AISI D2 Tool Steel Research Articles

Martensitic transformation in AISI D2 tool steel during continuous cooling to 173 K

Martensitic transformation of AISI D2 tool steel continuously cooled from 1303 K to the cryogenic temperature of 173 K is investigated by dilatometry using 10 or 50 K s−1 cooling rates. A ‘typical’ expansion takes place from the \( M_{\text{s}} \) temperature and reaches a maximum at 325 K. However, an atypical behavior is observed below this temperature implying the activation of further martensitic transformation. A modification to existing equations is proposed, which allows for more accurate description of the kinetics of martensitic transformation. Scanning electron microscopic studies indicated the presence of plate and lath martensite for both cooling rates. Carbide precipitation takes place at the rate of 10 K s−1 before the start of martensitic transformation while it was not observed when the 50 K s−1 rate was used. Transmission electron microscopic studies revealed that the microstructure also contains a significant amount of nano-twinned martensite.

The effects of process parameters on acceleration amplitude in the drilling of cold work tool steels

This study investigated the effects of process parameters on acceleration amplitude in the drilling of AISI D2 and AISI D3 cold work tool steels using HSS twist drills of two different lengths. Workpiece hardness, drill length/tool overhang, cutting speed, feed rate, and number of holes drilled were chosen as the process parameters. In the tests made by using the full factorial (FF) design technique, the values of induced machining vibrations occurring on the workpiece during drilling were specified as the acceleration amplitude. Measurement results were evaluated by linear regression analysis, and a second-order equation was developed for the prediction of the acceleration amplitude. The correlation coefficient of the predictive equation at a 95 % confidence level was calculated as R 2 = 0.703. The main and interaction effects of the process parameters on the acceleration amplitude were evaluated, and the percentage contributions of the process parameters to the acceleration amplitude were determined by variance analysis. In addition, scanning electron microscope (SEM) and optical microscope images of the drill wear were taken, and the wear characteristics and acceleration amplitude were correlated. The most effective parameters on acceleration amplitude were determined as the number of holes drilled, workpiece hardness, cutting speed, and feed rate. No significant effect of drill length/tool overhang was observed. With increasing cutting speed, number of holes drilled and the drill length/tool overhang, the acceleration amplitude values increased, although they decreased with increasing workpiece hardness.

Microstructure, Mechanical and Corrosion Properties of Friction Stir-Processed AISI D2 Tool Steel

In this study, AISI D2 tool steel underwent friction stir processing (FSP). The microstructure, mechanical properties, and corrosion resistance of the FSPed materials were then evaluated. A flat WC-Co tool was used; the rotation rate of the tool varied from 400 to 800 rpm, and the travel speed was maintained constant at 385 mm/s during the process. FSP improved mechanical properties and produced ultrafine-grained surface layers in the tool steel. Mechanical properties improvement is attributed to the homogenous distribution of two types of fine (0.2-0.3 μm) and coarse (1.6 μm) carbides in duplex ferrite-martensite matrix. In addition to the refinement of the carbides, the homogenous dispersion of the particles was found to be more effective in enhancing mechanical properties at 500 rpm tool rotation rate. The improved corrosion resistance was observed and is attributed to the volume fraction of low-angle grain boundaries produced after friction stir process of the AISI D2 steel.

Alternative phase transformation path in cryogenically treated AISI D2 tool steel

This study based on dilatometry test and microstructural observations showed a different phase transformation path during conventional heat treatment of AISI D2 tool steel instead of the traditionally expected fully martensitic transformation. Transformation started with carbide precipitation and continued with allotriomorphic ferrite, Widmanstätten ferrite, acicular ferrite, and finally bainite formation. This microstructure showed microhardness value of 730HV, which is very high for conventionally cooled samples. They are not expected to have gone under martensitic transformation. The results indicated that bainitic transformation take place even at temperatures near 173K and to obtain fully martensitic microstructure, cooling rates in the range of 50Ks−1must be utilized.

A contribution in EDM simulation field

Electrical discharge machining (EDM) simulation isproposedinthisstudythroughanimprovednumerical mod- el based on the finite difference method. This model takes into account the growth of the plasma channel and the instanta- neous removal of material throughout the electric discharge. The criteria used to assess the validity of the model are the material removal rate and morphology of the crater. The com- parison of numerical and experimental results, in the case of the tool steel AISI D2, shows encouraging results.

Electromagnetic nondestructive evaluation of tempering process in AISI D2 tool steel

The present paper investigates the potential of using eddy current technique as a reliable nondestructive tool to detect microstructural changes during the different stages of tempering treatment in AISI D2 tool steel. Five stages occur in tempering of the steel: precipitation of ɛ carbides, formation of cementite, retained austenite decomposition, secondary hardening effect and spheroidization of carbides. These stages were characterized by destructive methods, including dilatometry, differential scanning calorimetry, X-ray diffraction, scanning electron microscopic observations, and hardness measurements. The microstructural changes alter the electrical resistivity/magnetic saturation, which, in turn, influence the eddy current signals. Two EC parameters, induced voltage sensed by pickup coil and impedance point detected by excitation coil, were evaluated as a function of tempering temperature to characterize the microstructural features, nondestructively. The study revealed that a good correlation exists between the EC parameters and the microstructural changes.

Effect of Tool Material Properties and Cutting Conditions on Machinability of AISI D6 Steel During Hard Turning

Hard turning offers numerous advantages to grinding operation; however, there is a critical need for research to clarify issues related to high cutting forces, high temperatures, and surface roughness to meet the challenges it can offer as an alternate to grinding process. Mathematical models are generated for each response variable (main cutting force, thrust force, cutting temperature, and surface roughness) in terms of actual values of the factors (cutting speed, feed, and tool material) to establish relationships using design expert software for statistical investigation. A 33 full-factorial design with a total of 27 experiments was obtained for parametric analysis and investigation of machinability of AISI D6 tool steel using three different grades of low-content cubic boron nitride (CBN-L) inserts. The parametric analysis study shows that the main cutting force, thrust force, and surface roughness increase with feed. The thrust force and cutting temperature get influenced by tool material properties. It was established that grain size, CBN content, edge geometry, and hardness of the tools affected all the output characteristics. Scanned electron microscopy and energy-dispersive X-ray of the worn tools showed crater wear, chipping, and fracture of cutting edges, while abrasion and diffusion/dissolutions in CBN tools were the wear mechanisms affirmed in this study.

Assessment of Retained Austenite in AISI D2 Tool Steel Using Magnetic Hysteresis and Barkhausen Noise Parameters

Inaccurate heat treatment process could result in excessive amount of retained austenite, which degrades the mechanical properties, like strength, wear resistance, and hardness of cold work tool steel parts. Thus, to control the mechanical properties, quantitative measurement of the retained austenite is a critical step in optimizing the heat-treating parameters. X-ray diffraction method is the most frequently used technique for this purpose. This technique is, however, destructive and time consuming. Furthermore, it is not applicable to 100% quality inspection of industrial parts. In the present paper, the influence of austenitizing temperature on the retained austenite content and hardness of AISI D2 tool steel has been studied. Additionally, nondestructive magnetic hysteresis parameters of the samples including coercivity, magnetic saturation, and maximum differential permeability as well as their magnetic Barkhausen noise features (RMS peak voltage and peak position) have been investigated. The results revealed direct relations between magnetic saturation, differential permeability, and MBN peak amplitude with increasing austenitizing temperature due to the retained austenite formation. Besides, both parameters of coercivity and peak position had an inverse correlation with the retained austenite fraction.

Optimization of Wire EDM of AISI D3 Tool Steel using Orthogonal Array with Principal Component Analysis

Wire electric discharge machining, a non-traditional machining process comprising zinc coated wire electrode, has been developed for machining of electrically conductive difficult-to-machine very hard materials. Modernization of mechanical industry has lead to increase in demand for hard and tough materials and, having wide applications in aircraft and rocket industries, is important from the quality of cut point of view. Keeping this in view, a hybrid approach of Taguchi method (TM) and principal component analysis (PCA) has been applied for multi-objective optimization (MOO) WEDM of AISI D3 tool steel to achieve better cut qualities within existing resources. The quality characteristics MRR, have been considered for simultaneous optimization. The input parameters considered are pulse on time .pulse off time, peak current and servo voltage. Initially, single-objective optimization has been performed using TM and then the signal-to-noise (S/N) ratios obtained from TM have been further used in PCA for multi-objective optimization. The results of MOO include the prediction of optimum input parameter level and their relative significance on multiple quality characteristics (MQC).

Multi-Objective Optimization of Wire EDM of AISI D3 Tool Steel Using Orthogonal Array with Principal Component Analysis

Wire electric discharge machining, a non-traditional machining process comprising zinc coated wire electrode, has been developed for machining of electrically conductive difficult-to-machine very hard materials. Modernization of mechanical industry has lead to increase in demand for hard and tough materials and, having wide applications in aircraft and rocket industries, is important from the quality of cut point of view. Keeping this in view, a hybrid approach of Taguchi method (TM) and principal component analysis (PCA) has been applied for multi-objective optimization (MOO) WEDM of AISI D3 tool steel to achieve better cut qualities within existing resources. The three-quality characteristics MRR, Length of Cut, and Surface roughness have been considered for simultaneous optimization. The input parameters considered are pulse on time .pulse off time, peak current and servo voltage. Initially, single-objective optimization has been performed using TM and then the signal-to-noise (S/N) ratios obtained from TM have been further used in PCA for multi-objective optimization. The results of MOO include the prediction of optimum input parameter level and their relative significance on multiple quality characteristics (MQC).

Application of a general regression neural network for predicting radial overcut in electrical discharge machining of AISI D2 tool steel

In the present research, an artificial neural network (ANN) model has been presented to investigate the variation of the radial overcut, in the electrical discharge machining (EDM). The radial overcut significantly influences the precision and accuracy of the product. Due to the nonlinear, complex relationship amongst the electrode wear, the electrode diameter, EDM parameters, and the machine positioning accuracy the final work piece dimensions are difficult to predict. A full factorial design was used to conduct the experiments the obtained data were segregated in three parts to train the network, to testing for convergence and finally to validate the model. The mean square error convergence criteria, both in training and testing, came out very well. The developed models are found to approximate the responses quite accurately. Results reveal that the proposed models can be employed successfully in the prediction of the stochastic and complex EDM process.

Effect of Single and Double Austenitization Treatments on the Microstructure and Hardness of AISI D2 Tool Steel

This investigation is concerned with the study of effect of Double Austenitization (DA) and Single Austenitization (SA) heat treatment processes on microstructure and mechanical property of AISI D2type cold worked tool steel. To maximize hardness, tool steels are used in a quenched and tempered condition. This involves heating the material to the austenitizing temperature (∼850−1100°C), quenching at an appropriate rate to form martensite, and tempering to reduce the retained austenite content and induce toughness. The merits of DA treatment isto promote dissolution of carbides at the same time proscribe grain coarsening significantly was attempted in D2 tool steel. The study has found that DA treatment has induced high hardness with insignificant growth in grains. The increase in hardness is attributed to increase in carbon content in matrix due to dissolution of carbides; whereas finer grains due to role of inclusions.

Adaptive neuro fuzzy inference system modelling of multi-objective optimisation of electrical discharge machining process using single-wall carbon nanotubes

Electrical discharge machining (EDM) has new avenues for finishing of hard and brittle materials with nano surface finish, high tolerance and accuracy. The single-wall carbon nanotube (SWCNT) is mixed with kerosene dielectric fluid in die sinking EDM process. The analysis of surface characteristics like surface roughness and metal removal rate of AISID2 tool steel materials were carried out and an excellent machined nano finish can be obtained by setting the machining parameters at optimum level. This study deals with modelling of surface roughness with SWCNT-based EDM using adaptive neuro fuzzy inference system approach. The first-order Sugeno-type fuzzy interference modelling has been used to predict the output parameters and compared with experimental values. The R2 value of regression model for with CNT is 0.706 and for without CNT is 0.652. The high R2 indicate that better model fit the data very well using CNT-based machining. The proposed model can also be used for estimating surface roughness and metal removal rate online.

Evaluation of Machinability in Turning of Engineering Alloys by Applying Artificial Neural Networks

The present paper investigates the influence of main cutting parameters on the machinability during turning process for three typical materials namely AISI D6 tool steel, Ti6Al4V ELI and CuZn39Pb3 brass, all three under dry cutting environment. Spindle speed, feed rate and depth of cut were selected for study whilst arithmetic surface roughness average (Ra) and main cutting force component (FC) were treated as quality objectives characterizing machinability. For the aforementioned materials a full factorial design of experiments was conducted to exploit main effects and interactions among parameters it terms of quality objectives. The results obtained from dry turning experiments were utilized as a data set to test, train and validate a feed-forward back propagation artificial neural network for machinability prediction regarding all three materials. The work presents the results obtained from the aforementioned experimental effort under an extensive state-of-the-art survey concerning neural network technology and implementation to machining optimization problems.

English

The aim of this study is to investigate experimentally the microstructural evolution of the joints using ledeburitic AISI D2 Tool Steel parts. The application of a new process of the semisolid joining of two parts of AISI D2 cold-work tool steel is proposed using a partial remelting method. Samples were heated in an argon atmosphere up to 1300°C and held for 10 minutes. Metallographic analyses along the joint interface showed a smooth transition from one to the other and neither oxides nor microcracking was observed. The current work successfully confi rmed that avoidance of a dendritic microstructure in the semisolid joined zone and high bonding quality components can be achieved without the need for force or complex equipment when compared to conventional welding processes.

A Study of Microstructure Properties of AISI D2 Tool Steel in Partial Re-Melting Method

Due to the growing demand for cold-work tool steel in various industrial applications, it is crucial to improve the fabrication technique, because complex shapes involve an extensive and costly workshop effort. Hence, a one-step net-shaping process, such as the semi-solid forming, could offer great benefits. With the aim of finding a minimum process chain for the manufacturing of a high-quality production, the microstructural evolution of the ledeburitic AISI D2 tool steel in the semi-solid-state was studied experimentally via the Direct Partial Re-Melting Method (DPRM). Samples were heated in an argon atmosphere up to 1255°C, which corresponded to about 16% of liquid fraction, and held for 0 minute. The microstructural observation after DPRM showed that the equiaxed austenite grains are observable within a small liquid matrix. The microstructure also contains primary, non-dissolved carbides with a new, precipitated eutectic.

Experimental investigation of surface integrity in finish dry hard turning of hardened tool steel at different hardness levels

In this paper, the influences of cutting speed, depth of cut, feed, workpiece hardness (51, 55, 58, 62, and 65 ± 1 HRC), and nose radius on surface integrity in finish dry hard turning (FDHT) of the hardened tool steel AISI D2 by utilizing the polycrystalline cubic boron nitride (PCBN) inserts were experimentally investigated. Experimental results showed that the ploughing effect, serious squeeze, and elastic deformation have more significant on the surface roughness than those black fusion welding materials and side flows, and the surface roughness values are in the range of 0.34–0.86 μm between 150- and 301-m/min cutting speeds and totally attains a surface finish of grinding. The surface roughness is not very sensitive to the depth of cut in the range from 0.10 to 0.25 mm. The “residual cutting” materials, material plastic deformation, and even cohesion in the machined surface and cold welding effect do occur and hence influence on the surface integrity. The tiny grooves, severe plastic flow, and extensive material flows at lower feeds have significant influence on the surface integrity. The subsurface damages produced by the turning process also do occur and become severer at the larger feeds due to the localized stretch, plastic deformation, and even dilacerations of subsurface material induced by the friction from the tool flank face. The thermal softening, serious material side flows, and squeezing have significant effect on surface integrity when FDHT of the hardened tool steel AISI D2 at different hardness levels. And the heat effect is greater than the geometry effect as the nose radius attains a certain value.

Optimization of Wire Electro Discharge Machining Parameters for Improving Surface Finish of Cryo-Treated Tool Steel Using DOE

Obtaining good surface finish in tool steels by conventional machining is a difficult task. Nontraditional material removal processes, for example, wire electro discharge machining (WEDM), are very useful for precision manufacturing. In WEDM, it is very difficult to choose the correct combination of machining parameters and materials. Traditionally, heat treatment process is used for improving the hardness of tool steels, whereas cryogenic treatment is used as a secondary treatment to enhance the hardness as well as the wear properties. In this work, the effect of WEDM parameters on surface finish of cryo-treated AISI D2 tool steel is investigated. Taguchi DOE methodology is used for designing and conducting the experiments. The surface roughness was considered a response for improving surface quality. ANOVA is used to optimize the machining process for better surface quality. A mathematical model is developed for predicting the response quality using regression analysis. Further experiments were conducted with optimized parameters to authenticate the usefulness of the proposed model. The confirmation experimental result shows that the model suggested by the Taguchi's method is suitable for improving the surface quality by reducing the surface roughness.

Empirical models for cutting forces in finish dry hard turning of hardened tool steel at different hardness levels

In this research, the exponential and quadratic polynomial empirical models for three-component cutting forces by employing five factors, such as the cutting speed, depth of cut, feed, workpiece hardness, and nose radius, were developed by utilizing the orthogonal regression methodology (ORM) and response surface methodology (RSM). On the other hand, an attempt has been made to experimentally investigate the effects of those factors on three-component cutting forces in finish dry hard turning (FDHT) of tool steel AISI D2 with the PCBN tool. In this investigation, based on five-factor three-level orthogonal experiments, three-component cutting forces were measured, and then, analysis of variance (ANOVA) was performed to estimate the significance of developed models and analyze the main and interaction effects of the factors. The experimental results indicated that the RSM quadratic polynomial empirical model (RSMQPEM) is much more accurate and credible than the ORM exponential empirical model (ORMEM) in predicting the three-component cutting forces. It was also found that the cutting speed and feed are the two dominant factors affecting the main cutting forces F Z; the feed is the one dominant factor affecting radial cutting force F Y and the feed cutting force F X. Additionally, the optimum cutting parameters for the hardened materials with 51, 55, 60, and 64 HRC was found.

Surface roughness prediction using Taguchi-fuzzy logic-neural network analysis for CNT nanofluids based grinding process

The present study highlights the Taguchi design of experiment techniques proved to be an efficient tool for the design of neural networks' surface roughness to predict in the grinding process, where CNT mixed nanofluids are used as dielectric for machining AISI D3 Tool steel material. Empirical model for the prediction of output parameters has been developed using regression analysis and the results are compared for with and without using CNT nanofluids. Analysis of variance and F test is used to determine the significant parameter affecting the surface roughness which is the crucial parameter for any grinding process. Feedforward artificial neural networks are used to train the experimental values with the Levenberg---Marquardt algorithm; the most influencing factors are determined. The predicted surface roughness for without using CNT based cutting fluid is 11.3 % and with CNT is 10.37 %. Further, a fuzzy logic system is used to investigate the relationship between the machining process parameters' accuracy and to determining the efficiency of each parameter design with Taguchi design of experiments.