Face Milling Process Research Articles

Optimization of Process Parameters on CNC milling machine for Mild steel IS 2062:2011 E250 Gr. A with AlTiN coated tool insert in wet condition

Now a days manufacturing industries wants to increase the quality of product and reduce manufacturing cost. The aim of this project work is to reduction of energy consumption during the machining phase, reduce surface roughness and increase material removal rate by optimization of face milling process parameters of mild steed IS 2062:2011 E250 Gr A with Taguchi orthogonal array. 27 experimental runs based on L27 orthogonal array of Taguchi method were performed. Spindle speed, feed rate and depth of cut this process parameters are optimized with multiple performance characteristics such as surface roughness (Ra), Material removal rate (MRR) and power consumption. The machining operation performed on VMC by using coolant with 63 mm cutter diameter. The analysis of variance (ANOVA) is also applied to identify the most significant factor using Minitab software. The analysis can be done on AlTiN insert coating. At the last conformation test were performed.

Analytical Prediction and Experimental Investigation of Burnishing Force in Rotary Ultrasonic Roller Burnishing Titanium Alloy Ti–6Al–4V

Abstract Ultrasonic burnishing is usually applied to make machined surface modification. The acoustic softening effect caused by ultrasonic vibration is beneficial to the machining of difficult-to-cut materials. In the present work, a burnishing force prediction model was proposed for rotary ultrasonic burnishing of titanium alloy Ti–6Al–4V, whose surface had been machined with the face milling process. Firstly, the contact between the burnishing roller and one single milling mark was analyzed with plane strain assumption based on the Boussinesq–Flamant contact problem. Then, the effect of ultrasonic softening on the yield stress of Ti–6Al–4V was investigated. The critical contact width and contact load that the burnishing roller crushed on one single milling mark were examined to confirm the feasibility of the proposed ultrasonic burnishing force prediction model. The experimental verifications were carried out at various ultrasonic powers. The burnishing forces from experiment measurements were consistent with the calculated results from the proposed model. The mean deviations between theoretical and experimental results of the ultrasonic burnishing force were 10.4%, 12.2%, and 15.2%, corresponding to the ultrasonic power at the level of 41 W, 158 W, and 354 W, respectively.

FPGA Based Real Time Simulations of the Face Milling Process

The article presents a successful implementation of the milling process simulation at the Field-Programmable Gate Array (FPGA). By using FPGA, very rigorous Real-Time (RT) simulation requirements can be met. The response time of the FPGA simulations is significantly reduced, and the time synchronization is better than in a typical RT system implemented in software. The FPGA-based approach is characterized by enormous flexibility when it comes to input and output operations that can be implemented deterministically in RT. Complex simulation software has been implemented using the High Level Synthesis technique, which is a relatively easy and fast approach for FPGA programming without using complex Hardware Description Languages. The hardware functions are based on procedures written in high-level C programming language. The mathematical descriptions of simulations, results of computer simulations, Hardware-in-the-Loop Simulation experiments, and real experiments are presented. The approach presented in this paper can be used to simulate the dynamics of various mechatronic systems.

Optimization of CNC Face Milling Process Parameters using Response Surface Methodology and Particle Swarm Optimization Algorithm

Optimization of CNC Face Milling Process Parameters using Response Surface Methodology and Particle Swarm Optimization Algorithm

Multi-Objective Process Parameter Optimization for Surface Roughness and Material Removal Rate in Face Milling of Al 6061 T6 Alloy

This study uses Taguchi methodology and Gray Relational Analysis approach to explore the optimization of face milling process parameters for Al 6061 T6 alloy.Surface Roughness (Ra), Material Removal Rate (MRR) has been identified as the objective of performance and productivity.The tests were performed by selecting cutting speed (mm / min), feed rate (mm / rev) and cutting depth (mm) at three settings on the basis of Taguchi's L9 orthogonal series.The grey relational approach was being used to establish a multiobjective relationship between both the parameters of machining and the characteristics of results. To find the optimum values of parameters in the milling operation, the response list and plots are used and found to be Vc2-f1-d3. To order to justify the optimum results, the confirmation tests are performed.The machining process parameters for milling were thus optimized in this research to achieve the combined goals such as low surface roughness and high material removal rate on Aluminum 6061 t6.It was concluded that depth of cut is the most influencing parameter followed by feed rate and cutting velocity.

Mathematical Modeling and Optimization of Milling Parameters in AA 5083 Aluminum Alloy

In this study, the selection of optimal cutting parameters for face milling of 5083 aluminum was investigated in order to minimize the surface roughness. Effect of selected parameters on the surface roughness was analyzed by using analysis of variance (ANOVA). The mathematical model was developed to estimate surface roughness in face milling process by using Response Surface Methodology (RSM). Feed, spindle speed and depth of cut were selected as input variables. The statistical analysis indicated that feed and spindle speed have the most considerable influence on surface roughness. After developed mathematical model, Desirability Function Analysis (DFA) was performed to optimize the cutting parameters. The lowest value of surface roughness (0.41 µm) was acquired at a feed of 3008 mm/min, a spindle speed of 5981 rpm and a depth of cut of 0.54 mm.

Methods of comparison of surface texture based on fractal dimension and Hotelling’s T2 test

Comparative analysis of the surface texture of machine parts can be successfully carried out using statistical tests. The paper presents a methodology of method used to compare the surface texture by applying Hotelling’s T2 test as well as a method used to evaluate surface topography by applying fractal dimension. The tests were carried out on samples produced with the use of face milling process for four types of materials. The following types of steel were used: 40HM, C45, NC6 and WCL. For each type of material, four areas were machined with the same machining parameters. Based on these results a decision was made whether the surfaces, despite the same machining conditions, were significantly different from each other. Furthermore, the analysis indicated that the fractal dimension enabled to characterise signal irregularities in quantitative and qualitative way.

OPTIMIZATION OF MULTI-PASS FACE MILLING PARAMETERS USING METAHEURISTIC ALGORITHMS

In this paper, six metaheuristic algorithms, in the form of artificial bee colony optimization, ant colony optimization, particle swarm optimization, differential evolution, firefly algorithm and teaching-learning-based optimization techniques are applied for parametric optimization of a multi-pass face milling process. Using those algorithms, the optimal values of cutting speed, feed rate and depth of cut for both roughing and finishing operations are determined for having minimum total production time and total production cost. It is observed that the teaching-learning-based optimization algorithm outperforms the others with respect to accuracy and consistency of the derived solutions as well as computational speed. Two statistical tests, i.e. paired t-test and Wilcoxson signed rank test also confirm its superiority over the remaining algorithms. Finally, these metaheuristics are employed for multi-objective optimization of the considered multi-pass milling process while concurrently minimizing both the objectives.

Automatic Identification of Tool Wear Based on Convolutional Neural Network in Face Milling Process.

Monitoring of tool wear in machining process has found its importance to predict tool life, reduce equipment downtime, and tool costs. Traditional visual methods require expert experience and human resources to obtain accurate tool wear information. With the development of charge-coupled device (CCD) image sensor and the deep learning algorithms, it has become possible to use the convolutional neural network (CNN) model to automatically identify the wear types of high-temperature alloy tools in the face milling process. In this paper, the CNN model is developed based on our image dataset. The convolutional automatic encoder (CAE) is used to pre-train the network model, and the model parameters are fine-tuned by back propagation (BP) algorithm combined with stochastic gradient descent (SGD) algorithm. The established ToolWearnet network model has the function of identifying the tool wear types. The experimental results show that the average recognition precision rate of the model can reach 96.20%. At the same time, the automatic detection algorithm of tool wear value is improved by combining the identified tool wear types. In order to verify the feasibility of the method, an experimental system is built on the machine tool. By matching the frame rate of the industrial camera and the machine tool spindle speed, the wear image information of all the inserts can be obtained in the machining gap. The automatic detection method of tool wear value is compared with the result of manual detection by high precision digital optical microscope, the mean absolute percentage error is 4.76%, which effectively verifies the effectiveness and practicality of the method.

Dynamics Modeling-Based Optimization of Process Parameters in Face Milling of Workpieces With Discontinuous Surfaces

AbstractFace milling is widely used in machining processes, aimed at providing workpieces with high surface quality. The chatter generated in face milling could lead to tremendous damage to machine tools, poor machined surface quality, and loss of processing efficiency. Most related researches have been focused on the modeling of spindle dynamics and discretization algorithms for chatter prediction. However, few published articles have taken the geometric characteristics of workpieces into consideration, especially for workpieces with discontinuous surfaces in face milling, which leads to poor accuracy of chatter prediction as well as the waste of processing efficiency. To overcome this shortage, a novel dynamic model for the face milling process is built in this paper, considering the cutting insert engagement based on the geometric characteristics of the workpieces and the tool path. The stability lobe diagrams (SLDs) applicable to workpieces with discontinuous surfaces are constructed. A process parameter optimization model is developed to maximize the chatter-free processing efficiency of the face milling process. The sensitivity analysis is utilized to simplify the objective function, and the genetic algorithm is employed to solve the optimization model. The proposed approach is validated by an experimental case study of an engine block, improving the chatter-free material removal rate by 53.3% in comparison to the classic approach.

The Investigation of the Influence of Modern Coating Applied to the Cutting Inserts During Machining

This article deals with benefits of PVD and CVD coatings applied to cutting inserts during face milling technology. The coatings deposited on applied cutting inserts were used to extension the tool life. Machining process was carried out on the conventional vertical milling machine tool. This machining process was performed without coolant. The set up of cutting conditions were constant throughout the machining and low-alloy Cr-Mo steel DIN 42CrMoS4 (W. Nr. 1.7227) was used as workpiece material in the realized process of experiments. The aim of this investigation was to compare coated and uncoated changeable cutting inserts clamped in the milling cutter and find out the benefits of PVD or CVD coatings during realization of face milling process. The monitored parameters were the force load measured by piezoeletrical dynamometer Kistler 9257B and the obtained flank wear measured by optical microscope.

Stress strain analysis for the face milling cutter to achieve a proper surface finish

According to an analysis of published works by the authors who investigated the process finishing machining operations the parts by the face milling cutter, the feasibility of using face milling processes for the final formation the surface layer of the parts to be machined is established. In doing so, using face milling of oblique cutting with equipped with super hard materials and a spiral-step arrangement of the cutting elements have significant advantages. However, the non-stationarity cutting process, which characteristic for the face milling processes, leads to variable deformations of the technological system, which negatively characterize the quality processing affects. The study of the authors of the stress-strain state (for one of the technological system components) allowed determining of the cutter body deformation process, which leads to a change position of the cutting edges, and that in turn to affect the machined surface. The measured values of axial displacement the cutters allow predicting the machined surfaces quality and to develop measures for tool deformations eliminated in the non-stationarity face milling condition process.

Multi-objective optimization in face milling process with cryogenic cooling using grey fuzzy analysis and BPNN-GA methods

Purpose The purpose of this study is to investigate the prediction and optimization of multiple performance characteristics in the face milling process of tool steel ASSAB XW-42. Design/methodology/approach The face milling parameters (cutting speed, feed rate and axial depth of cut) and flow rate (FR) of cryogenic cooling were optimized with consideration of multiple performance characteristics, i.e. surface roughness (SR), cutting force (Fc) and metal removal rate (MRR). FR of cryogenic cooling has two levels, whereas the three face milling parameters each have three levels. Using Taguchi method, an L18 mixed-orthogonal array was selected as the design of experiments. The rough estimation of the optimum face milling parameters was determined by using grey fuzzy analysis. The global optimum face milling parameters were searched by applying the backpropagation neural network-based genetic algorithm (BPNN-GA) method. Findings The optimum SR, cutting force (Fc) and MRR could be obtained by setting FR, cutting speed, feed rate and axial depth of cut at 0.5 l/min, 280 m/min, 90 mm/min and 0.2 mm, respectively. The experimental confirmation results showed that BPNN-based GA optimization method could accurately predict and significantly improve all of the multiple performance characteristics. Originality/value To the best of the authors’ knowledge, there were no publications available regarding multi-response optimization using the combination of grey fuzzy analysis and BPNN-based GA methods during cryogenically face milling process.

Numerical Modeling and Multi Objective Optimization of Face Milling of AISI 304 Steel

There is a requirement to find accurate parameters to accomplish precise dimensional accuracy, excellent surface integrity and maximum MRR. This work studies the influence of various cutting parameters on output parameters like Cutting force, Surface roughness, Flatness, and Material removal rate while face milling. A detailed finite element model was developed to simulate the face milling process. The material constitutive behavior is described by Johnson-Cook material model and the damage criteria is established by Johnson-Cook damage model. The result indicate significant effects of all three cutting parameters on MRR and both feed rate and depth of cut have significant effect on cutting force. Also, feed rate has significant effect on PEEQ and none of the parameters have effect on flatness.

Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time

Abstract Cutting tool and cutting parameters are important components in the process planning. Proper selection of cutting tool and cutting parameters can significantly reduce the energy consumption and production time of the machining process. In this paper, an integrated approach of cutting tool and cutting parameter optimization is proposed to minimize the energy footprint and production time of the face milling process. Firstly, the energy footprint characteristics are analyzed by considering multiple cutting tool flexibilities and cutting parameters. Then a multi-objective integrated optimization model for minimizing energy footprint and production time is proposed and solved via a multi-objective Cuckoo Search algorithm. Finally, case studies are conducted to verify the feasibility and validity of the proposed integrated optimization approach. From the results of the case studies, interaction effects between cutting tool and cutting parameters are revealed. Integrated optimization of cutting tool and cutting parameters can achieve more energy footprint savings than either cutting parameter optimization or cutting tool optimization. Moreover, it also can be found that the optimization results for minimum production time does not necessarily satisfy the optimization criterion of minimum energy footprint. The proposed multi-objective integrated optimization approach can strike a balance between minimum energy footprint and minimum production time.

Identification of the minimum chip thickness based on the two-dimensional wavelet analysis

In this paper, authors take attempt to adapt the two-dimensional wavelet transform to assess the value of the minimum chip thickness for the face milling process of C45 steel. The images of surface layer from selected measuring devices were analyzed using a mother wavelet bior6.8. The analysis based on the wavelet transform revealed in signals three characteristics zones of the impact of cutting insert on the workpiece during initiation of the cutting process. The obtained values allowed to determine the relative method accuracy and to evaluate the possibility of application the particular measuring machines to assess the value of hmin parameter.

A Coupled Model for the Prediction of Surface Variation in Face Milling Large-Scale Workpiece With Complex Geometry

Face milling commonly generates surface quality of variation, is especially severe for milling of large-scale components with complex surface geometry such as cylinder block, engine head, and valve body. Thus surface variation serves as an important indicator both for machining parameter selection and components' service performance such as sealing, energy consumption, and emission. An efficient and comprehensive numerical model is highly desired for the prediction of surface variation of entire surface. This study proposes a coupled numerical simulation method, updating finite element (FE) model iteratively based on integration of data from abaqus and matlab, to predict surface variation induced by face milling of large-scale components with complex surfaces. Using the coupled model, three-dimensional (3D) variation of large-scale surface can be successfully simulated by considering face milling process including dynamic milling force, spiral curve of milling trajectory, and intermittently rotating contact characteristics. Surface variation is finally represented with point cloud from iterative FE analysis and verified by face milling experiment. Comparison between measured and predicted results shows that the new prediction method can simulate surface variation of complex components well. Based on the verified model, a set of analyses are conducted to evaluate the effects of local stiffness nonhomogenization and milling force variation on machined surface variation. It demonstrates that surface variation with surface peaks and concaves is strongly correlated with local stiffness nonhomogenization especially in feed direction. And thus the coupled prediction method provides a theoretical and efficient way to study surface variation induced by face milling of large-scale complex components.

Models for Prediction of Surface Roughness in a Face Milling Process Using Triangular Inserts

Research was carried out to develop a mathematical model based on the cutting tool surface profile geometry to predict surface roughness in face milling. Previous models were derived using either the simple assumption of a perfectly round tool nose or statistical analysis based on a large number of experiments. In this research, three milling cases were defined based on the magnitude of the feed rate using a triangular insert with a round corner. In case 1, the machine marks only consisted of a series of arcs. In case 2, the machine marks included a series of an arc and one straight line. In case 3, the machine marks consisted of a series of an arc and two straight lines. Three different equations for surface roughness prediction were obtained based on each of the three cases. Experiments were done to validate the models, and the results showed that the mathematical models had good correlation with experimental results.

Simulation research of the temperature distribution during face milling

The paper presents the results of numerical calculations showing the temperature distribution of the workpiece (copper) during the face milling process. The ANSYS program was used for simulation studies. The calculations were carried out for two different cutting speeds, examining their influence on the temperature of the workpiece. The calculations constitute the basis for further research in order to determine the influence of variable temperature fields on the thermal expansion of the workpiece and to estimate the dimensional accuracy of the parts to be made during milling.

Ultrasonic assisted peripheral milling of fiber reinforced plastics in consideration of clamping and cutting edge geometry

In the field of machining fiber reinforced plastics (FRP) with defined cutting edges, there is still a high demand for research and development, especially, when various fiber types are embedded. Thus, when machining glass fiber reinforced plastics, the application of uncoated tools from tungsten carbide is often state of the art. This material is subject to excessive wear during machining FRP, which negatively affects the process performance and makes a regular tool change necessary. This is caused by a high abrasiveness of the fibers and by vibrations of the FRP components, which affects the cutting edge. One approach to reduce the process forces and therefore reduce tool wear is to superimpose the tool movement with a longitudinal high-frequency movement in ultrasonic range. Many studies showed that this technology is able to generate advantages in drilling and face milling processes. Primarily it reduces tool wear and improves machining quality. This study shows that positive effects also occur during peripheral milling of FRP, especially when using an unstable clamping. In this case, ultrasonic assistance can reduce the machining forces significantly. Additionally, it is shown that the shape and the state of wear of the cutting edge affects the impact of ultrasonic assistance in this application case.