Fuzzy Modeling of Surface Roughness Parameters in Machining Ti-6Al-4V Alloy

2015 ◽  
Vol 766-767 ◽  
pp. 681-686 ◽  
Author(s):  
J. Nithyanandam ◽  
K. Palanikumar ◽  
Sushil Laldas
Keyword(s):  
Surface Roughness ◽  
Titanium Alloys ◽  
Chemical Reactivity ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Fuzzy Modeling ◽  
Depth Of Cut ◽  
Work Piece ◽  
Nose Radius

Titanium alloys are attractive materials used in different engineering applications, due to its excellent combination of properties such as high strength to weight ratio, good corrosion resistance and high temperature applicability. They are also being used increasingly in chemical process, automotive, biomedical and nuclear plant. When machining of Titanium alloys with traditional tools the tool wear rate high. Because of high chemical reactivity and low modulus of elasticity resulting high cutting temperature and strong adhesion between the tool and work piece materials. The poly crystalline diamond (PCD) cutting tool is used for the turning experiment. The turning parameters for the experimental work are cutting speed, feed, nose radius, and depth of cut. From the results, analysis of the influences of the individual parameters on the surface roughness have been carried out. Fuzzy modeling technique is effectively used to predict the surface roughness in the machining of titanium alloy.

Fuzzy Rule Based Modeling for Surface Roughness in Machining of Titanium Alloy Using Nano Coated Carbide Inserts

2015 ◽  
Author(s):  
J. Nithyanandam ◽  
K. Palanikumar ◽  
Sushil Lal Das
Keyword(s):  
Surface Roughness ◽  
Fuzzy Logic ◽  
Titanium Alloy ◽  
Chemical Reactivity ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Depth Of Cut ◽  
Work Piece ◽  
Nose Radius ◽  
Coated Carbide

Titanium and its alloys are used in many industrial and engineering applications because of their good properties, such as high strength to weight ratio, excellent fracture and corrosion resistance. The major application of titanium has been in the aerospace industry. When turning of titanium alloys with conventional tools, the tool wear rate increases, because of high chemical reactivity and strong adhesion between the tool and work piece materials. The nano coated carbide cutting tool is used for the turning experiment. The cutting parameter for the experimental works are cutting speed, feed rate, nose radius, and depth of cut. Fuzzy logic modeling is used for the prediction of surface roughness in machining of titanium alloy. From the results, the Fuzzy logic model is the best suited method for modeling the turning parameters of titanium alloy by using Nanocoated carbide tools.

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.

Modelling and Optimization of Tool Chip Interface Temperature and Surface Roughness in CNC Dry Turning of EN 24 Steel

2016 ◽  
Vol 16 ◽  
pp. 7-15 ◽  
Author(s):  
Nirmal Kumar Mandal ◽  
Tanmoy Roy
Keyword(s):  
Surface Roughness ◽  
Cutting Tool ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Minimum Quantity Lubrication ◽  
Machining Process ◽  
Interface Temperature ◽  
Depth Of Cut ◽  
Work Piece ◽  
Substantial Impact

Abstract. Kinetic energy of a machining process is converted into heat energy. The generated heat at cutting tool and work piece interface has substantial impact on cutting tool life and quality of the work piece. On the other hand, development of advanced cutting tool materials, coatings and designs, along with a variety of strategies for lubrication, cooling and chip removal, make it possible to achieve the same or better surface quality with dry or Minimum Quantity Lubrication (MQL) machining than traditional wet machining. In addition, dry and MQL machining is more economical and environment friendly. In this work, 20 no. of experiments were carried out under dry machining conditions with different combinations of cutting speed, feed rate and depth of cut and corresponding cutting temperature and surface roughness are measured. The no. of experiments is determined through Design of Experiments (DOE). Nonlinear regression methodology is used to model the process using Response Surface Methodology (RSM). Multi-objective optimization is carried using Genetic Algorithm which ensures high productivity with good product quality.

Study of Nanolayer AlTiN/TiN Coating Deposition on Cemented Carbide and its Performance as a Cutting Tool

2017 ◽  
Vol 47 ◽  
pp. 1-10 ◽  
Author(s):  
Halil Caliskan ◽  
Emre Altas ◽  
Peter Panjan
Keyword(s):  
Surface Roughness ◽  
Wear Resistance ◽  
Elevated Temperatures ◽  
Chemical Reactivity ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Edge ◽  
Depth Of Cut ◽  
Tin Coating ◽  
Coated Tools

Titanium and its alloys are widely used in aerospace and aviation industries because of their high strength-to-weight ratio, high fracture resistance and corrosion resistance at elevated temperatures. However, chemical reactivity and low thermal conductivity of these alloys lead to adhesion and diffusion wears on carbide tools, respectively. In addition, fluctuations in cutting forces occur during the cutting process due to chip shear band formation; and chipping wear is observed at the tool cutting edge as a result. Therefore, machining of these alloys is a challenge for researchers. A common method to increase the lifetime of carbide tools is to coat them with a thin hard coating. In this study, a nanolayer AlTiN/TiN coating was deposited on carbide cutting tools using an industrial magnetron sputtering system in order to enhance their wear resistance and lifetime in milling of Ti6Al4V. The cutting tests with the coated tools were performed at a cutting speed of 50 m/min, feed rate of 0.1 mm/tooth and depth of cut of 1 mm under dry conditions. Tool wear and surface roughness on the workpiece were measured and recorded as a function of cutting distance. Wear mechanisms and types were revealed using optical and scanning electron microscopy and energy dispersive spectroscopy. It was found that the nanolayer AlTiN/TiN coated tools provide higher wear resistance and 4 times longer lifetime when compared to uncoated ones. The main observed wear types are notch wear and build-up edge formation on the cutting edge. A slight improvement in surface roughness of the workpiece was observed with the nanolayered coating.

Tool Wear Performance of CVD-Insert during Machining of Ti-6%Al-4%V ELI at High Cutting Speed

2010 ◽  
Vol 443 ◽  
pp. 371-375 ◽  
Author(s):  
Gusri Akhyar Ibrahim ◽  
Che Hassan Che Haron ◽  
Jaharah Abd. Ghani
Keyword(s):  
Titanium Alloys ◽  
Cutting Tool ◽  
Chemical Reactivity ◽  
Cutting Speed ◽  
Weight Ratio ◽  
Cutting Edge ◽  
Depth Of Cut ◽  
High Cutting Speed ◽  
Aerospace Material ◽  
Coating Delamination

Machining of titanium alloys as aerospace material that has extremely strength to weight ratio and resistant to corrosion at high-elevated temperature, become more interested topic. However, titanium alloys have low thermal conductivity, relative low modulus elasticity and high chemical reactivity with many cutting tool materials. The turning parameters evaluated are cutting speed (55, 75, 95 m/min), feed rate (0.15, 0.25, 0.35 mm/rev), depth of cut (0.10, 0.15, 0.20 mm) and tool grade of CVD carbide tool. The results that pattern of tool life progression is rapidly increase at the initial stage. It was due to small contact area between the cutting tool and the workpiece. At the first step of machining, the chip welded at the cutting edge but some chip removed away from the cutting edge. Wear mechanism produced are abrasive wear, adhesive, flaking, chipping at the cutting edge and coating delamination.

Surface Roughness Optimization in Machining of Titanium Alloy (Ti-6Al-4V)

2014 ◽  
Vol 984-985 ◽  
pp. 42-47
Author(s):  
J. Nithyanandam ◽  
Sushil Laldas ◽  
K. Palanikumar
Keyword(s):  
Surface Roughness ◽  
Titanium Alloy ◽  
Thermal Properties ◽  
Cutting Speed ◽  
Weight Ratio ◽  
High Strength ◽  
Cutting Parameters ◽  
Ratio Method ◽  
Depth Of Cut ◽  
Nose Radius

Titanium is one of the important kinds of material used in different engineering fields. They have very good properties like high strength to weight ratio, superior corrosion resistance and thermal properties. They are very attractive materials and has application aerospace, biomedical and automotive field. they are classified to be “difficult-to-Machine materials” as they posses poor thermal properties, poor machinability, etc.The prime important is with the study of machining characteristics and the optimization of the cutting parameters. In this paper Titanium alloy (Ti-6Al-4V) is taken, the dry turning experiments are carried out in semi-automatic lathe using poly crystalline diamond (PCD) cutting tool insert. The taguchi’s design of L27orthogonal array is done by four machining factors namely cutting speed, feed, nose radius and depth of cut at three levels. The optimal machining conditions are arrived by Signal-Noise ratio method with respect to surface roughness (Ra). The analysis of variance (ANOVA) and the percentage of contribution of feed, cutting speed, nose radius and depth of cut for better surface roughness is validated using S/N ratio. In this result indicated that the feed is a vital parameter followed by cutting speed, the nose radius and then by depth of cut. The worn out surface of the insert is examined by using scanning electron microscope (SEM).

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.

OPTIMIZATION OF PROCESS PARAMETERS AFFECTING CUTTING FORCE, POWER CONSUMPTION AND SURFACE ROUGHNESS USING TAGUCHI-BASED GRAY RELATIONAL ANALYSIS IN TURNING AISI 1040 STEEL

2021 ◽  
Author(s):  
MAHIR AKGÜN
Keyword(s):  
Surface Roughness ◽  
Power Consumption ◽  
Cutting Force ◽  
Cutting Speed ◽  
System Optimization ◽  
Gray Relational Analysis ◽  
Depth Of Cut ◽  
Nose Radius ◽  
Significance Level ◽  
Relational Analysis

This study focuses on optimization of cutting conditions and modeling of cutting force ([Formula: see text]), power consumption ([Formula: see text]), and surface roughness ([Formula: see text]) in machining AISI 1040 steel using cutting tools with 0.4[Formula: see text]mm and 0.8[Formula: see text]mm nose radius. The turning experiments have been performed in CNC turning machining at three different cutting speeds [Formula: see text] (150, 210 and 270[Formula: see text]m/min), three different feed rates [Formula: see text] (0.12 0.18 and 0.24[Formula: see text]mm/rev), and constant depth of cut (1[Formula: see text]mm) according to Taguchi L18 orthogonal array. Kistler 9257A type dynamometer and equipment’s have been used in measuring the main cutting force ([Formula: see text]) in turning experiments. Taguchi-based gray relational analysis (GRA) was also applied to simultaneously optimize the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]). Moreover, analysis of variance (ANOVA) has been performed to determine the effect levels of the turning parameters on [Formula: see text], [Formula: see text] and [Formula: see text]. Then, the mathematical models for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) have been developed using linear and quadratic regression models. The analysis results indicate that the feed rate is the most important factor affecting [Formula: see text] and [Formula: see text], whereas the cutting speed is the most important factor affecting [Formula: see text]. Moreover, the validation tests indicate that the system optimization for the output parameters ([Formula: see text], [Formula: see text] and [Formula: see text]) is successfully completed with the Taguchi method at a significance level of 95%.

Experimental Determination of Optimal Speeds for Alloy Steels in Plane Turning

2008 ◽  
Author(s):  
T. Srikanth ◽  
V. Kamala
Keyword(s):  
Surface Roughness ◽  
Force Measurement ◽  
Cutting Speed ◽  
Vital Role ◽  
The Other ◽  
Depth Of Cut ◽  
Work Piece ◽  
Material Interface ◽  
Alloy Steels

In machining, speeds play vital role. The operator should know exactly the speed at which machining should be performed to get the required surface finish. In this paper, an attempt is made to determine the optimal cutting speed for machining of alloy steels. Three work piece materials having different hardness are taken and machined using a round nose tool with a coated tip. The tool dynamometer is attached to the tool post for force measurement. Turning operation on the work piece is performed on lathe at four different speeds, keeping the feed and depth of cut constant. Cutting forces acting on the tool, temperature at the tool and material interface are recorded. Power consumed being determined by a wattmeter and surface roughness values are measured. The same procedure is repeated for the other two work-pieces materials and optimal speeds for machining are determined for the three specimens. The results obtained are compared with the theoretical values and found to be very close.

Identification of Optimum Heating Temperature in Thermal Assisted Turning of Stainless Steel Using Three Different Approaches

2013 ◽  
Vol 393 ◽  
pp. 194-199 ◽  
Author(s):  
A.K.M. Nurul Amin ◽  
Muammer Din Arif ◽  
Noor Hawa B. Mohamad Rasdi ◽  
Khairus Syakirah B. Mahmud ◽  
Abdul Hakam B. Ibrahim ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Heating Temperature ◽  
Cutting Temperature ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
304 Stainless Steel ◽  
Depth Of Cut ◽  
Work Piece ◽  
Optimum Temperature ◽  
Main Concept

Thermal or heat assisted machining is used to machine hard and difficult-to-machine materials such as Inconel and Titanium alloys. The main concept is that localized surface heating of the work-piece reduces the yield strength of the material significantly, making it amenable to plastic deformation and machining. Thus, heat assisted machining has been used for over a century. However, the heating technique and temperature are very much dependent on the type of working material. Therefore, a multitude of heating techniques has been applied over the years including Laser Assisted Machining (LAM) and Plasma Enhanced Machining (PEM) in the industry. But such processes are very expensive and have not been found in wide scale applications. The authors of the current research have therefore looked into the application of a simple Tungsten Inert Gas (TIG) welding setup to perform heat assisted turning of AISI 304 Stainless Steel. Such welding equipment is relatively cheap and available. Also, stainless steel is perennially used in the industry for high strength applications. Hence, it is very important to determine with optimal cutting temperature when applying a TIG setup for heat assisted machining of stainless steel. This paper describes three separate techniques for determining the optimum temperature. All three processes applied the same experimental setup but used different variables for evaluating the best temperature. The first process used vibration amplitude reduction with increment in temperature to identify the desired temperature. The second process used chip shrinkage coefficient to locate the same temperature. And finally, the third process investigated tool wear as a criterion for determining the optimum temperature. In all three cases the three primary cutting parameters: cutting speed, feed, and depth of cut, were varied in the same pattern. The results obtained from all three approaches showed that 450oC was undoubtedly the best temperature for heat assisted machining of stainless steel.

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