Top PDF Multi Objective Optimization of Cutting Parameters in Turning Operation to Reduce Surface Roughness and Cutting Forces

Multi Objective Optimization of Cutting Parameters in Turning Operation to Reduce Surface Roughness and Cutting Forces

Multi Objective Optimization of Cutting Parameters in Turning Operation to Reduce Surface Roughness and Cutting Forces

Turning is one the most important machining operation in industries. The process of turning is influenced by many factors such as the cutting velocity, feed rate, depth of cut, geometry of cutting tool cutting conditions etc. The finished product with desired attributes of size, shape, and surface roughness and cutting forces developed are functions of these input parameters. Properties wear resistance, fatigue strength, coefficient of friction, lubrication, wear rate and corrosion resistance of the machined parts are greatly influenced by surface roughness. Forces developed during cutting affect the tool life hence the cost of production. In many manufacturing processes engineering judgment is still relied upon to optimize the multi-response problem. Therefore multi response optimization is used in this study to optimization problem to finds the appropriate level of input characteristics.
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Multi objective optimization of cutting parameters in turning operation using Taguchi method

Multi objective optimization of cutting parameters in turning operation using Taguchi method

Kohli and Dixit (2005) [11] proposed a neural-network-based methodology with the acceleration of the radial vibration of the tool holder as feedback. For the surface roughness prediction in turning process the back-propagation algorithm was used for training the network model. The methodology was validated for dry and wet turning of steel using high speed steel and carbide tool and observed that the proposed methodology was able to make accurate prediction of surface roughness by utilizing small sized training and testing datasets. Pal and Chakraborty (2005) [12] studied on development of a back propagation neural network model for prediction of surface roughness in turning operation and used mild steel work-pieces with high speed steel as the cutting tool for performing a large number of experiments. The authors used speed, feed, depth of cut and the cutting forces as inputs to the neural network model for prediction of the surface roughness. The work resulted that predicted surface roughness was very close to the experimental value.
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Multi Objective Optimization of Cutting and Geometric parameters  in turning operation to Reduce Cutting forces and Surface Roughness

Multi Objective Optimization of Cutting and Geometric parameters in turning operation to Reduce Cutting forces and Surface Roughness

Turning operation is one of the most important machining operations to be carried out in different industries for manufacturing of various products. As it is a basic operation for various industries it is very essential to optimize the various parameters affecting turning operation for the optimum operating condition. Turning operation is affected by both cutting parameters and geometrical parameters. The parameter influence most are cutting velocity, depth of cut , feed rate, geometry of cutting tool like principle cutting edge angle ,rake angle, nose radius etc. In order to control surface roughness and cutting force acting on material during turning operation it is very necessary to control these parameters as the product with desired attributes are function of these parameter.
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A Utility Concept Approach for Multi-objective optimization of Taper Cutting Operation using WEDM

A Utility Concept Approach for Multi-objective optimization of Taper Cutting Operation using WEDM

The present study highlights a multi response optimization approach to determine the optimal process parameters in wire electrical discharge machining process during taper cutting operation. Experiments have been conducted using six process parameters such as part thickness, taper angle, pulse duration, discharge current, wire speed and wire tension each at three levels for obtaining the responses like angular error, surface roughness, and cutting speed. Taguchi’s L 27 orthogonal array is used to gather information regarding the process with less number of experimental runs. Traditional Taguchi approach is insufficient to solve a multi response optimization problem. In order to overcome this limitation, utility theory has been implemented, to convert multi-responses into single equivalent response called overall utility index. The weight for each criterion (response) is obtained by analytical hierarchy process (AHP) instead of using intuition and judgement of the decision maker. ANOVA analysis is also carried out to find out the significant effect of the process parameters during taper cutting in WEDM process. Finally confirmation test has been carried out to verify the result.
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Application of Regression in the Analysis of Cutting Parameters for Surface Roughness in Turning

Application of Regression in the Analysis of Cutting Parameters for Surface Roughness in Turning

P.V.S. Suresh et al. [13], The experimentation was carried out with TiN-coated tungsten carbide (CNMG) cutting tools, for machining mild steel work-pieces covering a wide range of machining conditions. W.S. Lin et al. [14], an abductive network is adopted to construct a prediction model for surface roughness and cutting force. M.Y. Noordin et al. [15], The performance of a multilayer tungsten carbide tool was described using response surface methodology (RSM) when turning AISI 1045 steel. D.I. Lalwani et al. [16], In the present study, an attempt has been made to investigate the effect of cutting parameters (cutting speed, feed rate and depth of cut) on cutting forces (feed force, thrust force and cutting force) and surface roughness in finish hard turning of MDN250 steel [equivalent to 18Ni(250) maraging steel] using coated ceramic tool. Davim. J et al. [17], presents a study of the influence of cutting parameters on surface roughness in turning of glass-fibre-reinforced plastics (GFRPs). Dilbag Singh et al. [18], An experimental investigation was conducted to determine the effects of cutting conditions and tool geometry on the surface roughness in the finish hard turning of the bearing steel (AISI 52100). Ahmet Hasçalhk et al. [19], the effect and optimization of machining parameters on surface roughness and tool life in a turning operation was investigated by using the Taguchi method. The experimental studies were conducted under varying cutting speeds, feed rates, and depths of cut. An orthogonal array, the signal-to-noise (S/N) ratio, and the analysis of variance (ANOVA) were employed to the study the performance characteristics in the turning of commercial Ti-6Al-4V alloy using CNMG 120408-883 insert cutting tools.
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Multi-objective optimization of cutting parameters in turning using grey relational analysis   Pages 547-558
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Multi-objective optimization of cutting parameters in turning using grey relational analysis Pages 547-558 Download PDF

Sadasiva Rao et al. (2012) work was focused to study the effect of process parameters such as speed, feed and depth of cut and approach angle of the cutter on cutting force, tool life and surface roughness in face milling of Inconel 718. The experiments were designed based on L9 orthogonal array and carried out under dry conditions. Grey relational analysis was used to optimize the multi performance characteristics to minimize the cutting force and surface roughness and maximize the tool life criteria. Refaie et al. (2010) used Taguchi method grey analysis to determine the optimal combination of control parameters in milling. The measures of machining performance were material removal rate and surface roughness. Wang et al. (2006) utilized a hybrid algorithm combining Genetic algorithm (GA) and the Simulated Annealing (SA) to optimize multicriteria high speed milling process.
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Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718   Pages 111-134
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Multi-objective optimization of surface roughness, cutting forces, productivity and Power consumption when turning of Inconel 718 Pages 111-134 Download PDF

The Inconel is one of the most important materials used in the modern industries. In addition of the best properties in terms of high strength, corrosion resistance, heat resistance and fatigue resistance, the Inconel 718 has, also a low thermal conductivity (Lynch, 1989). Generally, this type of alloy is difficult to machine for the following reasons (Alauddin et al., 1996): High work hardening rates at machining, strain rates leading to high cutting forces; abrasiveness; toughness, gummy and strong tendency to weld to the tool with forming the built-up edge; low thermal properties leading to high cutting temperatures. However, it has a wide variety of applications such as aircraft gas turbines stack gas reheaters, reciprocating engines, etc. For those special material properties, high cutting force, tool wear, and cutting temperature are the main characteristic features in the machining process. Surface integrity is relatively an important term used to describe the nature or condition of the surface region of a component (Sadat, 1987). In the study of wear behavior of nano-multilayered coatings, Biksa et al. (2010) obtained that the metallurgical design of the nano-multilayered coating should be tailored to its application and to achieve better tool life when machining aerospace alloys and the adaptive nano-multilayered AlTiN/MoN coating was recommended. A review of developments towards dry and high speed machining of Inconel 718 alloy, by Dudzinski et al. (2004) shows that the higher cutting speeds under dry conditions, certainly up to 100 m/min, may be carried out with coated carbide tools. Settineri et al. (2008) investigated properties and performances of innovative coated tools in turning aerospace alloy Inconel 718 and obtained that the all tested tools performed better than the uncoated inserts.
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Analysis of Surface Roughness in Turning Using Nano Fluids: Prediction Model and Cutting Parameters Optimization

Analysis of Surface Roughness in Turning Using Nano Fluids: Prediction Model and Cutting Parameters Optimization

Abstract: Almost all machining process generates heat and friction which leads to damage of the cutting tools as well as the machined work piece. To reduce the friction, heat transfer and to remove metal particles away from the cutting zone normally cutting fluids are used in any machining operation. The present paper outlines an experimental study to optimize the effects of selected cutting parameters i.e. Cutting Speed, Feed rate, Depth of cut and type of tool for Surface Roughness of EN-19 steel alloy using Al 2 O 3 Nano fluid by employing Taguchi robust design methodology. Taguchi orthogonal array is designed with three
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Optimization Machining Parameters in a Turning Operation of Steels to Minimize Surface Roughness and Temperature

Optimization Machining Parameters in a Turning Operation of Steels to Minimize Surface Roughness and Temperature

Faculty, Department of Mechanical Engineering, Adhiparasakthi College of Engineering, Vellore, Tamilnadu, India --------------------------------------------------------------------------***---------------------------------------------------------------------------- Abstract - The present work concerned an experimental study of turning on Steel grade of EN8, Mild steel and OHNS by a Tungsten and cemented coated carbide insert tool. The primary objective of the ensuing study was to use the Taguchi Methodology in order to determine the effect of machining parameters viz. cutting speed, feed, and depth of cut, On the Temperature, Hardness and Surface roughness of the machined material. The objective was to find the optimum machining parameters so as to minimize the surface roughness and Temperature for the work materials in the chosen domain of the experiment. Temperature was measured using a digital thermometer; Surface Roughness was measured using a Mitutoyo surface tester and hardness with the help of a Brinell hardness tester. The data was compiled into MINITAB 18 for analysis. Taguchi and Analysis of Variance (ANOVA) were used to investigate the significance of these parameters on the response variables with the machining parameters as the independent variables, with the help of a MINITAB. Results showed that cutting speed is the most significant factor affecting the surface roughness and hardness, closely followed by feed and depth of cut, while the only significant factor affects the temperature was found to be the depth of cut.
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Optimization of Cutting Parameters for Turning Operation on Thermoplastic Polymer-Delrin 500AL

Optimization of Cutting Parameters for Turning Operation on Thermoplastic Polymer-Delrin 500AL

Today any component that has been manufactured undergoes some sort of machining process. Turning is one of the important processes that is widely used to create cylindrical components and it is also used for surface finish the product to make it smooth. Nowadays, plastic materials are widely used for making variety of components. To make a component with high dimensional accuracy, turning operation is used. Quality parameters are those parameters which has direct influence on the quality, cost and productivity of the product. Some of the influential parameters for turning operation are Surface Roughness, Material Removal Rate, Cutting Forces, and Tool Life etc. These parameters are in turn affected by various factors such as speed, feed, depth of cut, nose radius, tool material, lubricant etc.
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Material Removal Rate and Surface Roughness based Cutting Parameters Optimization for Turnining EN24 Steel

Material Removal Rate and Surface Roughness based Cutting Parameters Optimization for Turnining EN24 Steel

© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 253 Miroslav Radovanovic, 2018, Studied optimization of turning operation consisting multi-pass roughing and single-pass finishing AISI 1064 steel with carbide cutting tool, in terms of material removal rate and machining cost. For multi-pass roughing, optimization problem with two objectives (material removal rate and machining cost), three factors (depth of cut, feed and cutting speed), and five machining constraints (cutting force, torque, cutting power, tool life, and cutting ratio) is studied. For single-pass finishing, optimization problem with two objectives (material removal rate and machining cost), four factors (tool nose radius, depth of cut, feed, and cutting speed), and three machining constraints (surface roughness, tool life, and cutting ratio) is studied. Optimization problem is solved using three techniques: iterative search method, multi-objective genetic algorithm (MOGA), and genetic algorithm (GA). The optimal solution is determined by using the weighted-sum-type objective function, with a genetic algorithm.
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Optimization of Cutting Parameters in Ti alloy during CNC Turning

Optimization of Cutting Parameters in Ti alloy during CNC Turning

The normal probability curve for surface roughness (Ra) validates the experimental data obtained while machining the titanium alloy on CNC. The linear line is the expected values and the dots are the observed values and the dot along the line shows that the experiment is good. The versus fit drawn around the 0 line and the randomness is observed in the graph which also validate the experiment. It is valid because of randomness. The histogram is the transformation of versus graph using any operation (mainly logarithmic), The versus order is graph drawn by connecting the observed values around a initial line of 0.
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Effects of cutting parameters on surface roughness during precision turning of ti 6al 4v alloy

Effects of cutting parameters on surface roughness during precision turning of ti 6al 4v alloy

Titanium and its alloys has played a significant role in the field of aerospace, energy, chemical and bio medics due to its high strength to weight ratio and exceptional mechanical and chemical properties. Machining of titanium alloys are a major low thermal conductivity that prevents dissipation of heat easily from the tool chip interface, which in turn heats up the tool due to increasing temperature resulting in lower tool life. 2) Titanium forms alloys easily due l reactivity that causes weld and smear rapid cutting tool destruction.3) Titanium has comparatively low elasticity modulus than steel. Therefore work piece has a tendency to move away from the . Also thin parts may deflect under tool pressures, causing chatter, tool wear and tolerance problems. [1] Selection cutting conditions, tool material and its coating and cutting edge geometry is important not only to increase the productivity of machining operation but also to obtain a desirable surface integrity (i.e. residual stresses roughness, etc.) of the finished machined part. Hence, comprehensive reviews on machinability of Roughness plays a interaction of a material with its surroundings. Rough surfaces deteriorate quickly and have greater coefficient of friction than smooth surfaces. Roughness often predicts the performance of a mechanical component, as he formation of nucleation sites for cracks or corrosion [6]. Measurement of surface
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Influence of Cutting Factors on the Cutting Tool Temperature and Surface Roughness of Steel C45 during Turning Process

Influence of Cutting Factors on the Cutting Tool Temperature and Surface Roughness of Steel C45 during Turning Process

N.A. Abukhshim et al [2] explained that the cutting temperature is a key factor directly affecting cutting tools and wear, workpieces surface and precision machining accuracy according to the relative movement between the cutting tool and workpieces. The heat generated amount varies with the kind of operated material and the cutting factors, particularly the cutting speed. These cutting factors such as speed, depth of cut and feed rate were studied using 3-D temperature field of tool during machining and compared with experimental work on C45 workpiece using carbide cutting tool inserts. Cutting speed, surface quality and cutting forces depend mainly on the temperature that high temperatures can cause high mechanical stresses which lead to early tool wear and reduce tool life. Therefore, considerable attention was paid to determine the tool temperatures. The experiments were carried out for dry and orthogonal machining condition. It showed that an increase of tool temperature depended on depth of cut and especially cutting speed in high range of cutting conditions [3]. However, S.R. Das [4] presented an optimal method of the cutting parameters (cutting speed, depth of cut and feed) in dry turning of AISI D2 steel to achieve minimum tool wear and low workpiece surface temperature. The experimental layout was designed based on the Taguchi’s L9 Orthogonal array technique and analysis of variance (ANOVA) was performed to identify the effect of the cutting parameters on the response variables. The results confirmed that the depth of cut and cutting speed were the most important parameter influencing the tool wear. The minimum tool wear was found at cutting speed of 150 m/min, depth of cut of 0.5 mm and feed of 0.25 mm/rev. Similarly, low work piece surface temperature was obtained at cutting speed of 150 m/min, depth of cut of 0.5 mm and feed of 0.25 mm/rev. Thereafter, optimal ranges of tool wear and workpiece surface temperature values were predicted.
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Effect of Cutting Materials on Roughness in Turning

Effect of Cutting Materials on Roughness in Turning

The machining of materials is a working process where a workpiece obtains its required shape and dimensions by removing the material from the surface layer. The most widespread machining method is turning where material is being removed in the form of chips based on the mutual interaction of the tool and workpiece (Žitňanský et al., 2014a). Based on a long-term development of cutting materials, it is not possible to expect in the near future the development of a completely new cutting material; therefore, the research of leading manufacturers of tools and cutting materials is aimed especially at improving the existing materials, specifying the optimum parameters of machining, and exactly defi ning the areas of their use.
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Optimisation of Cutting Parameters for Minimal Surface Roughness in Single Point Diamond Turning of TI6AL4VELI

Optimisation of Cutting Parameters for Minimal Surface Roughness in Single Point Diamond Turning of TI6AL4VELI

study, L18, which is a multilevel experiment. So, cutting speed, feed rate, depth of cut and nose radius was selected as input parameters whose values were selected from the existing literature. Three levels of each parameter were chosen for testing in the current research. The chosen range of cutting speed was (2000 to 4000 rpm), the feed rate was (1 to 5 mm/min) and depth of cut was (1 to 10 µm). There are two levels for tool nose radius so for this we are going for mixed-level for making DOE. To compare the machining of Ti6Al4VELI is done with a single point diamond tool in cutting mentioned above conditions. Machining parameters and observation are shown in Table 2.2.1.
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Experimental examination of main cutting force and surface roughness dependingon cutting parameters

Experimental examination of main cutting force and surface roughness dependingon cutting parameters

Main cutting forces acting on cutting tool depending on cutting parameters when machining AISI 1117 steel were examined experimentally. Experimental results obtained were compared with the empirical results. For the experimental studies, a Kistler 9257A three component (Fc, F f and Fp) piezoelectric dynamometer was used to measure cutting forces. This dynamometer was associated with a 5019 B130 charge amplifier connected to a PC running Kistler Dynoware force measurement software. The empirical results were obtained through Kienzle approach. Five different cutting speeds, five different fe e d rates and two different depth o f cuts were used in the experiments. It was observed that cutting forces decreased as the cutting speed increased and increased by the feed rate. Experimental results also showed similar trends with the empirical results. At the end o f experiments, it was observed that surface quality increased by with increasing cutting speed and decreased with increasing feed rate. © 2008 Journal o f Mechanical Engineering. All rights reserved.
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EXPERIMENTAL STUDY ON THE EFFECT OF CUTTING PARAMETERS ON SURFACE FINISH OBTAINED IN CNC TURNING OPERATION

EXPERIMENTAL STUDY ON THE EFFECT OF CUTTING PARAMETERS ON SURFACE FINISH OBTAINED IN CNC TURNING OPERATION

ABSTRACT: This paper deals with finding optimal control parameters to get the minimum Surface roughness. It considers the analysis of effect of the process parameters, cutting speed, feed rate and depth of cut on cutting forces during turning operation. Machining is the process of removing the excess material from the work piece or unwanted material from the work piece using cutting tool. Surface finish obtained in machining process depends upon so many factors like work material, tool material, tool geometry, machining conditions, cutting fluids used and feed rate etc. In this experimental work it is planned to study the effect of process parameters on surface finish obtained in the machining process of materials like stainless steel and aluminum.
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Control of the Cutting Forces in Turning by Entry Angle and Cutting Inserts Geometry

Control of the Cutting Forces in Turning by Entry Angle and Cutting Inserts Geometry

The study o f the machinability results also in obtaining the guidelines for the development o f the cutting tools. It has contributed to very intensive development o f the cutting tools, particularly in the area o f high-speed machining, hard machining and dry m achining. Im provem ent o f ex istin g and development o f new cutting tool materials, same as the new concepts o f machine tools, provide new p o s s ib ilitie s an d c h an g e q u a n tita tiv e ly the machinability indicators. Therefore, the study o f machinability represents a continuous process [3] to [6].
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Cutting Forces and Surface Roughness in Face-Milling of SKD61 Hard Steel

Cutting Forces and Surface Roughness in Face-Milling of SKD61 Hard Steel

The surface roughness and cutting force are important machining characteristics for evaluating the productivity of machining processes. In milling processes, by using Taguchi method and ANOVA analysis, the cutting forces and surface roughness could be investigated based on a number of factors, such as depth of cut, feed rate, cutting speed, cutting time, workpiece hardness, etc. Several research works had been conducted in different conditions and had also been applied for different workpieces and tool materials, such as Kıvak [26], Ozcelik and Bayramoglu [27], Turgut et al. [24], Karakas et al. [28], and Jayakumar et al. [29].
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