Modeling of Top Burr Formation in Micro-End Milling of Zr-Based Bulk Metallic Glass

2019 ◽  
Vol 7 (4) ◽  
Author(s):  
Debajyoti Ray ◽  
Asit Baran Puri ◽  
Naga Hanumaiah ◽  
Saurav Halder
Keyword(s):  
Metallic Glass ◽  
Bulk Metallic Glass ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Milling Process ◽  
Burr Formation ◽  
Depth Of Cut ◽  
Glass Work ◽  
Micro End Milling

Abstract In this study, an analysis is presented for the formulation of a model in top burr formation to estimate the width of the top burrs formed in micro-end milling process. In this analysis, the width of the top burrs, considered to be the measure of burr size of top burrs, represents the extent of burr formation. The model is applied to quantify the burr formation in Zr-based bulk metallic glass, an amorphous metallic alloy. In this study, micromilling experiments were carried out on the bulk metallic glass work material at different levels of feed rate, axial depth of cut, and cutting speed to compare the experimental values of top burr width with the predicted values. Analysis is carried out to characterize top burr formation based on the experimental results of burr sizes in the up-milling and down-milling side edges, and the influential effects of the cutting parameters on the burr formation are analyzed.

Investigation on Cutting Forces and Surface Finish in Mechanical Micro Milling of Zr-Based Bulk Metallic Glass

2019 ◽  
Vol 18 (01) ◽  
pp. 113-132
Author(s):  
Debajyoti Ray ◽  
Asit Baran Puri ◽  
Nagahanumaiah
Keyword(s):  
Surface Roughness ◽  
Metallic Glass ◽  
Cutting Force ◽  
Bulk Metallic Glass ◽  
Cutting Parameters ◽  
Milling Process ◽  
Spindle Speed ◽  
Micro Milling ◽  
Depth Of Cut ◽  
Axial Depth

Precision micro-component fabrication demands suitable manufacturing processes that ensure making of parts with good form and finish. Mechanical micro milling represents a flexible and powerful process that exhibits enhanced capability to create micro features. Bulk metallic glass (BMG) represents a young class of amorphous alloy material with superior mechanical and physical properties and finds appreciable micro scale applications like biomedical devices and implants, micro parts for sport items and various other micro- components. In the present work, an attempt has been made to analyze the influence of the cutting parameters like spindle speed, feed per tooth and axial depth of cut on the machinability of BMG, in mechanical micro-milling process. The micro-milling process performances have been evaluated concerning to cutting forces and surface roughness generated, by making full slots on the workpiece with solid carbide end mill cutters. The paper presents micro-machining results for bulk metallic glass machined with commercial micro-milling tool at low cutting velocity regime. Response surface methodology (RSM) has been employed for process modeling and subsequent analysis to study the influence of the combination of cutting parameters on responses within the selected domain of cutting parameters. It has been found that the effect of axial depth of cut on the cutting force components is remarkably significant. Cutting force components increases with the increase in axial depth of cut and decreases with increase in spindle speed. At low feed rate, cutting force in the feed direction (Fx, i.e., cutting force along x-direction) increases with a decrease in feed rate. This increase of force could be due to the possible ploughing effect. A similar pattern of variation has been observed with cutting force component in cross-feed direction (Fy) also. It has been found that effect of feed per tooth on the roughness parameter Ra is remarkably significant. Surface roughness increases with feed per tooth. Axial depth of cut does not contribute much to the surface roughness. Surface roughness decrease with the increase of spindle speed.

Prediction of Burr Formation in Fabricating MEMS Components by Micro End Milling

2009 ◽  
Vol 74 ◽  
pp. 247-250 ◽  
Author(s):  
Mohammad Yeakub Ali ◽  
Mohd Aliff Omar ◽  
Khairul Irman Othman ◽  
Wayne N.P. Hung
Keyword(s):  
End Milling ◽  
Cutting Speed ◽  
304 Stainless Steel ◽  
Burr Formation ◽  
Aisi 304 ◽  
Burr Height ◽  
Milling Parameters ◽  
Milling Tool ◽  
Micro End Milling ◽  
Chip Load

This paper discusses burr formation in micromilling of AISI 304 stainless steel. Chip load, cutting speed and the application of coolant were chosen as the milling parameters. Experiments were conducted using 500 µm diameter tungsten carbide end milling tool. Milling parameters and measured burr height values were analyzed and statistical models were developed for the estimation of burr height. The models showed that the chip load and cutting speed both have direct and interactive contribution to burr formation. When micromachining without coolant, the burr height increases about 40% compared to that of machining with coolant. The optimized values of chip load and cutting speed were found to be 1 µm/tooth and 78 mms-1 respectively. The predicted burr heights were 5-7% larger than that of measured values.

Optimization of Cutting Parameters of Multiple Performance Characteristics in End Milling of AlSi/AIN MMC – Taguchi Method and Grey Relational Analysis

2014 ◽  
Vol 68 (4) ◽  
Author(s):  
S. H. Tomadi ◽  
J. A. Ghani ◽  
C. H. Che Haron ◽  
M. S. Kasim ◽  
A. R. Daud
Keyword(s):  
Taguchi Method ◽  
Grey Relational Analysis ◽  
Volume Fraction ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Performance Characteristics ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

The main objective of this paper is to investigate and optimize the cutting parameters on multiple performance characteristics in end milling of Aluminium Silicon alloy reinforced with Aluminium Nitride (AlSi/AlN MMC) using Taguchi method and Grey relational analysis (GRA). The fabrication of AlSi/AlN MMC was made via stir casting with various volume fraction of particles reinforcement (10%, 15% and 20%). End milling machining was done under dry cutting condition by using two types of cutting tool (uncoated & PVD TiAlN coated carbide). Eighteen experiments (L18) orthogonal array with five factors (type of tool, cutting speed, feed rate, depth of cut, and volume fraction of particles reinforcement) were implemented. The analysis of optimization using GRA concludes that the better results for the combination of lower surface roughness, longer tool life, lower cutting force and higher material removal could be achieved when using uncoated carbide with cutting speed 240m/min, feed 0.4mm/tooth, depth of cut 0.3mm and 15% volume fraction of AlN particles reinforcement. The study confirmed that with a minimum number of experiments, Taguchi method is capable to design the experiments and optimized the cutting parameters for these performance characteristics using GRA for this newly develop material under investigation.

Improvement of Surface Roughness in End Milling of Ti6Al4V by Coupling RSM with Genetic Algorithm

2011 ◽  
Vol 264-265 ◽  
pp. 1154-1159
Author(s):  
Anayet Ullah Patwari ◽  
A.K.M. Nurul Amin ◽  
S. Alam
Keyword(s):  
Genetic Algorithm ◽  
Surface Roughness ◽  
Titanium Alloys ◽  
End Milling ◽  
Fitness Function ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Resistance Surface

Titanium alloys are being widely used in the aerospace, biomedical and automotive industries because of their good strength-to-weight ratio and superior corrosion resistance. Surface roughness is one of the most important requirements in machining of Titanium alloys. This paper describes mathematically the effect of cutting parameters on Surface roughness in end milling of Ti6Al4V. The mathematical model for the surface roughness has been developed in terms of cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). Central composite design was employed in developing the surface roughness models in relation to primary cutting parameters. The experimental results indicate that the proposed mathematical models suggested could adequately describe the performance indicators within the limits of the factors that are being investigated. The developed RSM is coupled as a fitness function with genetic algorithm to predict the optimum cutting conditions leading to the least surface roughness value. MATLAB 7.0 toolbox for GA is used to develop GA program. The predicted results are in good agreement with the experimental one and hence the model can be efficiently used to achieve the minimum surface roughness value.

A Prediction Model of Surface Roughness in Micro End Milling

2015 ◽  
Vol 727-728 ◽  
pp. 354-357
Author(s):  
Mei Xia Yuan ◽  
Xi Bin Wang ◽  
Li Jiao ◽  
Yan Li
Keyword(s):  
Surface Roughness ◽  
Prediction Model ◽  
Feed Rate ◽  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Cutting Depth ◽  
Micro End Milling

Micro-milling orthogonal experiment of micro plane was done in mesoscale. Probability statistics and multiple regression principle were used to establish the surface roughness prediction model about cutting speed, feed rate and cutting depth, and the significant test of regression equation was done. On the basis of successfully building the prediction model of surface roughness, the diagram of surface roughness and cutting parameters was intuitively built, and then the effect of the cutting speed, feed rate and cutting depth on the small structure surface roughness was obtained.

Tool Vibration due to High Speed Micro End Milling Parameters

2013 ◽  
Vol 372 ◽  
pp. 364-368 ◽  
Author(s):  
Abdul Rahman Mohamed ◽  
Nur Atiqah ◽  
Mohammad Yeakub Ali ◽  
M.S.H. Chowdhury
Keyword(s):  
High Speed ◽  
End Milling ◽  
Signal To Noise Ratio ◽  
Cutting Parameters ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Signal To Noise ◽  
Tool Vibration ◽  
Milling Parameters ◽  
Micro End Milling

This paper presents the effect of high speed micro end milling parameters on tool vibration during machining of poly (methyl methacrylate) (PMMA). The main focus is to achieve minimum tool vibration by controlling the cutting parameters; spindle speed, feed rate and depth of cut. An empirical model for tool vibration has been developed using Taguchi method. The orthogonal array, signal-to-noise ratio and analysis of variance revealed that high spindle speed is the most influential parameter to increase the level of tool vibration.

Research on the Modeling and Experimenting of Burr Formation Process in Double-Edged Micro-Plat End Milling Operation

2013 ◽  
Vol 770 ◽  
pp. 248-252 ◽  
Author(s):  
Ni Chen ◽  
Ming Jun Chen ◽  
Hai Bo Ni ◽  
Ning He ◽  
Zhan Qiang Liu
Keyword(s):  
End Milling ◽  
Orthogonal Experiment ◽  
Cutting Parameters ◽  
Micro Milling ◽  
Burr Formation ◽  
Depth Of Cut ◽  
Generation Process ◽  
Burr Size ◽  
Milling Operation ◽  
Exit Burr

Burrs generated in micro-milling operation have a significant impact on the surface quality and operational performance of the finished microstructures. In order to gain a better recognition of burr generation process, 3-dimensional double-edged micro-flat end milling operation FEM models on Ti6Al4V have been established. Burrs occurred in simulation can be classified into three types: entrance burr, exit burr, top burr. Their formation processes and causes are well investigated and analyzed, moreover, a series of experiments are conducted to validate the burr morphologies which are received in simulation. At last, the effect of cutting parameters on top burr size is studied through orthogonal experiment on Ti6Al4V, it can be concluded that the axial depth of cut has the greatest effect on top burr size, and the effect of spindle speed on top burr size is the least.

Studies Regarding the Influence on the Surface Roughness Material of the Cutting Regime during the Milling Processing

2015 ◽  
Vol 1128 ◽  
pp. 271-281 ◽  
Author(s):  
Mihai Demian ◽  
Luminita Grecu ◽  
Gabriela Demian
Keyword(s):  
Surface Roughness ◽  
Process Parameters ◽  
Optimal Parameter ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Error Variance ◽  
Milling Process ◽  
Depth Of Cut ◽  
Low Level ◽  
First Case

The aim of the present paper is to establish the optimal parameter values of the cutting regime of a milling process. The paper presents a study regarding the influence of the cutting parameters on the surface roughness of the material and also on the vibration generated by their combinations, during a processing by milling. The studies are made on samples made from S355 JR steel with a metal milling machine FUS 25, which is used also for the experiments. The samples dimensions are 210x150x16mm. For the experiments there was used a cylindrical - frontal milling tool, with 32mm diameter and 10 tooth. Basic parameters of milling processing of materials we have considered in this paper are: feed rate [mm/min]; cutting speed RPM [rot/min]; depth of cut [mm]. For each of this parameters three levels were envisaged. For a 100% accurate experiment results at least 27 experiments must be done. Using an L9 orthogonal array, the number of experiments is reduced to nine and the accurate of the method is around 99.96%. The optimal process parameters values are obtained using Taguchi method considering three situations. In the first case the goal is to get only a fine roughness for the sample. The second studied case is focused on finding a low level for the vibration generated during the milling process. The aim of the last study is to find a fine roughness and also a low level of vibration for the process. The analysis of variance (ANOVA) is applied, in all cases, in order to estimate the error variance and to rank the process parameters according to their importance.

Prediction of Surface Roughness Using Back-Propagation Neural Network in End Milling Ti-6Al-4V Alloy

2011 ◽  
Vol 325 ◽  
pp. 418-423 ◽  
Author(s):  
Song Zhang ◽  
Jian Feng Li
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
End Milling ◽  
Back Propagation ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Back Propagation Neural Network ◽  
Depth Of Cut ◽  
Radial Depth ◽  
Bpnn Model

Surface roughness plays a significant role in machining industry for proper planning of process system and optimizing the cutting conditions. In this paper, a back-propagation neural network (BPNN) model has been developed for the prediction of surface roughness in end milling process. A large number of milling experiments were conducted on Ti-6Al-4V alloy using the uncoated carbide tools. Four cutting parameters including cutting speed, feed per tooth, radial depth of cut, and axial depth of cut are used as the inputs to develop the BPNN model, while surface roughness corresponding to these combinations of different cutting parameters is the output of the neural network model. The performance of the trained BPNN model has been verified with the experimental results, and it is found that the BPNN predicted and the experimental values are very close to each other.

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