting temperatures, tool wear, and the contact area. Using the measured cutting forces and the contact area in the orthogonal cutting model, we calculated the heat flux on the tool and applied it in the FE thermal analysis. The temperatures history from the analysis was matched with the experimental data to estimate the fraction of heat entering the tool for both conventional and high speeds. The heat partition into the tool was estimated to be around 21–22% for conventional speeds, whereas for high-speed turning, it was around 14%. The tool wear, however, was found to be dominated by chipping for both cutting speeds and could be reduced considerably by reducing the amount of heat entering the tool.     

An artificial-neural-networks-based in-process tool wear prediction system in milling operations

An artificial-neural-networks-based in-process tool wear prediction (ANN-ITWP) system has been proposed and evaluated in this study. A total of 100 experimental data have been received for training through a back-propagation ANN model. The input variables for the proposed ANN-ITWP system were feed rate, depth of cut from the cutting parameters, and the average peak force in the y-direction collected online using a dynamometer. After the proposed ANN-ITWP system had been established, nine experimental testing cuts were conducted to evaluate the performance of the system. From the test results, it was evident that the system could predict the tool wear online with an average error of ±0.037 mm. Experiments have shown that the ANN-ITWP system is able to detect tool wear in 3-insert milling operations online, approaching a real-time basis .     

Characterization of microstructure of H13 tool steel using ultrasonic measurements

This study presents nondestructive characterization of microstructure of AISI H13 hot work tool steel. Heat treatments were carried out in order to obtain different microstructural phases in the tool steel specimens. The microstructural phases were characterized by metallographic examinations and hardness measurements. Velocities of ultrasonic longitudinal and transverse waves were measured by means of pulse-echo method using contact type normal beam probes. Ultrasonic apparent attenuation also determined in the steel specimens having different microstructural phases. A lower value of ultrasonic velocity was observed for the martensite compared to the other microstructures, while the opposite was observed in ultrasonic attenuation. Results show that the use of ultrasonic measurements in order to correlate them with the microstructures is fast and reliable, permitting nondestructive characterization of microstructure in steels.     

Electrochemical characteristics of white layer generated by hard milling AISI H13 steel

In the field of hard cutting, researches on white layer have become more extensive and in-depth in recent years, because white layer has an important influence on the performances and life of components. Nevertheless, properties of white layer, especially its corrosion properties, have not been well understood or clearly defined. In this study, specimens with different subsurface microstructures (no obvious change, dark layer, and white layer) were produced in the process of tool wear to analyze their electrochemical properties using electrochemical methods. It was found that electrochemical properties were distinct between specimens with and without white layer. A specimen with white layer had obviously lower electrochemical impedance and more anodic steady-state open-circuit potential than that without white layer in 3.5 wt.% NaCl solution. That is to say, specimens with white layer were prone to corrosion in this solution. The results also manifest that electrochemical method can be a reliable, convenient, and nearly nondestructive method to detect white layer.     

A predicted modelling of tool life of high-speed milling for SKD61 tool steel

This paper presents an abductive network for predicting tool life in high- speed milling (HSM) operations. The abductive network is composed of a number of functional nodes. These functional nodes are well organised to form an optimal network architecture by using a predicted squared error criterion. Once the cutting speed, feed per tooth, and axial depth of cut are given, tool life can be predicted based on the developed network. Experimental results have shown that the abductive network can be used to predict HSM end mill life under varying cutting conditions and the prediction error of HSM tool life is less than 10%.     

制冷器件晶粒相邻面准共焦成像检测的光学装置

提出了一种半导体制冷器件晶粒相邻面同时准共焦成像检测的光学装置。选择晶粒天面成像光路中直角反射转像棱镜到玻璃载物转盘之间的距离调节来实现双面准共焦成像。设计了晶粒相邻面缺陷同时准共焦成像检测的光学系统,完成了晶粒相邻面缺陷同时准共焦成像检测的实验验证。结果表明,该检测装置可以实现晶粒相邻面缺陷同时成像检测的功能,满足晶粒相邻面缺陷成像检测的性能要求。具有提高检测速度、简化结构且提高系统可靠性等优点,可在晶粒缺陷智能检测筛选系统中获得应用。     

Investigation of micromilled tool steel H13 using tungsten carbide micro-end mills

Micromilling allows for the high precision machining of different types of materials and thus promotes the manufacturing of micro-components for various te     

Empirical models and optimal cutting parameters for cutting forces and surface roughness in hard milling of AISI H13 steel

In the present research, an attempt has been made to experimentally investigate the effects of cutting parameters on cutting forces and surface roughness in hard milling of AISI H13 steel with coated carbide tools. Based on Taguchi’s method, four-factor (cutting speed, feed, radial depth of cut, and axial depth of cut) four-level orthogonal experiments were employed. Three cutting force components and roughness of machined surface were measured, and then range analysis and analysis of variance (ANOVA) are performed. It is found that the axial depth of cut and the feed are the two dominant factors affecting the cutting forces. The optimal cutting parameters for minimal cutting forces and surface roughness in the range of this experiment under these experimental conditions are searched. Two empirical models for cutting forces and surface roughness are established, and ANOVA indicates that a linear model best fits the variation of cutting forces while a quadratic model best describes the variation of surface roughness. Surface roughness under some cutting parameters is less than 0.25 μm, which shows that finish hard milling is an alternative to grinding process in die and mold industry.     

Cutting performance of coated carbide tools in high-speed face milling of AISI H13 hardened steel

In the present study, high-speed face milling of AISI H13 hardened steel was conducted to investigate the cutting performance of coated carbide tools. The characteristics of chip morphology, tool life, tool wear mechanisms, and surface roughness were analyzed and compared for different cutting conditions. It was found that as the cutting speed increased, the chip morphology evolved in different ways under different milling conditions (up, down, and symmetric milling). Individual saw-tooth segments and sphere-like chip formed at the cutting speed of 2,500 m/min. Owing to the relatively low mechanical load, longest tool life can be obtained in up milling when the cutting speed was no more than 1,000 m/min. As the cutting speed increased over 1,500 m/min, highest tool life existed in symmetric milling. When the cutting speed was 500 m/min, owing to the higher mechanical load, the flaked region on the tool rake face in symmetric milling was much larger than that in up and down milling. There was no obvious wear on the tool rake face at the cutting speed of 2,500 m/min due to the short tool-chip contact length. In symmetric milling, the delamination of tool material, which did not occur in up and down milling, was caused by the relatively large cutting force. Abrasion had great effect on the tool flank wear in symmetric milling. With the increment of cutting speed, surface roughness decreased first and then increased rapidly. Lowest surface roughness can be obtained at the cutting speed of about 1,500 m/min.     

Direct additive laser manufacturing using gas- and water-atomised H13 tool steel powders

To date only gas-atomised tool steel powders have been used for direct laser additive manufacturing and the potential benefits of using water-atomised powders have not been explored. As the use of the process in the rapid tooling field is growing, there is a need to explore if the less expensive water-atomised materials can be realistically utilised. A comparative investigation is described, using gas- and water-atomised H13 powder deposited with a CO₂ laser and coaxial powder feed nozzle. Multiple layer wall dimensions, composition, microstructure, surface finish and hardness are related to process conditions and the causes of the observed phenomena are discussed. An energy-balance method is used to model the temperature of the powders and the results used to explain some of the effects. Results indicate that using the lower cost water-atomised powder still allows a metallurgically sound component to be built and does not significantly affect surface finish. The build rate is, however, lower and the water-atomised powder tends to produce slightly softer walls, attributable to a higher temperature during tempering of deposited material by subsequent laser passes.     

钛合金铣削刀具磨损对表面完整性影响研究

为了掌握钛合金TC4铣削过程中刀具磨损对表面完整性的影响规律,通过对不同刀具后刀面磨损量下铣削钛合金工件的表面完整性测试,得出了刀具磨损对表面完整性的影响规律,并对其影响机理进行了分析.结果表明,在刀具处于初期磨损和正常磨损阶段,刀具的挤光效应引起的压应力占主导地位,而在刀具剧烈磨损阶段,加工过程中的热塑性变形引起的拉应力占主导地位;随着刀具后刀面磨损量的增加,刀具正常磨损阶段粗糙度值缓慢增加,剧烈磨损阶段粗糙度值迅速增加;随着刀具后刀面磨损量的增加,已加工表面的显微硬度值和表面层的硬化深度都随之增大.     

Development of a tool wear observer model for online tool condition monitoring and control in machining nickel-based alloys

Online monitoring and in-process control improves machining quality and efficiency in the drive towards intelligent machining. It is particularly significant in machining difficult-to-machine materials like super alloys. This paper attempts to develop a tool wear observer model for flank wear monitoring in machining nickel-based alloys. The model can be implemented in an online tool wear monitoring system which predicts the actual state of tool wear in real time by measuring the cutting force variations. The correlation between the cutting force components and the flank wear width has been established through experimental studies. It was used in an observer model, which uses control theory to reconstruct the flank wear development from the cutting force signal obtained through online measurements. The monitoring method can be implemented as an outer feedback control loop in an adaptive machining system.