Research on Relationship between Cutting Conditions and Chip Formation during End Milling of Aluminium Alloy 6061

2017 ◽  
Vol 909 ◽  
pp. 56-60
Author(s):  
Mohd Rasidi Ibrahim ◽  
Najah Mahadi ◽  
Afiff Latif ◽  
Zulafif Rahim ◽  
Zazuli Mohid ◽  
...  
Keyword(s):  
Aluminium Alloy ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Chip Morphology ◽  
Depth Of Cut ◽  
Slot Milling ◽  
Milling Operation ◽  
Alloy 6061

This paper studied chip morphology in end milling of aluminium alloy 6061 by various cutting parameter such as feed rate, cutting speed and depth of cut. Slot milling operation were conducted. The analysis consists of chip morphology, chip weight, chip thickness and chip length. Scanning Electron Microscope (SEM) were used to obtain and examine the chips. Result shows that, end milling with higher cutting speed, feed rate and depth of cut generated short, small and light weigh of chips.

Analysis of Tool Performance during Ball-End Milling of Aluminium Alloy 6061-T6

2015 ◽  
Vol 761 ◽  
pp. 318-323 ◽  
Author(s):  
Mohd Shahir Kasim ◽  
Mohamad Hazizan Atan ◽  
C.H. Che Haron ◽  
Jaharah A. Ghani ◽  
Mohd Amri Sulaiman ◽  
...  
Keyword(s):  
Aluminium Alloy ◽  
Feed Rate ◽  
Failure Modes ◽  
End Milling ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Cutting Condition ◽  
Tool Performance ◽  
Coated Carbide ◽  
Alloy 6061

This article presents the tool wear mechanism when machining Aluminium alloy 6061-T6 with PVD coated carbide under dry cutting condition. Cutting parameters selected were cutting speed, Vc = 115-145 m/min; feed rate fz = 0.15-0.2 mm/tooth and depth of cut, ap = 0.5-0.75 mm. The result showed the tool life of PVD TiAlN ranged from 11 to 97 min. Full factorial approach was employed to exhibit relationship between parameter input and output. From the analysis, cutting speed was found to be the most significant factor for tool performance followed by feed rate and depth of cut. It was also found that most of failure modes occurred were notch wear and flaking near those found near depth of cut line.

Performance Analysis of Trihexyltetradecylphosphonium Chloride Ionic Fluid under MQL Condition in Hard turning

2020 ◽  
Vol 17 (1) ◽  
pp. 7629-7647
Author(s):  
A. Pandey ◽  
R. Kumar ◽  
A. K. Sahoo ◽  
A. Paul ◽  
A. Panda
Keyword(s):  
Material Removal Rate ◽  
Feed Rate ◽  
Material Removal ◽  
Removal Rate ◽  
Cutting Speed ◽  
Coconut Oil ◽  
Chip Morphology ◽  
Weight Fraction ◽  
Cutting Fluid ◽  
Depth Of Cut

The current research presents an overall performance-based analysis of Trihexyltetradecylphosphonium Chloride [[CH3(CH2)5]P(Cl)(CH2)13CH3] ionic fluid mixed with organic coconut oil (OCO) during turning of hardened D2 steel. The application of cutting fluid on the cutting interface was performed through Minimum Quantity Lubrication (MQL) approach keeping an eye on the detrimental consequences of conventional flood cooling. PVD coated (TiN/TiCN/TiN) cermet tool was employed in the current experimental work. Taguchi’s L9 orthogonal array and TOPSIS are executed to analysis the influences, significance and optimum parameter settings for predefined process parameters. The prime objective of the current work is to analyze the influence of OCO based Trihexyltetradecylphosphonium Chloride ionic fluid on flank wear, surface roughness, material removal rate, and chip morphology. Better quality of finish (Ra = 0.2 to 1.82 µm) was found with 1% weight fraction but it is not sufficient to control the wear growth. Abrasion, chipping, groove wear, and catastrophic tool tip breakage are recognized as foremost tool failure mechanisms. The significance of responses have been studied with the help of probability plots, main effect plots, contour plots, and surface plots and the correlation between the input and output parameters have been analyzed using regression model. Feed rate and depth of cut are equally influenced (48.98%) the surface finish while cutting speed attributed the strongest influence (90.1%). The material removal rate is strongly prejudiced by cutting speed (69.39 %) followed by feed rate (28.94%) whereas chip reduction coefficient is strongly influenced through the depth of cut (63.4%) succeeded by feed (28.8%). TOPSIS significantly optimized the responses with 67.1 % gain in closeness coefficient.

Effects of Insert Nose Radius and Processing Cutting Parameter on the Surface Roughness of Aisi 316 Stainless Steel

2010 ◽  
Vol 447-448 ◽  
pp. 51-54
Author(s):  
Mohd Fazuri Abdullah ◽  
Muhammad Ilman Hakimi Chua Abdullah ◽  
Abu Bakar Sulong ◽  
Jaharah A. Ghani
Keyword(s):  
Surface Roughness ◽  
Stainless Steel ◽  
Surface Finish ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Aisi 316

The effects of different cutting parameters, insert nose radius, cutting speed and feed rates on the surface quality of the stainless steel to be use in medical application. Stainless steel AISI 316 had been machined with three different nose radiuses (0.4 mm 0.8 mm, and 1.2mm), three different cutting speeds (100, 130, 170 m/min) and feed rates (0.1, 0.125, 0.16 mm/rev) while depth of cut keep constant at (0.4 mm). It is seen that the insert nose radius, feed rates, and cutting speed have different effect on the surface roughness. The minimum average surface roughness (0.225µm) has been measured using the nose radius insert (1.2 mm) at lowest feed rate (0.1 mm/rev). The highest surface roughness (1.838µm) has been measured with nose radius insert (0.4 mm) at highest feed rate (0.16 mm/rev). The analysis of ANOVA showed the cutting speed is not dominant in processing for the fine surface finish compared with feed rate and nose radius. Conclusion, surface roughness is decreasing with decreasing of the feed rate. High nose radius produce better surface finish than small nose radius because of the maximum uncut chip thickness decreases with increase of nose radius.

Micro Flat End Milling Simulation Model With Instantaneous Plowing Area Prediction

2016 ◽  
Vol 4 (2) ◽  
Author(s):  
Abdolreza Bayesteh ◽  
Junghyuk Ko ◽  
Martin Byung-Guk Jun
Keyword(s):  
Feed Rate ◽  
Tool Path ◽  
End Milling ◽  
Chip Thickness ◽  
Depth Of Cut ◽  
Uncut Chip Thickness ◽  
Chip Area ◽  
Edge Radius ◽  
Wide Range ◽  
Radial Depth

There is an increasing demand for product miniaturization and parts with features as low as few microns. Micromilling is one of the promising methods to fabricate miniature parts in a wide range of sectors including biomedical, electronic, and aerospace. Due to the large edge radius relative to uncut chip thickness, plowing is a dominant cutting mechanism in micromilling for low feed rates and has adverse effects on the surface quality, and thus, for a given tool path, it is important to be able to predict the amount of plowing. This paper presents a new method to calculate plowing volume for a given tool path in micromilling. For an incremental feed rate movement of a micro end mill along a given tool path, the uncut chip thickness at a given feed rate is determined, and based on the minimum chip thickness value compared to the uncut chip thickness, the areas of plowing and shearing are calculated. The workpiece is represented by a dual-Dexel model, and the simulation properties are initialized with real cutting parameters. During real-time simulation, the plowed volume is calculated using the algorithm developed. The simulated chip area results are qualitatively compared with measured resultant forces for verification of the model and using the model, effects of cutting conditions such as feed rate, edge radius, and radial depth of cut on the amount of shearing and plowing are investigated.

Experimental Study of End Milling Force on Manganese Steel

2013 ◽  
Vol 718-720 ◽  
pp. 239-243
Author(s):  
Girma Seife Abebe ◽  
Ping Liu
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Second Order ◽  
Manganese Steel ◽  
Depth Of Cut ◽  
Machining Deformation ◽  
Radial Depth ◽  
Axial Depth

Cutting force is a key factor influencing the machining deformation of weak rigidity work pieces. In order to reduce the machining deformation and improve the process precision and the surface quality, it is necessary to study the factors influencing the cutting force and build the regression model of cutting forces. This paper discusses the development of the first and second order models for predicting the cutting force produced in end-milling operation of modified manganese steel. The first and second order cutting force equations are developed using the response surface methodology (RSM) to study the effect of four input cutting parameters (cutting speed, feed rate, radial depth and axial depth of cut) on cutting force. The separate effect of individual input factors and the interaction between these factors are also investigated in this study. The received second order equation shows, based on the variance analysis, that the most influential input parameter was the feed rate followed by axial depth, and radial depth of cut. It was found that the interaction of feed with axial depth was extremely strong. In addition, the interactions of feed with radial depth; and feed rate with radial depth of cut were observed to be quite significant. The predictive models in this study are believed to produce values of the longitudinal component of the cutting force close to those readings recorded experimentally with a 95% confident interval.

scholarly journals Optimum combination of process parameters to optimize Surface Roughness and Chip Thickness during End Milling of Aluminium 6351-T6 Alloy Using Taguchi Grey Relational Analysis

2017 ◽  
Vol 8 (2) ◽  
pp. 287
Author(s):  
Reddy Sreenivasulu
Keyword(s):  
Surface Roughness ◽  
Grey Relational Analysis ◽  
Feed Rate ◽  
End Milling ◽  
Chip Thickness ◽  
Optimum Combination ◽  
Spindle Speed ◽  
Depth Of Cut ◽  
Relational Analysis ◽  
Grey Relational

In any machining operations, quality is the important conflicting objective. In order to give assurance for high productivity, some extent of quality has to be compromised. Similarly productivity will be decreased while the efforts are channelized to enhance quality. In this study,  the experiments were carried out on a CNC vertical machining center (KENT and INDIA Co. Ltd, Taiwan make) to perform 10mm slots on Al 6351-T6 alloy work piece by K10 carbide, four flute end milling cutter as per taguchi design of experiments plan by L9 orthogonal array was choosen to determine experimental trials. Furthermore the spindle speed (rpm), the feed rate (mm/min) and depth of cut (mm) are regulated in these experiments. Surface roughness and chip thickness was measured by a surface analyser of Surf Test-211 series (Mitutoyo) and Digital Micrometer (Mitutoyo) with least count 0.001 mm respectively. Grey relational analysis was employed to minimize surface roughness and chip thickness by setting of optimum combination of machining parameters. Minimum surface roughness and chip thickness obtained with 1000 rpm of spindle speed, 50 mm/min feed rate and 0.7 mm depth of cut respectively. Confirmation experiments showed that Gray relational analysis precisely optimized the drilling parameters in drilling of Al 6351-T6 alloy. 

scholarly journals Neural Network Modeling of Cutting Force and Chip Thickness Ratio for Turning Aluminum Alloy 7075-T6

2018 ◽  
Vol 14 (1) ◽  
pp. 67-76
Author(s):  
Mohanned Mohammed H. AL-Khafaji
Keyword(s):  
Cutting Force ◽  
Feed Rate ◽  
Cutting Speed ◽  
Chip Thickness ◽  
Cutting Parameters ◽  
Thickness Ratio ◽  
Depth Of Cut ◽  
Feed Force ◽  
Machining Force ◽  
Force Components

The turning process has various factors, which affecting machinability and should be investigated. These are surface roughness, tool life, power consumption, cutting temperature, machining force components, tool wear, and chip thickness ratio. These factors made the process nonlinear and complicated. This work aims to build neural network models to correlate the cutting parameters, namely cutting speed, depth of cut and feed rate, to the machining force and chip thickness ratio. The turning process was performed on high strength aluminum alloy 7075-T6. Three radial basis neural networks are constructed for cutting force, passive force, and feed force. In addition, a radial basis network is constructed to model the chip thickness ratio. The inputs to all networks are cutting speed, depth of cut, and feed rate. All networks performances (outputs) for all machining force components (cutting force, passive force and feed force) showed perfect match with the experimental data and the calculated correlation coefficients were equal to one. The built network for the chip thickness ratio is giving correlation coefficient equal one too, when its output compared with the experimental results. These networks (models) are used to optimize the cutting parameters that produce the lowest machining force and chip thickness ratio. The models showed that the optimum machining force was (240.46 N) which can be produced when the cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.27 mm/rev). The proposed network for the chip thickness ratio showed that the minimum chip thickness is (1.21), which is at cutting speed (683 m/min), depth of cut (3.18 mm) and feed rate (0.17 mm/rev).

scholarly journals PREDICTION OF TANGENTIAL CUTTING FORCE IN END MILLING OF MEDIUM CARBON STEEL BY COUPLING DESIGN OF EXPERIMENT AND RESPONSE SURFACE METHODOLOGY

1970 ◽  
Vol 40 (2) ◽  
pp. 95-103 ◽  
Author(s):  
Md. Anayet Patwari ◽  
A.K.M. Nurul Amin ◽  
Waleed F. Faris
Keyword(s):  
Response Surface Methodology ◽  
Carbon Steel ◽  
Cutting Force ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Medium Carbon Steel ◽  
Depth Of Cut ◽  
Medium Carbon

The present paper discusses the development of the first and second order models for predicting the tangential cutting force produced in end-milling operation of medium carbon steel. The mathematical model for the cutting force prediction has been developed, in terms of cutting parameters cutting speed, feed rate, and axial depth of cut using design of experiments and the response surface methodology (RSM). All the individual cutting parameters affect on cutting forces as well as their interaction are also investigated in this study. The second order equation shows, based on the variance analysis, that the most influential input parameter was the feed rate followed by axial depth of cut and, finally, by the cutting speed. Central composite design was employed in developing the cutting force 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 adequacy of the predictive model was verified using ANOVA at 95% confidence level. This paper presents an approach to predict cutting force model in end milling of medium carbon steel using coated TiN insert under dry conditions and full immersion cutting.Keywords: Tangential Cutting Forces; RSM; coated TiN; model.DOI: 10.3329/jme.v40i2.5350Journal of Mechanical Engineering, Vol. ME 40, No. 2, December 2009 95-103

scholarly journals Analyzing the Effect of Machining Parameters Setting to the Surface Roughness during End Milling of CFRP-Aluminium Composite Laminates

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
M. Nurhaniza ◽  
M. K. A. M. Ariffin ◽  
F. Mustapha ◽  
B. T. H. T. Baharudin
Keyword(s):  
Surface Quality ◽  
Feed Rate ◽  
End Milling ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Optimal Cutting Parameters ◽  
Cnc End Milling

The quality of the machining is measured from surface finished and it is considered as the most important aspect in composite machining. An appropriate and optimum machining parameters setting is crucial during machining operation in order to enhance the surface quality. The objective of this research is to analyze the effect of machining parameters on the surface quality of CFRP-Aluminium in CNC end milling operation with PCD tool. The milling parameters evaluated are spindle speed, feed rate, and depth of cut. The L9 Taguchi orthogonal arrays, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA) are employed to analyze the effect of these cutting parameters. The analysis of the results indicates that the optimal cutting parameters combination for good surface finish is high cutting speed, low feed rate, and low depth of cut.

Optimisation of Machining Parameters for High Feed End Milling on Inconel 718 Super Alloy

2014 ◽  
Vol 592-594 ◽  
pp. 584-590 ◽  
Author(s):  
Vinay Varghese ◽  
K. Annamalai ◽  
K. Santhosh Kumar
Keyword(s):  
Inconel 718 ◽  
End Milling ◽  
Removal Rate ◽  
Cutting Speed ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
Milling Operation ◽  
High Feed ◽  
Grey Relational ◽  
Super Alloy

This study investigates about machining practices used worldwide for machining of Inconel 718 super alloy. The effect of machining parameters like cutting speed, feed and depth of cut on machining responses like surface roughness and material removal rate when end milling Inconel 718 is studied using nine trials carried out based on L9 orthogonal array. A Taguchi based grey relational analysis was used for optimisation of machining parameters for high feed end milling operation on Inconel 718. An analysis of variance (ANOVA) was used to find the most significant factor. Validation of results through confirmation tests was performed and experimental results show that surface quality and productivity can be improved efficiently with this approach.

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