Enhancement of machinability of Inconel 718 in end milling through online induction heating of workpiece

This paper presents the outcome of a study on heat assisted end milling of Inconel 718 using inducting heating technique conducted to enhance the machinability of the material. The heating temperature maintained below the phase transformation temperature was aimed at softening the top removable material layers. The experimental results of both conventional and heat assisted machining were compared. The machinability of Inconel 718 under these conditions was evaluated in terms of tool life, tool wear morphology and chatter. The advantages of Induction heating is demonstrated by an longer tool life and lower chatter. The study showed that preheated machining facilitates up to 80% increase of tool life over conventional machining conducted using TiAlN coated carbide inserts

Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy

In this work, an artificial neural network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and surface roughness during high speed end milling of nickel-based Inconel 718 alloy. The input parameters of the ANN model are the cutting parameters: cutting speed, feed, and axial depth of cut. The output parameter of the model was surface roughness. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical three-level full factorial experimental design technique. A predictive model for surface roughness was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated when end milling Inconel 718 alloy with single-layer PVD TiAlN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production

Enhancement of machinability by workpiece preheating in end milling of Ti-6Al-4V

Purpose: The main objective of this paper is to investigate the effect of workpiece preheating with high frequency induction heating on improvement of machinability of Ti-6Al-4V during end milling using PVD TiAlN coated inserts. Tool life, cutting force and vibration were investigated during the experiments. Design/methodology/approach: End milling tests were conducted on Vertical Machining Centre (VMC ZPS, Model: MCFV 1060 with quarter immersion cutting. Titanium based alloy Ti-6Al-4V bar was used as the work-piece. Machining was performed with a 20 mm diameter end-mill tool holder (R390-020B20-11M) fitted with one insert. PVD TiAlN coated carbide inserts (R390-11 T3 08E-ML 2030) were used in the experiments. All of the experiments were run at room temperature and preheated conditions. The preheated temperature was maintained at 420ºC and no phase change of the workpiece in preheating was ensured from the phase diagram of Ti-6Al-4V. High frequency induction heating was utilized to run the preheated machining. Findings: Preheating helps in substantially increasing tool life and in lowering down the cutting force value, lowering the amplitude of vibration and dynamic forces. Practical implications: The cost of machining Ti-6Al-4V is extremely high because of the relatively low machining speed and short tool life. Therefore, improving the machinability of Ti-6Al-4V is a research topic of much interest, with a number of approaches reported with varied results, such as, cryogenic cutting, highpressure coolant, rotary-tool, and minimum quantity lubrication (MQL). Originality/value: A new approach of induction preheating to overcome the difficulties in machining of Ti-6Al-4V is presented in this paper. In preheated machining, high frequency induction heating is used as an external heat source to soften the work material surface layer in order to decrease its tensile strength and strain hardening. An experimental study has been performed to assess the effect of workpiece preheating using induction heating system to enhance the machinability of Ti-6Al-4V. The preheating temperature was maintained below the phase change temperature of Ti-6Al-4V

A case study on design and manufacture of tilting pad thrust bearing for steam turbine of fertilizer factories

Bangladesh has to depend mainly on imported spare parts for maintenance of its major industries. The picture is not different in the case of the fertilizer factories. Tilting pad thrust bearing is a critical and very expensive spare part of fertilizer factories. Like many other spares this spare has to be imported because it is not locally manufactured. The country is loosing a huge amount of foreign currency in importing this and other spare parts. Apart from the high procurement cost import involves long lead time. In order to avoid factory shut downs a lot of spares are also kept in reserve. This leads to very high inventory costs which results in high factory overhead costs. As an attempt to solve these problems the present study was under taken. The main objective of the work was to ascertain whether local manufacture of a critical spare parts like tilting pad thrust bearing was at all feasible. Design and manufacture of the above mentioned bearing were performed under the present case study. Tests were performed to identify the material of the bearing lobes as well as the composition of the of the white metal layer. Since no working or detail drawing of the part was available in the factory, the different dimensions of the spare part were determined by precision measurements and by analytical methods. Apart from that the sequence of manufacturing operations were so designed as to permit its manufacturing using local facilities. Fixtures required for maintaining the required precision of manufacturing were designed appropriately. The part was subsequently manufactured using technical facilities of BUET and BITAC. The lobes were precisely machined and then white metalling was performed. The bearing lobes were then ground and polished to accurate dimensions. Finally the jobs were checked for quality and are ready for practical tests

Comparison of uncoated and coated carbide inserts in end milling of Ti–6Al–4V in terms of surface roughness

This paper compares and also optimizes the surface finish in end milling of titanium alloy Ti-6Al-4V using uncoated and PVD TiAlN coated carbide inserts under dry conditions. Response Surface Methodology (RSM) is utilized to develop an efficient mathematical model for surface roughness in terms of cutting speed, feed and axial depth of cut. For this purpose, a number of machining experiments based on factorial design of experiments method are carried out. The Center Composite Design (CCD) surface roughness models have been developed at 95% confidence level. The adequacy of the models has been verified through analysis of variance (ANOVA). Then the RSM models were further coupled with Genetic Algorithm (GA) to optimize the cutting conditions for getting achievable minimum surface roughness. The GA outcomes were further verified by experimental results. It was found that GA results matched successfully with the experimental data. Uncoated carbide insert was stumbled on as a better option than TiAlN coated carbide in terms of surface roughness

Development of an artificial neural network algorithm for predicting the cutting force in end milling of Inconel 718 alloy

To predict the required cutting force is necessary to realize the potentials of difficult-to-cut materials and get better efficiency. Cutting force is a critical and important target while machining because the change of it will affect surface finish, tool wear, vibration etc. The forces that are developed during the milling process can directly or indirectly measure/estimate process parameters of end milling such as, tool life, tool wear, surface finish etc. For the instance, excessive cutting forces generally result in low product quality while small cutting forces often indicate low machining efficiency [1]. Therefore controlling these forces is of vital importance. Because of its paramount significance, researchers have been trying to develop mathematical models that would predict the cutting forces based on the geometry and physical characteristics of the process. A.S. Mohruni et al [2] developed the cutting force models where the primary machining parameters such as cutting speed, feed and radial rake angle were used as independent variables for factorial design of experiment coupled with response surface methodology (RSM). Kuang-hua fuh et al proposed a predicted milling force model for the end milling operation. In that study, the spindle rotation, feed, axial and redial depth of cut are considered as the affecting factors and an orthogonal rotatable central composite design and the response surface methodology were used to construct the model [3]. Such prediction could then be used to optimize the process. Nonetheless, due to its complexity, the milling process still poses a challenge to the modeling and simulation research effort. In fact, most of the research works reported pertained to this are based on either analytical or semi-empirical approaches, has in general shown only limited levels of accuracy and/or generality. ANN offers an alternative way to simulate complex and ill defined problems. As the machining process is nonlinear and time-dependent, it is difficult for the traditional identification methods to provide an accurate model. Compared to traditional computing methods, the artificial neural network (ANN) is robust and global. ANN has the characteristics of universal approximation, parallel distributed processing, hardware implementation, learning and adaptation, and multivariable systems. Because of this, ANN is widely used for system modeling, function optimizing, image processing, and intelligent control. ANN gives an implicit relationship between the input(s) and output(s) by learning from a data set that represents the behavior of a system [4]. In the present paper, a different approach that is based on advanced artificial intelligence techniques is implemented and tested. More specifically two different neural networks are used to predict the forces developed during End milling. The network is selected based on certain criteria

Wear mechanism in end milling of Inconel 718

Nickel-based, creep-resistant, superalloy Inconel 718 is amongst the most difficult alloys to machine. The main reason for the poor machinability of the alloy is the high work-hardening rate by the precipitation of a γ’ phase and the presence of hard abrasive phases such as titanium carbide, niobium carbide and the Ni3AlTi phase. Generally, increasing the amount of γ’ phase by increasing the amount of titanium and aluminum increases the rate of tool wear [1]. The nickel-based alloys also retain their strength at elevated temperatures and this result in high cutting forces even at high cutting speeds for which high temperatures are generated [2]. It is very complicated to predict tool life in end milling with sufficient accuracy on the basis of controllable process parameters. Nevertheless, it is an essential part of a machining system in the automated factory to change tools automatically due to wear or catastrophic failure. A number of tool materials were used by the researchers in an attempt to increase machinability of Inconel 718 so far, such as, coated tungsten carbide, alumina (Al2O3), SiC whiskerreinforced alumina and cubic boron nitrate (CBN) etc. [3],[4],[5]. Of these materials coated tungsten carbide is the most widely used. Currently, it is estimated that over 80-85% of all carbide tools sold are coated [6]. In general, coated tools perform better when machining nickel-based superalloys due to the coatings increased hardness, ability to act as a barrier to thermal and atomic diffusion and by altering the coefficient of friction [7]. Derrien et al found that TiN coated tools resulted in higher tool life and lower surface roughness that uncoated tools when milling Inconel 718 [8]. Gatto et al recommended that CrN and TiAlN coatings improved tool performance by acting as a thermal barrier and therefore preventing the high temperature generated in the cutting process from softening the substrate [9]. TiAlN and CrN coated carbide tools were compared in end milling of Inconel 718 by Sharman et al [10] and it was found that TiAlN gave on an average three times better performance compared to CrN in terms of metal removal, due to the lower hardness (lower abrasive wear resistance) and higher chemical affinity of CrN to Inconel 718. It concluded that under conditions where thermal rather than mechanical stresses predominate, the TiAlN coating would be expected to give better results

Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

Surface roughness is one of the important factors for evaluating workpiece quality during the machining process because the quality of surface roughness affects the functional characteristics of the workpiece such as compatibility, fatigue resistance and surface friction. The factors that affect the surface roughness during the end milling process include tool geometry, feed rate, depth of cut and cutting speed. Several researchers have studied the end milling process in the recent years. The researchers also used response surface methodology (RSM) to explore the effect of cutting parameters as cutting speed, feed rate and axial depth of cut. Alauddin et al. [1] developed a mathematical model to predict the surface roughness of steel after end milling. The prediction model was expressed via cutting speed, feed rate and depth of cut. Fuh and Hwang [2] used RSM to construct a model that can predict the milling force in end milling operations. But as the machining process is nonlinear and time-dependent, it is difficult for the traditional identification methods to provide an accurate model. Compared to traditional computing methods, the artificial neural networks (ANNs) are robust and global. ANNs have the characteristics of universal approximation, parallel distributed processing, hardware implementation, learning and adaptation, and multivariable systems [3]. ANNs have been extensively applied in modeling many metal-cutting operations such as turning, milling, and drilling [4-5]. However, this study was inspired by the very limited work on the application of ANNs in modeling the relationship between cutting conditions and the surface roughness during high-speed end milling of nickel-based, Inconel 718 alloy

Assessment of performance of uncoated and coated carbide inserts in end milling of Ti-6Al-4V through modelling

Materials used in the manufacturing of aero-engine components generally comprise nickel and titanium base alloys. A major requirement of cutting tool materials used for machining aero-engine alloys is that they must possess adequate hot hardness to withstand the elevated temperatures generated at high speed conditions of aerospace alloys. Most cutting tool materials lose their hardness at elevated temperatures resulting in the weakening of the interparticle bond strength and consequent acceleration of tool wear which results in deterioration of surface roughness. So it is very essential to establish an adequate functional relationship between the responses (such as surface roughness, tool life) and the cutting parameters (cutting speed, feed and depth of cut). Response surface methodology (RSM) may help in establishing the relationships between surface roughness and the cutting parameters for coated and uncoated inserts. The method was introduced by G.E.P Box and Wilson [1]. The main idea of RSM is to use a set of designed experiments to obtain an optimal response with limited number of experiments to save cost and time. RSM is a dynamic and foremost important tool of design of experiment (DOE), wherein the relationship between response(s) of a process with its input decision variables is mapped to achieve the objective of maximization or minimization of the response properties [1,2]. Many machining researchers have used response surface methodology to design their experiments and assess results. Analytical models have been created to predict surface roughness and tool life in terms of cutting speed, feed and axial depth of cut in milling steel material [3] and [4]. An effective approach has also been presented to optimize surface finish in milling Inconel 718 [5]. Kaye et al [6] used response surface methodology in predicting tool flank wear using spindle speed change. Wu [7] first pioneered the use of response surface methodology in tool life testing. Thomas et al. [8] used a full factorial design involving six factors to investigate the effects of cutting and tool parameters on the resulting surface roughness and on built-up edge formatting in the dry turning of carbon steel. Choudhury and El-Baradie [9] had used RSM and 23 factorial designs for predicting surface roughness when turning high-strength steel. The main objective of the current work was to develop RSM models for surface roughness based on cutting speed, axial depth of cut and feed for uncoated and coated inserts and then coupling GA with the developed RSM model to optimize the cutting conditions to search out the minimum surface roughness