End Milling Process Research Articles

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission.

This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

Numerical analysis of the effect of air pressure and oil flow rate on droplet size and tool temperature in MQL machining

Dry machining is undesirable for machining of good surface quality products, because the thermal properties of a material may contribute to a reduction of tool life when machining difficult to cut materials. Likewise, the conventional lubrication through a jet or flood cooling is not economical or eco-friendly and increases the environmental burden. Owing to the above facts it becomes necessary to implement Minimum Quantity Lubrication (MQL) in end milling process. In this study, a numerical analysis has been carried out to investigate the impact of compressed air pressure and the oil flow rate on Sauter mean diameter of MQL spray and on tool temperature. Sauter Mean Diameter (SMD) of MQL spray and temperature distribution in the end milling tool are calculated for three different air pressure (2, 3, and 4 bar) and oil flow rate (100, 150, and 200 ml/hr). ANSYS Fluent software is used for the analysis with discrete phase model (DPM) for predicting the spray particle size. From the analysis, SMD and tool temperature are found to decrease with increase in air pressure for constant oil flow rate whereas there is no considerable change in SMD as the flow rate of oil is changed beyond 150 ml/h. This study has been used for selecting the optimum air pressure and flow rate of the oil for efficient lubrication and cooling in MQL aided machining.

Influence of radial rake angle and cutting conditions on friction during end milling of Nimonic 263

In machining processes, the friction and frictional stress formation along the interface between the chip and the tool rake face give rise to energy consumption, tool wear, and failure. This study aims to investigate the influence of tool nomenclature (radial rake angle) and cutting conditions (spindle speed and table feed velocity) on the impediment of friction and frictional stress in the perspective of equivalent pressure coefficients during end milling process, and additionally, the comparative study has been made. The experimental outcome clearly indicates that the machining parameter and tool nomenclature have significant effects on frictional stress impediment under pure sliding condition. Among these, tool nomenclature has higher and significant beneficial impact on the obstruction of frictional stress formation, and it is also stated that the cutting force and friction decreases, when the radial rake angle of cutter decreases when the helix angle and the positive axial rake angle is constant.

Prediction of Surface Roughness Based on Cutting Parameters and Machining Vibration in End Milling Using Regression Method and Artificial Neural Network

This study presents surface roughness modeling for machined parts based on cutting parameters (spindle speed, cutting depth, and feed rate) and machining vibration in the end milling process. Prediction models were developed using multiple regression analysis and an artificial neural network (ANN) modeling approach. To reduce the effect of chatter, machining tests were conducted under varying cutting parameters as defined in the stable regions of the milling tool. The surface roughness and machining vibration level are modeled with nonlinear quadratic forms based on the cutting parameters and their interactions through multiple regression analysis methods, respectively. Analysis of variance was employed to determine the significance of cutting parameters on surface roughness. The results show that the combined effects of spindle speed and cutting depth significantly influence surface roughness. The comparison between the prediction performance of the multiple regression and neural network-based models reveal that the ANN models achieve higher prediction accuracy for all training data with R = 0.96 and root mean square error (RMSE) = 3.0% compared with regression models with R = 0.82 and RMSE = 7.57%. Independent machining tests were conducted to validate the predictive models; the results conclude that the ANN model based on cutting parameters with machining vibration has a higher average prediction accuracy (93.14%) than those of models with three cutting parameters. Finally, the feasibility of the predictive model as the base to develop an online surface roughness recognition system has been successfully demonstrated based on contour surface milling test. This study reveals that the predictive models derived on the cutting conditions with consideration of machining stability can ensure the prediction accuracy for application in milling process.

CNC End Milling Process Parameters Optimization on Aluminium Alloy by Using Taguchi and ANOVA Methods

This paper is focused on the experimental research which is carried out on the machining of the aluminum alloy. Its main objective is to find the optimum cutting parameters used in the end-milling process. An experimental plan was established based on the L27 orthogonal matrix. The experiments were performed using the recommended values for the cutting parameters by the cutting tools manufacturer on the aluminum machining. The design of experiment methods that were used, are the Taguchi method, the variance analysis and the regression analysis. The controllable factors were the cutting speed, the cutting depth and the feed per tooth. The surface roughness represents the objective function and this was obtained by the studied cutting process. The surface roughness was measured longitudinally and transversely in the cutting direction.

Soft Computing Techniques for the Prediction of Hybrid Composites

Soft computing techniques such as Artificial Neural Networks and Fuzzy logic are widely used in application of manufacturing technology. Surface roughness plays a vital role for quality of the product using machining parameters. Soft computing techniques are applied to predict the surface roughness in an economical manner. In this paper, prediction of surface roughness is evaluated using ANFIS [Adaptive Neuro-Fuzzy Inference System] methodology for the cutting parameters of end-milling process for machining the halloysite nanotubes (HNTs) with aluminium reinforced epoxy hybrid composite material. Experimental datas are used to analyse the relationship between the input parameter such as depth of cut (d), cutting speed (S), feed-rate (f) and output parameters as surface roughness. Datas are classified into training and testing with different types of membership functions. The observed results accurately predict the output which was not used in training and it is almost very close to the actual output obtained in the experimental work. Moreover it was found that gbellmf is helpful for better prediction with minimum error.

ANALYSIS OF SURFACE ROUGHNESS MILLED OF STEEL AISI 1045 USING X-BAR AND R CONTROL CHART

The steel milling AISI 1045 has been gaining prominence in industry in recent years as it allows machined parts to be obtained with low-cost inserts. However, to ensure the final product quality, it is important that the milling for machining procedure be well planned in order to the cutters have their wear minimized in the process, as well as a considerable productivity rate with a zero occurrence of reworked parts or scraps. Thus, this paper presents a study about the quality of the machined surface on the end milling process of AISI 1045 steel using titanium nitride (TiN) coated carbide inserts, optimized for a combined design, using Design of Experiments (DOE). Statistical Process Control (SPC) is applied to analyze the process variations using X-bar and R control charts. The objective of this study is to identify the optimal combination of the input setup such as cutting speed (Vc), feed per tooth (fZ), work penetration (ae) and machining depth (ap) that is capable of minimize the process variation. The response measured is the roughness parameter Ra, observed under the influence of cutting fluid, tool wear, concentration and flow of the cutting fluid as noise. The obtained result was the stability of the Ra roughness for the AISI 1045 steel in end milling process, which is not influenced by noise variables due to Robust Parameter Design used in this study

Optimal design of spindle-tool system for improving the dynamic stability in end-milling process

This paper presents an optimum design approach of a spindle-tool system to improvise the dynamic stability of end-milling. Initially, the tool-tip frequency response function is obtained by analyzing the spindle-tool assembly with the finite element model. The chatter-free regions are maximized using an optimization methodology by considering the spindle and tool parameters as design variables. A simulated experimental dynamic data consisting of the average stable depth of cut is obtained for different combinations of design variables by the method of design of experiments (DOE) and analysis of variance technique is applied to find the influence of design parameters on the outputs. Based on the obtained results, the data is generalized with the help of a neural network model that works as an estimator of the average stable depths for the optimization module. The average stable depth of cut over a range of operating speeds is maximized by selecting optimal tool and spindle parameters.

Residual Stresses Modelling of End Milling Process Using Numerical and Experimental Methods

Machining has been one of the most sort of process for realizing different products. It has significant role in the value additions process. Machining is one of the production process where material is removed from the parent material to realize the final part or component. Among machining, the well known machining processes are turning, milling, shaping, grinding and non-conventional machining processes like electric discharge machining, ultrasonic machining, chemical machining etc. The fundamental of all these processes being material removal in the form of chips using a tool either in contact or not in contact. In the present work, milling is being taken for study Finite element analysis is being used as a tool to understand the different phenomenon that underlies the machining processes. Of late, the machining induced residual stresses is of great interest to the researchers since the residual stresses have an impact on the functional performances. The present work is to model the milling process to predict the forces and residual stresses using finite element method. Unlike many researchers, the authors have attempted to develop oblique cutting model rather than an orthogonal cutting model. The present work was carried out on AISI 1045 steel.

A hybrid approach of NSGA-II and TOPSIS for minimising vibration and surface roughness in machining process

Increasing vibration amplitude during end milling process can seriously affect the life of end mills and reduces surface finish. Spindle and worktable vibration has a significant influence on surface quality of machined components. This paper confronts and investigates the effect of machining and geometrical parameters (spindle speed, feed rate, axial depth of cut, radial depth of cut and radial rake angle) on spindle and worktable vibration in terms of acceleration amplitude and surface roughness. Experiments were conducted on aluminium alloy 6061-T6 with high-speed steel (HSS) end mill cutter based on the central composite design (CCD). Response surface methodology (RSM) was used to develop the predictive models and the adequacy of the models were verified using analysis of variance (ANOVA). Non-dominated sorting of genetic algorithm (NSGA-II) was adopted to solve the multi objective optimisation problem and the optimised results were resulted with a set of Pareto-optimal solutions. The multi criteria decision making method (MCDM) such as technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) were designed to rank the Pareto optimal solutions based on response of closeness coefficient values.

A Hybrid Approach of NSGA-II and TOPSIS for Minimizing Vibration and Surface Roughness in Machining process

Increasing vibration amplitude during end milling process can seriously affect the life of end mills and reduces surface finish. Spindle and worktable vibration has a significant influence on surface quality of machined components. This paper confronts and investigates the effect of machining and geometrical parameters (spindle speed, feed rate, axial depth of cut, radial depth of cut and radial rake angle) on spindle and worktable vibration in terms of acceleration amplitude and surface roughness. Experiments were conducted on aluminium alloy 6061-T6 with high-speed steel (HSS) end mill cutter based on the central composite design (CCD). Response surface methodology (RSM) was used to develop the predictive models and the adequacy of the models were verified using analysis of variance (ANOVA). Non-dominated sorting of genetic algorithm (NSGA-II) was adopted to solve the multi objective optimisation problem and the optimised results were resulted with a set of Pareto-optimal solutions. The multi criteria decision making method (MCDM) such as technique for order preference by similarity to ideal solution (TOPSIS) and analytical hierarchy process (AHP) were designed to rank the Pareto optimal solutions based on response of closeness coefficient values.

Effect of Process Variables using Square Type Cutting Tool on Surface Roughness and Residual Stress of EN24 in an End-Milling Process

Effect of Process Variables using Square Type Cutting Tool on Surface Roughness and Residual Stress of EN24 in an End-Milling Process

Optimization of process parameters in CNC milling of P20 steel by cryo-treated tungsten carbide tools using NSGA-II

ABSTRACT This article proposes an extensive experimental and microstructural analysis to study the consequences of deep cryogenic treatment (DCT) soaking duration on tungsten carbide end mill cutter on the machinability of P20 mold steel. A Box-Behnken design of response surface methodology (RSM) is utilized to collect data for the study. Cutting speed, feed rate, depth of cut and milling cutters subjected to various soaking durations are taken as important process variables which are a function of performance measures viz. tool wear rate (TWR), material removal rate (MRR) and surface roughness (R a). It is observed that cutting speed, feed, depth of cut and cryogenic treatment exhibit a considerable effect on performance measures. In the present study, NSGA-II multi-objective optimization technique is used to obtain the optimal process parameters of end milling process to enhance the productivity of the process. For this purpose, the developed RSM model is coupled with NSGA-II in MATLAB.

Effect of milling parameters on surface roughness: An experimental investigation

Milling is the very well-known machining technique; it is widely adopted in industries in the current age. The machining of Aluminum Alloy AA6062 based on a high-speed milling machine is very essential because of its applications in various industries i.e. Automobile, Shipbuilding etc. This paper deals with three main process parameters which affect the end milling process these are Feed rate, Depth of cut and spindle speed which influence the surface roughness of Aluminum Alloy AA6062 during end-milling operation. Different levels of surface roughness are measured through the Stylus profile meter of a surface roughness measuring machine Surftest SJ-210.

Surface finish improvement using a damping-alloy sleeve-insert tool holder in the end milling process

This study investigates the effect of a tool holder with a metallic damping alloy on both vibration reduction and surface finish improvement for use in an end-milling process. Two different configurations of damping alloys are designed: a collet and a sleeve inside a conventional tool holder. To determine the system characteristics including damping effects, an experimental verification was organized, after that actual machining tests are conducted. The damping effects of each tool holder are compared through machining tests on carbon-fiber reinforced plastics (CFRP) and mold steel respectively. Additionally, a chatter vibration suppression test using thin aluminum workpiece concludes that the metallic damping alloy is comparable to other damping methods. The results show that damping alloy can reduce machining vibration regardless of workpiece materials or the form of it, and the sleeve type tool holder improves the surface roughness significantly in an end-milling process for both workpiece materials.

Novel servo-feed-drive model considering cutting force and structural effects in milling to predict servo dynamic behaviors

This paper presents an integrated servo-feed-drive model including the cutting force and structural effect to predict tracking errors in an end-milling process. Most conventional approaches consider the cutting force to be an equivalent torque to servo feed drive. However, in addition to acting as the equivalent torque to the servo feed drive, cutting forces also cause the machine table to vibrate. This paper considers the aforementioned cutting-force effects to predict tracking errors and then verifies the tracking errors using experimental results. Experiments are conducted on a 3-axis computer numerical control (CNC) machining center to validate the tracking errors predicted by the proposed servo-feed-drive model. For one case study, the peak-to-peak tracking errors from the experimental, proposed, and traditional models are 3 μm, 2.8 μm, and 0.5 μm, respectively, for the x-axis, and 2.1 μm, 1.7 μm, and 0.4 μm, respectively, for the y-axis. The experimental results illustrate that the tracking errors predicted using the proposed model are more accurate than those predicted using the traditional model without consideration of the transmission path. Therefore, it can be concluded that the proposed integrated model provides much accurate tracking-error prediction, and thus, the ball-screw and machine-table flexibilities should be considered.

Prediction and control of surface roughness for end milling process using ANFIS

In this paper, we applied an Adaptive Neuro-Fuzzy Inference System (ANFIS) approach for prediction of the workpiece surface roughness for the end milling process. A small number of fuzzy rules were used for building ANFIS models with the help of Subtractive clustering method (ANFIS-Subtractive clustering model). The predicted values are found to be in excellent agreement with the experimental data with average error values in the range of 3.47-3.49%. Also, we compared the proposed ANFIS models to other Artificial intelligence (AI) approaches. Results show that the proposed model has high accuracy in comparison to other AI approaches in literature. Therefore, we can use ANFIS model to predict the workpiece surface roughness for the end milling process.

Effect of Machining Parameter and Multi-Response Optimization during End-Milling in GFRP-VARTM Composites using RSM Based Response Surface Regression Method

This paper is going to find out the correlation between cutting parameters and responses for GFRP composite material during end milling using the WC-CO tool. An experimental plan, based on Response Surface Methodology (RSM) techniques according to the Response Surface Regression (RSR), made up of Vaccum-Assisted Resin Transfer molding using solid carbide end mills. The objective of this paper is to investigate the machining parameters on glass fiber reinforcement polymer during the end milling process of a WC-Co cutting tool grade.

Numerical and experimental study of end-milling process of titanium alloy with a cryogenic internal coolant supply

Cryogenic machining is an environmentally friendly process; liquid nitrogen (LN2) is sprayed onto cutting tool to reduce cutting temperature, increasing tool life. Cutting temperature and force were numerically predicted during cryogenic assisted milling with an internal coolant-assisted tool holder (internal cryogenic milling) for Ti-6Al-4V alloy. The influence of LN2 on the material temperature throughout the machining was estimated; a numerical model to simulate the initial temperature of work material was discussed by consideration of LN2 injective mechanism. A modified Johnson-Cook model including the cryogenic temperature range was adopted to model material plasticity. The predictive models were validated based on side-milling test. The predicted values captured the trend of experimental result; the minimum and maximum temperature errors were 0.1% and 8.6%, and those for the cutting force were 0.2% and 34.4%. Moreover, comprehensive experimental studies for the cutting temperature, cutting force, chip morphology, and chip composition were performed to understand the effect of cryogenic cooling condition. In internal cryogenic milling, the cutting temperature and force tended to be lower than dry machining. Based on the morphological analysis of the generated chip, the coefficient of sliding friction at tool-chip interface under the internal cooling was reduced by 21.4% as compared to the dry condition.

Chatter Identification in End Milling Process Based on Cutting Force Signal Processing

During milling process, chatter is one of the major limitations of productivity, it has more negative effects on the surface finish, dimensional accuracy and tool life. It is thus necessary to detect and avoid chatter occurrence. In this study, a chatter detection approach based on cutting force signal is presented. The milling force signals were measured by cutting force sensor and then analyzed by using time-frequency analysis approaches such as short-time Fourier transform (STFT) and continuous wavelet transform (CWT) in order to identify chatter. The different sets of cutting conditions were utilized to recognize chatter presence. The spectrogram and scalogram results of the force signals represented a great difference in frequency content between stable and unstable cuttings. The analysis results were then validated by cutting force behavior in the time domain.