Face Milling Research Articles

Digital dynamic modeling and topology optimized design of shell face mills

This paper presents digital modeling of shell face mills in milling cylinder heads. The cutter's structural dynamics and its mode shapes are predicted using a Finite Element system. The geometries of the cutter body and inserts are imported from their Computer Aided Design (CAD) models. The insert edge is discretized into small segments to model its varying normal rake and inclination angles, which affect the cutting mechanics. The cutter is dynamically assembled with the target machine tool spindle using the receptance coupling method. A general dynamic cutting force model, which considers the varying edge geometry and inserts’ run-outs, is developed and used to predict cutting forces and chatter stability diagrams. The proposed model is experimentally verified to demonstrate the feasibility of the systematic application of physics-based digital design and analysis of tools for the mass machining of specific parts. The cutter body shape is optimized to increase the stiffness of the bending mode shape that caused chatter via topology optimization, which led to five-fold increase in the absolute stable depth of cut.

Analyzing the correlation between tool vibration and flank wear in face milling of EN-31 steel employing the CRITIC approach

Analyzing the correlation between tool vibration and flank wear in face milling of EN-31 steel employing the CRITIC approach

Material Removal Rate of Face-Milled Bevel Gears Based on a Ring-Dexel Cutting Simulation

Abstract Face milling is not only a primary machining technique for the mass production of bevel gears, but it has also become a standardized process integrated into CNC bevel gear-cutting machines in the last two decades. Controlling suitable feed rates for face milling is one of the most direct and pivotal factors influencing processing efficiency for CNC machines. Despite being programmed through numerical codes, the feed rates provided by the gear machines most rely on experiential insights rather than optimization. Therefore, leveraging material removal rate (MRR) directly correlates with machining power and will hold immense potential for optimizing feed rates and enhancing efficiency. Because commercial software solutions cannot accurately predict the MRR for face milling operations, this paper uses a novel ring-dexel-based model for cutting simulation to address this issue. The main aim of this model is to provide a more precise prediction of the MRR across all face milling cuttings. By controlling the cutting depth and generating angle, the ring gear's plunging process and the pinion's single-roll generating process were successfully simulated. Thus, the MRRs through all cutting processes were calculated. Experimental results showed that tool torques are positively correlated with the MRRs. Finally, by appropriately increasing the cutting feed rate based on the MRR, the pinion and ring gear machining times were reduced by 44% and 18%, respectively.

Evaluating productivity characteristics of laser engineered net shaping titanium alloy

Advances in Additive Manufacturing (AM) technologies have made it possible to reduce the design and prototyping costs to a minimum especially for a low-productivity material like titanium. Titanium alloys are commonly and widely used alloys in the aerospace and biomedical sector due to their advantageous material properties. This paper is an evaluation study of factors affecting the productivity characteristics of Laser Engineered Net Shaping (LENS) titanium alloy (Ti-6Al-4 V) using face milling. Some of the productivity challenges associated with titanium such as rapid tool wear, poor surface finish, and high-power consumption are explored in this paper. All materials processed using AM face the same critical problem that the manufactured part requires a post machining since AM produces relatively poor surface finish. Machining trials are conducted using the combinations of machining parameters such as spindle speed of 800 and 1600 rev/min; feed rate of 50 and 100 mm/min; and a constant depth of cut of 1 mm, respectively. Titanium being a poor thermal conductivity material, the effect of coolant was investigated using wet/dry machining. Data related to the productivity factors and material behavior under a milling trial was recorded and analyzed. The obtained data from the trials include productivity factors such as Metal Removal Rate (MRR), power consumed, and the surface finish for each plate/trial. The power consumed in dry milling was observed to be lower than that in wet milling which is contrary to the observations from conventional wet milling. The paper concludes the trends observed for LENS titanium are opposed to the trends in conventional machining such as increasing cutting speed will result in lower cutting force and power consumed.

Influence of TiAlN coating thickness in dry machining of AISI 1045 steel using Finite Element simulation and experiments

High performance cutting can be accomplished using coated tools with optimal coating thickness. Therefore, Finite Element (FE) simulation and experimental investigation are employed to study the influence of thickness of TiAlN coating in the range of 1 to 4 μm during face milling of AISI 1045 steel under dry condition. FE simulation is successful in effectively investigating the influence of coating thickness in machining performance in terms of cutting forces, Von Mises stress, tool tip temperature and crater wear of uncoated as well as coated milling inserts. Simulation results have predicted cutting forces and temperature with maximum errors of 9.21 % and 12.20 %, respectively. FE simulation further demonstrates 2.100 and 1.602 μm as the depths of crater for 1 and 3 μm thick TiAlN coated inserts, respectively, which are also validated by the experiments. The experimental results demonstrate that 3 μm thick coated tool is superior in mitigating cutting forces, particularly under higher cutting speed (150 m/min), bringing down tool tip temperature (6.67 %) and machined surface roughness (18.18 %) as compared to 1 μm thick TiAlN coated milling insert. The results obtained from the current research work clearly indicate that the effect of coating thickness can be effectively investigated using the FE simulation before finalising the optimal coating thickness for the coating depositions and machining experiments.

Extension of the displacement method by measuring reference pieces using multiple sensors for on-machine surface profile measurement

On-machine measurement is crucial in industrial production because it allows the measurement of the surface properties of products without having to remove them from the machine tool; however, measurement results are superimposed with the motion error caused by the table traverse. Therefore, eliminating motion errors is this field's main objective. This paper discusses measuring a surface profile using an extension of the displacement method to eliminate motion errors. The displacement method can separate the surface profile and motion error of the stages in measurement results; however, it introduces a specific cumulative error. The tolerance of the accumulated error was first investigated by simulating a one-axis measurement. The displacement method was extended to a two-axis stage to measure the surface profile and correct the stage tilt to each axis. Moreover, carbon steel finished using a face mill was measured in the experiments, and a coordinate measuring machine was used to compare the results with those of the extended displacement method. It was found that the extended displacement method was able to remove the motion error. Applying the displacement method resulted in an improvement of approximately 88.9%. Error analyses were also formulated and evaluated for sensor drift, reference piece measurement, motion error, table rotation, and thermal expansion, and the percentage of these errors in the total was clarified. The results showed that the motion error and table rotation for the y-axis of the prototype experimental apparatus were larger than the others. These errors are discussed to analyze each error and listed in the error budget. The proposed extended displacement method can eliminate motion errors despite the simplicity of the measurement principle and can be easily integrated into machine tools and other tables.

Experimental investigation on performance of bubble-bursting atomization methods for minimum quantity lubrication in face milling tool steel

The metal cutting industry faces challenges in machining hard materials due to high forces and temperatures. This paper introduces bubble-bursting atomization minimum quantity lubrication (BBA-MQL), a novel MQL technique that generates fine biodegradable oil mists for cooling and lubrication. Although the initial results of BBA-MQL are promising, there has not been a comprehensive investigation into its use in machining hard materials, specifically tool steels. Therefore, this study focused on the applicability of BBA-MQL in face milling of AISI P20 + Ni tool steel in comparison with dry cutting and conventional MQL using commercial and vegetable oil. The machining tests were performed at three cutting speeds (50, 80, and 110 m/min), fixed cutting depth (0.2 mm), and feed rate (0.15 mm/tooth). The performance is evaluated by measuring the cutting force, surface quality, cutting temperature, and tool wear. It was found that BBA-MQL decreased cutting force and surface roughness considerably, with the average reductions being 23.2 % and 49.8 % compared with the conventional minimum quantity lubrication. The highest cutting speed of 110 m/min was preferred for achieving the lowest roughness value and cutting force when milling tool steel P20 + Ni. Furthermore, BBA-MQL with castor oil proved more effective compared to conventional MQL in reducing cutting force, showing improved surface finish, reduced cutting temperature, and delayed tool wear.

TOOL CONDITION MONITORING IN MACHINING USING ROBUST RESIDUAL NEURAL NETWORKS

Tool condition monitoring (TCM) aims to improve process efficiency, quality and tool maintenance costs by monitoring critical variables such as tool wear. This study proposes a deep learning (DL) architecture based on process-informed robust residual networks (Robust-ResNet) to predict tool wear in milling processes using time series of internal computer numerical control (CNC) signals. The Robust-ResNet architecture uses skip connections to move through multiple convolutional layers, avoiding the vanishing gradient problem of other neural network algorithms. The study includes an evaluation of the binding of process information as input to the architecture and an attention mechanism between skips to make more robust predictions. The proposed architecture has been trained and optimised using an open access data set of face milling time series. In this particular case, AC and DC signals have been used together with the corresponding tool wear values. The results of this study demonstrate the benefits of using deep learning techniques in the prediction of tool wear using internal signals provided by the CNC itself. The implementation of the proposed architecture is expected to help reduce maintenance costs, improve product quality and increase production efficiency in milling manufacturing processes.

QUALITY AND PRODUCTIVITY IMPROVEMENT IN THE MILLING OF POLYOXYMETHYLENE CO-POLYMER (POM C) USING RSM, SVM AND GENETIC ALGORITHM

This paper aims to conduct an economic and environmental machining procedure (dry condition) during face milling of the Polyoxymethylene Co-polymer. In this context, an experimental study based on Taguchi’s design was conducted to develop empirical models using Response Surface Methodology (RSM) on one hand and Support Vector Machine (SVM) for regression on the other hand. The ANalysis of VAriance (ANOVA) is conducted to determine the contribution and the significance of each cutting parameter on the surface quality and productivity (MRR). The obtained models are being compared to determine the most efficient approach. The last part is to find the optimum cutting combination by using Genetic Algorithm (GA) optimization based on SVM models, whether to minimize surface roughness Ra, or for composite objective to improve the quality and to increase the productivity. The results show that feed per tooth (fz) is the most affecting parameter on Ra followed by depth of cut (ap) and then the cutting velocity (Vc). SVM was more robust than RSM with less deviation error.

Experimentally Aided Operational Virtual Prototyping to Predict Best Clamping Conditions for Face Milling of Large-Size Structures

Vibrations occurring during milling operations are one of the main issues disturbing the pursuit of better efficiency of milling operations and product quality. Even in the case of a stable cutting process, vibration reduction is still an important goal. One of the possible solutions to obtain it is selection of the favorable conditions for clamping the workpiece to the machine table. In this paper, a method for predicting and selecting the clamping condition of a large-size workpiece for the reduction in vibrations during milling is presented. A modal test of the workpiece is performed first for a selected set of tightening screw settings. Next, one milling pass is performed to obtain reference data which are then used to tune the hybrid computational model. In the subsequent step, milling simulations are performed for a set of tightening variants, and the best one is selected, providing the lowest vibrations, assessed as the root mean square (RMS) of vibration displacements. In this paper, the description of the clamping selection procedure, key elements of the simulation model, and simulation and experimental results obtained for the milling of the test workpiece performed for a set of different clamping conditions are provided. The proposed method accurately predicts not only the best but also the worst clamping conditions.

Optimization of cutting parameters using response surface methodology (RSM) in milling Inconel 718 superalloy using Borax-added nanofluid in (MQL) system

PurposeThe need for materials with superior mechanical and physical properties has recently increased. Inconel 718, one of these superalloys, is frequently used in the aviation and space industry. However, during Inconel 718 superalloy machining, cutting tools and cutting fluid were excessively consumed. This study aims to investigate using an innovative and environmentally friendly cutting fluid in milling the Inconel 718 superalloy.Design/methodology/approachIn this study, a Borax- (BX-)added cutting nanofluid was prepared and used for the first time as a coolant in the minimum quantity lubrication (MQL) system of Inconel 718’s face milling process. Response surface methodology (RSM) was used to determine the effect of the BX element on cutting performance. Face milling operations were carried out by adding BX elements at 1.5% and 3% at two different rates.FindingsAs the BX additive ratio in the cutting fluid used in the MQL system increased, the cutting force values decreased. The lowest cutting force value was measured in the tests with cutting fluid containing 1.5% BX. In addition, a smoother surface was obtained by adding 1.5% BX to the cutting fluid. Furthermore, cutting tool life increased by 20% compared to 0% by 3% BX nanofluid concentration.Originality/valueThe study is innovative regarding the material processed, the cutting fluid used and the method used for the aerospace industry.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0191/

Modeling of milling forces in facing process of aluminum alloy AL7075 using the square inserts

Cutting forces play very important in designing the tool machine, cutting tool, and in optimization of machining processes. Modeling and prediction of cutting forces by theoretical methods are quite difficult, so, this study was focused on modeling the cutting force in face milling process using combination of theoretical and experimental methods. This study was performed to model the milling forces (MFs) and determine the milling force coefficients (MFCs) in the face milling process of aluminum alloy Al7075 using square inserts. From theoretical and experimental methods, the relationship of average milling forces (AMFs) and feed per flute (ft) were determined as the linear regression. Using experimental data, the linear regressions of AMFs and feed per flute were determined with high values of determination coefficients (larger than 95 %). MFCs were determined including shear and edge MFCs (tangential shear MFC (Ktc) of 538.127 N/mm2, radial shear MFC (Krc) of 185.967 N/mm2, axial shear MFC (Kac) of -691.297 N/mm2, tangential edge MFC (Kte) of 11.253 N/mm, radial edge MFC (Kre) of 6.991 N/mm, and axial edge MFC (Kae) of –32.971 N/mm. The MF models were successfully verified by comparing the measured and predicted MFs in face milling process of Al7075. The tendency and shape of predicted MFs were quite close to the measured ones. The differences between the predicted and the measured MFs can be due to the several reasons such as the influence of vibrations, the influence of cutting heat, etc., and these are also the limitations of this study. The modeling and prediction methods of this study can be used to model and predict the cutting forces in face milling of other milling types and other pairs of cutting tool and workpiece material as well

Analysis of Face Milling of Hard Steel 55NiCrMoV7 by Studying Rough and Semi-Finished Machining and the Influence of Cutting Parameters on Macroscopic Chip Dimensions.

Hard milling is being increasingly used as an alternative to EDM due to its high productivity. The present paper presents the results of theoretical-experimental research on the face milling of hard steel 55NiCrMoV7. A comprehensive analysis of cutting temperatures and forces during single-tooth milling and a morphological examination of the resulting chips are conducted for roughing and semi-finishing operations. The temperature is analyzed in the chip formation area, and the detached chips and the cutting force are analyzed through their tangential, radial, and penetration components, depending on the contact angle of the cutter tooth with the workpiece. The analysis of chip morphology is carried out based on the dimensional and angular parameters of chip segmentation and their degree of segmentation. Based on the central composite design and the response surface method, it is shown that it is possible to mathematically model the dependence of the macroscopic dimensions of the detached chips on the cutting parameters. The determined process functions, the maximum chip curling diameter, and the maximum chip height allow for establishing the influence of the cutting parameters' values on the chips' macroscopic dimensions and, thus, guiding the cutting process in the desired direction.

Численное моделирование процесса прерывистого резания с использованием ПО LS-Dyna

The results of numerical simulation of the stress-strain and thermal state in the cutting zone during face milling are presented. The simulation process was performed using LS-Dyna dynamic process research software

Development of the scientific and pedagogical school of the department “Ground Transport and Technological Complexes”

The scientific article reflects the main stages of the historical development of the scientific and pedagogical school of the cycle “Technology of structural metals. Materials Science”, which is one of the areas of the department “Ground transport and technological complexes”. Employees of the department made a significant contribution to solving practical problems in assessing the effectiveness of existing technological processes for restoring the tread surface profile of wheel pairs of wheels, the proposed processes of face milling and turning when restoring the wheel rim profile, including the use of preliminary induction annealing to improve machinability, optimization of cutting conditions wheels based on linear and nonlinear programming methods. A large amount of research and implementation work has been carried out to improve the repair technology and increase the reliability of the wheel-motor unit of rail crews of railway rolling stock. Issues related to surfacing operations during repairs and increasing the wear resistance of deposited surfaces were considered.At the same time, the teachers of the department conduct training sessions on disciplines at a high methodological level, without which it is impossible to imagine a modern engineer.Over the years of work of the cycle “Technology of Structural Metals. Materials Science” a large number of textbooks, teaching aids, guidelines and manuals on laboratory work and course design, as well as collections of scientific papers, articles and monographs on the main areas of research work were published.The historical digression presented in this article gives an idea of the scientific potential of the department accumulated over many years, which can serve as a reliable basis for further development of new scientific directions.

Surface integrity and modification of SA508Gr.3Cl.2 RPV steel induced by high-speed face milling process

Surface integrity and modification of SA508Gr.3Cl.2 RPV steel induced by high-speed face milling process

Prediction of surface roughness in duplex stainless steel face milling using artificial neural network

Prediction of surface roughness in duplex stainless steel face milling using artificial neural network

On-machine detection of face milling cutter damage based on machine vision

On-machine detection of face milling cutter damage based on machine vision

Cryogenic and conventional milling of AZ91 magnesium alloy

Use of magnesium is the need of the hour due to its low density as well as its high strength-to-weight and stiffness-to-weight ratio etc. This study focuses on the effectiveness of liquid nitrogen (LN2) assisted cryogenic machining on the surface integrity (SI) characteristics of AZ91 magnesium alloy. Face milling using uncoated carbide inserts have been performed under liquid nitrogen (LN2) assisted cryogenic condition and compared with conventional (dry) milling. Experiments are performed using machining parameters in terms of cutting speeds of 325, 475, 625 m/min, feed rates of 0.05, 0.1, 0.15 mm/teeth and depth of cuts of 0.5, 1, 1.5 mm respectively. Most significant surface integrity characteristics such as surface roughness, microhardness, microstructure, and residual stresses have been investigated. Behaviour of SI characteristics with respect to milling parameters have been identified using statistical technique such as ANOVA and signal-to-noise (S/N) ratio plots. Additionally, the multi criteria decision making (MCDM) techniques such as additive ratio assessment method (ARAS) and complex proportional assessment (COPRAS) have been utilized to identify the optimal conditions for milling AZ91 magnesium alloy under both dry and cryogenic conditions. Use of LN2 during machining, resulted in reduction in machining temperature by upto 29% with a temperature drop from 251.2 °C under dry condition to 178.5 °C in cryogenic condition. Results showed the advantage of performing cryogenic milling in improving the surface integrity to a significant extent. Cryogenic machining considerably minimized the roughness by upto 28% and maximised the microhardness by upto 23%, when compared to dry machining. Cutting speed has caused significant impact on surface roughness (95.33% – dry, 92.92% – cryogenic) and surface microhardness (80.33% – dry, 82.15% – cryogenic). Due to the reduction in machining temperature, cryogenic condition resulted in compressive residual stresses (maximum σ║ = -113 MPa) on the alloy surface. Results indicate no harm to alloy microstructure in both conditions, with no alterations to grain integrity and minimal reduction in the average grain sizes in the near machined area, when compared to before machined (base material) surface. The MCDM approach namely ARAS and COPRAS resulted in identical results, with the optimal condition being cutting speed of 625 m/min, a feed rate of 0.05 mm/teeth, and a depth of cut of 0.5 mm for both dry and cryogenic environments.

Finite Element Analysis of Plastic Deformation in Ultrasonic Vibration Superimposed Face Milling of Steel X46Cr13.

Ultrasonic vibration superimposed face milling enables the generation of predefined surface microstructures by an appropriate setting of the process parameters. The geometrical reproducibility of the surface characteristics depends strongly on the plastic material deformation. Thus, the precise prediction of the emerging surface microstructures using kinematic simulation models is limited, because they ignore the influence of material flow. Consequently, the effects of plastic as well as elastic deformation are investigated in depth by finite element analysis. Microstructured surfaces resulting from these numerical models are characterized quantitatively by areal surface parameters and compared to those from a kinematical simulation and a real machined surface. A high degree of conformity between the values of the simulated surfaces and the measured values is achieved, particularly with regard to material distribution. Deficits in predictability exist primarily due to deviations in plastic deformation. Future research can address this, either by implementing a temperature consideration or adapting specific modeling aspects like an adjusted depth of cut or experimental validated material parameters.