Optimization of Turning Parameters by Using Taguchi Method for Optimum Surface Finish (original) (raw)
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Abstract— Turning is one of the most important machining processes used in various industrial applications. Usually the quality of finished part in turning operation is measured in terms of surface roughness. In turn, surface quality is determined by machining parameters and tool geometry. The objective of this study is to model and optimize machining parameters and tool geometry in order to improve the surface roughness in turning operation of AISI1045 steel. Machining parameters and tool geometry are considered as input parameters. In turn, the surface roughness is selected as process output measure of performance. A Taguchi approach is employed to gather experimental data. Then, based on signal-to-noise (S/N) ratio, the best sets of cutting parameters and tool geometry specifications have been determined. Using these parameters values, the surface roughness of AISI1045 steel parts may be minimized. Experimental results are provided to illustrate the effectiveness of the proposed approach. Keywords: machining parameters; tool geometry; signal to noise, Taguchi method, analysis of variance
Optimization of Process Parameters in Turning Operation Using Taguchi Method and Anova: A Review
This paper investigates the parameters affecting the roughness of surfaces produced in the turning process for the various materials studied by researchers. Design of experiments were conducted for the analysis of the influence of the turning parameters such as cutting speed, feed rate and depth of cut on the surface roughness. The results of the machining experiments were used to characterize the main factors affecting surface roughness by the Analysis of Variance (ANOVA) method Taguchi‟s parametric design is the effective tool for robust design it offers a simple and systematic qualitative optimal design to a relatively low cost. The Taguchi method of off-line (Engineering) quality control encompasses all stages of product/process development. However the key element for achieving high quality at low cost is Design of Experiments (DOE). In this paper Taguchi‟s (DOE) approach used by many researchers to analyze the effect of process parameters like cutting speed, feed, and depth of cut on Surface Roughness and to obtain an optimal setting of these parameters that may result in good surface finish, has been discussed.
Surface quality is one of the specified customer requirements for machined parts. There are many parameters that have an effect on surface roughness, but those are difficult to quantify adequately. In finish turning operation many parameters such as cutting speed, feed rate, and depth of cut are known to have a large impact on surface quality. In order to enable manufacturers to maximize their gains from utilizing hard turning, an accurate model of the process must be constructed. Several statistical modeling techniques have been used to generate models including regression and Taguchi methods. In this study, an attempt has been made to generate a surface roughness prediction model and optimize the process parameters Genetic algorithms (GA). Future directions and implications for manufacturers in regard to generation of an robust and efficient machining process model is discussed.
The intend of this research work is to employ Taguchi method and Analysis of Variance to find out the influences of cutting parameters such as spindle speed, depth of cut and feed on material removal rate and surface roughness for their optimization. The experimental results obtained were analyzed to find out the most significant factor effecting MRR and surface roughness. Spindle speed was found to be the most significant parameter influencing material removal rate followed by feed and depth of cut in turning of mild steel (0.18% C). Feed rate was found to be the most influencing parameter in case of surface roughness.
A Review-Optimization of Machining Parameters in Turning Operation by Employing Taguchi Method
International Journal for Scientific Research and Development, 2015
This paper deals with literature review on optimization of independent process parameters like cutting speed, feed rate, depth of cut, tool nose radius, cutting environment, tool tip temperature, etc. which affect desired response parameters or characteristics like cutting forces, material removal rate, surface roughness, power consumption . Different optimizing tools or techniques like Taguchi’s design approach, Taguchi grey relational analysis, Analysis of variance (ANOVA) are reviewed to investigate their effectiveness in optimization and finding significant factors in turning operation.
In the modern high-tech world the accuracy and finishing of a job are very important. In the present study the control parameters of a cast iron specimen undergoing turning operation are optimized so as to obtain minimum surface roughness. The parameters most responsible for surface roughness are identified and their working ranges are set. These parameters are spindle speed, feed rate and depth of cut. Experiments are conducted using parameter combinations obtained by Taguchi's L-9 orthogonal array and corresponding surface roughness are noted. S/N ratio calculations are done to find the significance order of the control parameters. Next analysis of variance (ANOVA) verifies the working ranges of the control parameters and their order of significance.
IJSRD, 2013
In present area of advance technology, the qualitative and quantitative requirement of customer is frequently change so the demand of the hour is increasing. Maintaining the economic production with optimal use of resources is of prime concern for the engineers. Metal machining is one of them. Metal machining with various process parameters is one of them. The experimental and theoretical studies show that process performance can be improved considerably by proper selection of machining parameters. The challenge of modern machining industries is mainly focused on the achievement of high quality, in terms of work piece dimensional accuracy, lower surface roughness, high production rate, less tool wear on the cutting tools, economy of machining in terms of cost saving and increase the performance of the product with reduced environmental impact.This particular phenomenon can affect the efficiency and quality of the process. The performance was assessed in terms of different parameters such as depth of cut, material removal rate, cutting efficiency. Experimental investigations were conducted to study the effect of different cutting speed , feed rate and depth of cut on material removal rate (MRR) and surface roughness in CNC machine. Taguchi's design of experiments and analysis of variance were used to determine the effect of machining parameters on Ra and MRR.
IRJET, 2020
Nowadays, surface finish has become an important indicator of quality and precision in manufacturing processes and it is considered one of the most significant parameter in industry. In this present study, the influence of different machining parameter surface roughness has been analyzed through experiments. For this experiment the material used is stainless steel420. Stainless steel420 is one of the highly used materials in thermodynamic steam trap and manufacturing industries. Most of the metal parts are manufactured by machining resulting in one of the most vital characteristics of all metal parts which is the surface roughness of the machined surfaces. Moreover, DOE techniques have been used to predict the surface quality and to select the optimal turning conditions. In this study an experimental investigation of cutting parameters (spindle speed, feed and depth of cut) in turning operation of stainless steel420 was done and influence of cutting parameters on surface roughness, tool wear, material removal rate was studied. The machining was performed using tool such as tungsten carbide tool (0.4). Taguchi method is used to find optimum result. Orthogonal array, signal to noise ratio and used to study the performance characteristics in turning operation Keywords: SR, TWR, MRR, ANOVA, S/N-RATIO, Turning is an important and widely used manufacturing process in engineering industries. The study of metal removal focuses on the features of tools, input work materials, and machine parameter settings. The technology of metal removal using turning operations has grown substantially over the past decades and several branches of engineering have contributed to this to achieve the various objectives of the process. Selection of optimal machining conditions is a key factor in achieving these objectives. There are large numbers of variables involved in the turning process. These can be categorized as input variables and output variables. Various input variables involved in the turning process are: cutting speed, feed, depth of cut, number of passes, work material and its properties, tool material and tool geometry, cutting fluid properties and characteristics, etc. Similarly, the output variables associated with the turning process are: production cost, production time, tool life, dimensional accuracy, surface roughness, cutting forces, cutting temperature, and power consumption, etc. For optimization purposes, each output variable is taken as a function of a set of input variables. To achieve several conflicting objectives of the process, optimum setting of the input variables is very essential, and should not be decided randomly on a trial basis or by using the skill of the operator. Use of appropriate optimization techniques is needed to obtain the optimum parameter settings for the process. Bhosale et al. [1], discusses on the parameter optimization of CNC lathe machining for surface roughness using the Taguchi method, where surface roughness generated during machining. In the parameter optimization, the parameters are cutting speed, feed, and depth of cut. After selecting parameters turning on CNC lathe is to be done and selected orthogonal array and parameters used for the optimum set of combined controlled parameters for surface roughness. Into this combination of parameters selected for minimum surface roughness value and for the optimum combination of parameters by Taguchi design. Taguchi orthogonal array L9 for three parameters cutting speed, feed rate, and depth of cut with its combination surface roughness measured. Material-Mild Steel Result-cutting speed-1800rpm, feed rate-0.1mm/min, depth of cut-0.4mm, surface roughness-1.27µ. Davis et al. [2], The present experimental study is concerned with the optimization of cutting parameters (depth of cut, feed rate, spindle speed) in wet turning of EN24 steel (0.4% C) with hardness 40+2 HRC. In the present work, turning operations were carried out on EN24 steel by carbide P-30 cutting tool in wet condition and the combination of the optimal levels of the parameters was obtained. The Analysis of Variance (ANOVA) and Signal-to-Noise ratio were used to study the performance characteristics in turning operation. The results of the analysis show that none of the factors was
2014
This project is based upon the study which means it is derived from experiment and observation rather than theory. For the fulfilment of objective our first motive is selection of cutting tool & work tool material selection of various process and performance parameters after parameterssss selection aims to study various techniques for the optimization for that purpose literature review and industrial survey is conducted. The objective of this study was to utilize Taguchi methods to optimize surface roughness in turning mild steel, EN-8 and EN-31. The turning parameters evaluated are cutting speed of 200, 250, and 300 m/min, feed rate of 0.08, 0.12 and 0.15 mm/rev, depth of cut of 0.5 mm and tool grades of TN60, TP0500 and TT8020, each at three levels. The experiment was designed and carried out on the basis of standard L9 Taguchi orthogonal array. The results show that the Taguchi method is suitable to solve the stated problem with minimum number of trials as compared with full factorial design.
Optimization of Surface Roughness in CNC Turning Using Taguchi Method and ANOVA
International Journal of Advanced Science and Technology, 2016
Now a day's achieving a good Surface Finish is the main challenge in the metal cutting industry during turning processes. The present work is to investigate the effect of cutting parameters (speed, feed and depth of cut) in CNC (Computer Numerical Control) turning of AA7075 to achieve low Surface Roughness using tungsten carbide insert. The experiments were designed as per the Taguchi's L9 (3 levels * 3 parameters) Orthogonal array technique. Analysis of variance (ANOVA) was performed to find the significance of the cutting parameters on the Surface roughness. The results showed that feed and cutting speed are the most important parameters influencing the surface roughness. From Taguchi analysis the minimum surface roughness are found at cutting speed of 1000 rpm (Level 1), feed of 0.2 mm/rev (Level 1) and depth of cut of 0.5 mm (Level 1) respectively. Thereafter, optimal range of surface roughness values was predicted. Finally, the relationship between cutting parameters and response was developed by using the MINITAB-16 software and regression analysis has been done. The predicted values were compared with the experimental values and it is observed that both the values were very nearer and hence the models prepared were more accurate and adequate.