Preheating in end milling of AISI D2 hardened steel with coated carbide inserts

This study was conducted to investigate the effect of preheating through inductive heating mechanism in end milling of AISI D2 hardened steel (60-62 HRC) by using coated carbide tool inserts. Apart from preheating, two other machining parameters such as cutting speed and feed were varied while the depth of cut constant was kept constant. Tool wear phenomenon and machined surface finish were found to be significantly affected by preheating temperature and other two variables. End milling operation was performed on a Vertical Machining Centre (VMC). Preheating of the work material to a higher temperature range resulted in a noticeable reduction in tool wear rate leading to a longer tool life. In addition, improved surface finish was obtained with surface roughness values lower than 0.4 um, leaving a possibility of skipping the grinding and polishing operations for certain applications

Prediction of surface roughness in hard milling of AISI D2 tool steel

This paper presents a study of the development of a surface roughness model in end milling of hardened steel AISI D2 using PVD TiAIN coated carbide cutting tool. The hardness of AISI D2 tool lies within the range of 56-58 HRe. The independent variables or the primary machining parameters selected for this experiment were the cutting speed, feed, and depth of cut. First and second order models were developed using Response Surface Methodology (RSM). Experiments were conducted within specified ranges of the parameters. Design-Expert 6.0 software was used to develop the surface roughness equations as the predictive models. Analysis of variance (ANOVA) with 95% confidence interval has indicated that the models are valid in predicting the surface roughness of the part machined under specified condition

Modeling and optimization of surface roughness and vibration amplitude in heat assisted end milling of SKD 11 tool steel using ball nose tool

Tool steel - SKD 11 is frequently used in industries for making dies and molds. This grade is chosen for its toughness, strength, and hardness maintained up to high temperature. However, the same properties make the steel extremely difficult and expensive to machine using conventional approaches. Heat assisted machining has been found wide spread application in recent years to improve machinability of difficult-to-cut materials. This research paper presents the outcome of an investigation on heat assisted end milling of SKD 11 conducted on a vertical machining center using ball nose coated carbide inserts. The Design of Experiments (DoE) was done using the Response Surface Methodology, in order to develop empirical mathematical models of surface roughness and vibration in terms of cutting speed, feed, axial depth of cut, and heating temperature. The models were checked for significance using Analysis of Variance (ANOVA). 3-D response surface graphs of the interactions of primary cutting parameters with the responses were plotted. Optimization was then performed by using the desirability function approach. From the graphs and optimized results it was concluded that the primary input parameters could be controlled in order to reduce vibration amplitude and produce semi-finished machined surfaces applying induction heat assisted technique

Prediction of tool life in end milling of hardened steel AISI D2

Most published research works on the development of tool life model in machining of hardened steels have been mainly concerned with the turning process, whilst the milling process has received little attention due to the complexity of the process. Thus, the aim of present study is to develope a tool life model in end milling of hardened steel AISI D2 using PVD TiAIN coated carbide cutting tool. The hardness of AISI D2 tool lies within the range of 56-58 HRC. The independent variables or the primary machining parameters selected for this experiment were the cutting speed, feed, and depth of cut. First and second order models were developed using Response Surface Methodology (RSM). Experiments were conducted within specified ranges of the parameters. Design-Expert 6.0 software was used to develop the tool life equations as the predictive models. The predicted tool life results are presented in terms of both 1st and 2nd order equations with the aid of a statistical design of experiment software called Design-Expert version 6.0. Analysis of variance (ANOVA) has indicated that both models are valid in predicting the tool life of the part machined under specified condition and the prediction of average error is less than 10%

Modeling of surface roughness during end milling of AISI H13 hardened tool steel

Hard machining, a frequently used term in today’s machine tool industries, refers to the machining of material with a hardness value over 45 HRC. The concept of hard machining was developed in 80s; however, the prevalent industrial implementation of hard part machining was adopted during the last decade [1]. Advantages in hard machining incorporate the complete machining process with a single fixture setup, eliminating intermediate heat treatment and final grinding process while still meeting the dimensional and surface roughness specifications [2]. The widespread demand of hardened tool steel like AISI H13 requires high speed machining (HSM). Over the last decade, HSM has been used to manufacture molds/dies made from AISI H13. Many progressive works have been carried out to improve the high speed machining performance of H13. However, despite the significant importance of surface finish most of the machining researchers to date have concentrated on chip morphology, tool life and wear mechanism. In hard part machining, surface finish is a major quality criterion. With an accurate level of roughness it is possible to eliminate final grinding process, sometime even the hand polishing [3]. Considering its importance some researchers conducted their studies on surface quality during hard machining. Choudhury at el. [4] applied Taguchi method for the prediction of surface roughness during the end milling of AISI H13 tool steel and found that roughness value tends to decrease with increasing cutting speed and decreasing feed rate. El-Baradie [5] drew similar conclusion on cutting speed. He observed that increase of cutting speed maximizes productivity, at the same time, it improves surface quality. However, in all of the above cases and other works related to the surface roughness study it was found that the lowest achievable surface finish was only 0.2 μm. at high cutting speed mode. In most of the cases roughness values were sufficiently high to fall in the grinding region (above 0.2 to 0.4 μm). Moreover, material removal rate was limited for using lower radial depth of cut and feed per tooth (Rd = 0.3 mm to 0.8 mm and f = 0.10 mm/tooth). In this context, considering the influence of surface finish on mold/die for net shape manufacturing, current paper deals with the performance of PCBN and PVD-TiAlN coated carbide tool inserts in terms of surface roughness during the end milling of H13 hardened tool steel (52 HRC). In this regard, mathematical models based on the experimental results were developed in order to predict the surface finish during the end milling of H13 hardened tool steel

Development of tool life prediction model of TiAlN coated tools during the high speed hard milling of AISI H13 steel

Considering the demand for reduced cycle time and increased productivity hard turning and milling have become a useful alternative when high material removal rate is an immense requirement. Advantages in hard machining incorporate the complete machining process with a single fixture setup, eliminating intermediate heat treatment and final grinding process while still meeting the dimensional and surface roughness specifications [1]. Over the last decade high speed machining has been used extensively to produce mould and die from hardened material like AISI H13 tool steel. Many progressive works have been carried out to improve the high speed machining performance of H13. Despite the widespread adoption of milling process in fabricating mould and die, most of the research works till to date concentrated on hard turning. J. J. Junz Wang & M. Y. Zheng, 2003 et al [1], illustrated the machining characteristics of AISI H13 tool steels of hardness 41 and 20 HRC and found that the higher cutting and frictional energies are required in the chip shearing as well as in the nose ploughing processes of the softer tool steel. Poulachon et al. [2] , showed that the major influencing parameter on tool-wear happens to be the presence of carbides in the steel microstructure. Ghani et al. [3] applied Taguchi method to optimize cutting parameters in end milling of H13 steel at high speed cutting. They found that feed and depth of cut possess the most significant effect over tool life for a given range of cutting speed, feed and depth of cut. Recently with the advent of new fabrication and coating technology, tool insert like TiAlN coated carbide is receiving increasing attention from both industrial and research communities. Coated carbide tools enjoy lower price than CBN tools, (normally used for hard machining) but have a shorter tool life with lower material removal in comparison to PCBN. Early prediction of tool wear during high speed machining by coated carbide is quite important since high tool wear has an adverse effect on surface finish, which is considered to be the major quality criterion of finished part. In this context, in present study, an appropriate model for effective prediction of tool life has been developed during the high speed end milling of H13 tool steel using PVD-TiAlN coated tool inserts. RSM is a statistical method that combines design of experiments, regression analysis and statistical inferences [4]. RSM also reduces total number of trials needed to generate the experimental data in order to response model. The application of RSM in machining parameter optimization was first reported to be used by SM.Wu, 1965. Since then many researchers have been using this technique to design their experiments and model the responses. Alauddin et al. [5] used RSM to optimise the surface finish in end milling of Inconel 718 under dry condition. They developed contours to select a combination of cutting speed, and feed without increasing the surface roughness. Öktem et al. [6] incorporated RSM with developed genetic algorithm to optimize cutting parameters for better surface quality in case of Inconel 718. S. Saikumar and M. S. Shunmugam et al. [7] also combined RSM with differential evolution and genetic algorithms to draw a comparison between these methods. In current study, the model has been developed by RSM in terms of cutting speed (v), feed (f) and axial depth of cut (a). Experimental runs were designed based on the principles of central composite design (CCD) of RSM. Tool life data collected from the experimental trials were used to formulate the RSM models

Dental Anxiety and Fear among Patients in Jazan, Kingdom of Saudi Arabia: A Cross-sectional Study

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