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“Optimization of Cutting Parameters for Turning AISI 316 Stainless Steel Based on Taguchi Method”
... They observed that the hardness of AISI 304 steel was the least affected by an increase in cutting speed and feed rate, however, cutting speed significantly affected SR and better surface finish was achieved at higher cutting speeds and smaller feed rates. Chandrasheker et al. [15] performed the turning of AISI 316 stainless using diamond tipped tool, since diamond is a super hard material and has many advantages, as compared to tools made with common abrasives. They concluded that surface finish was significantly affected by cutting speed and was followed by cutting fluid. ...
The present study investigates the CNC milling performance of the machining of AISI 316 stainless steel using a carbide cutting tool insert. Three critical machining parameters, namely cutting speed (v), feed rate (f) and depth of cut (d), each at three levels, are chosen as input machining parameters. The face-centred central composite design (FCCCD) of the experiment is based on response surface methodology (RSM), and machining performances are measured in terms of material removal rate (MRR) and surface roughness (SR). Analysis of variance, response graphs, and three-dimensional surface plots are used to analyse experimental results. Multi-response optimization using the data envelopment analysis based ranking (DEAR) approach is used to find the ideal configuration of the machining parameters for milling AISI 316 SS. The variables v = 220 m/min, f = 0.20 mm/rev and d = 1.2 mm were obtained as the optimal machine parameter setting. Study reveals that MRR is affected dominantly by d followed by v. For SR, f is the dominating factor followed by d. SR is found to be almost unaffected by v. Finally, it is important to state that this work made an attempt to successfully machine AISI 316 SS with a carbide cutting tool insert, to investigate the effect of important machining parameters on MRR and SR and also to optimize the multiple output response using DEAR method.
... Finally, multipleregression analysis was performed to find the relationship between cutting parameters and the performance measures. Chandrasheker et al. [3] presents an effectiveapproach for the optimization of turning parameter usingthis machining parameters namely Cutting Speed, Depth of Cut, Feed Rate and cutting fluids are optimized withmultiple performance characteristics, such as minimum surface finish and maximummaterial removal rate [4]. Theresponse table and response chart for each level of machiningparameters are from the Taguchi Method and theoptimum levels of machining parameters are chosen.Kumar, et al. [5] provided us the optimized value of cutting parameters for turning AISI 316 stainless steel to achieve the better surface finish or roughness using Taguchi"s Total 9 experiments using L9 (3 4 ) Orthogonal Array by using four control factors i.e. cutting speed, depth of cut, feed rate and three different cutting fluids (straight cutting oil, sherol B, sherol ENF) and work piece material (AISI 316 stainless steel) and the turning operations are done on Banka 1000 lathe machine. ...
The objective of this work is to optimize the response Cutting parameters (Tool wear and Material Removal Rate) of AISI 1018 Low carbon mild steel by Taguchi Method in straight turningprocess. We have taken speed, feed, depth of cut and types of cutting fluids as machining parameters with their three level values. In our study a commercial semi-synthetic cutting fluid (SSCF) and two vegetable based cutting fluids are used and values of response variables are analyzed to see if the performance of response machining parameters is increased by using Vegetable based cutting fluids for sustainable machining.For individual optimization,Taguchi‟s L9(34 ) orthogonal array and Analysis of Variance(ANOVA) are used. The optimum results are verified with the help of confirmation test
... J. Chandrasheker et. al. [2] used Taguchi method for optimization of cutting parameters for turning AISI 316 Stainless steel with diamond cutting tool. Did the experiment with four cutting parameters feed rate, speed, depth of cut and cutting fluid. ...
This paper presents the effects of process parameters on surface roughness of hardened AISI420 stainless steel using cemented carbide inserts. Plan of experiment is done with the help of design of experiment. A total number of 9 experiments were conducted with an orthogonal array. In this research work, four control factors like spindle speed, feed rate, depth of cut and insert nose radius are used as a input process parameters and work piece material (AISI 420 stainless steel) is hardened to a hardness 30HRC were investigated at three different levels. The turning operations are done on Fanuc controlled Laxmi lathe machine. Surface roughnesses of the machined parts are checked by using Mitutoyo surface roughness tester SJ-210. For the statistical representation MINITAB 19 statistical software was used. The effect of each process parameter on surface roughness was analyzed using one way ANNOVA. The graph shows that, surface roughness value increases with increase in feed rate while increase in spindle speed, depth of cut and insert nose radius decreases the surface roughness.
... They found from the analysis that the parameters which affect the MRR and surface roughness in descending orders are as spindle speed, feed rate and depth of cut. J. Chandrasekhar et al. [4] used Taguchi method for optimization of cutting parameters for turning AISI 316 Stainless steel with diamond cutting tool. Did the experiment with four cutting parameters feed, speed, depth of cut and cutting fluids. ...
The objective of this paper is to obtain an optimum setting of turning parameters for to get an optimum value of surface roughness and MRR while machining AISI420 stainless steel using CNMG inserts. Turning process parameters included spindle speed, feed, depth of cut and insert nose radius. A total number of 9 experiments conducted with orthogonal array. Attempt has been made to optimize the process parameters using Taguchi method. Minitab statistical software used for the analysis of the experimental work. After experimentation and analysis, it shows that feed affects greatly on MRR followed by DOC. For to achieve maximum MRR all process parameters have to be at level 3. In case of surface roughness, feed is a major influencing parameter followed by insert nose radius. To get good surface finish spindle speed, DOC, insert nose radius to be at level 3 and feed has to be at level 1. Index Terms-Turning process, MRR, surface roughness, Taguchi method, S/N ratio, carbide inserts, Minitab software.
... Chandrasheker et al. [12]: investigated the effect of four control factors viz cutting speed, feed rate, depth of cut and three cutting fluids (Sherol B, Sherol ENF, Straight Cutting Fluid) for optimising surface roughness produced during turning operation of AISI 316 stainless steel based on Taguchi methodology by using orthogonal array with three levels of control factors. The best condition for cutting speed was observed as 96.6 m/min, feed rate 150 mm/rev. ...
The growing competition in the modern era for high productivity with high quality surface finish has emerged the need of using high quality machining tool. The influence of cutting parameters in the turning process mainly affects the surface roughness and machining time. The significant performance measures in turning of different materials are mainly cutting speed, feed rate, depth of cut, cutting fluids which affects the surface roughness of the required material. This paper reviews the optimisation of cutting parameters in turning process using Taguchi and Response Surface Methodology (RSM). The performance characteristics were checked by using Analysis of Variance (ANOVA) to find out which input parameter dominates the most during the turning process on CNC lathe. Signal to Noise ratio(S/N) is used to compare the level of desired output parameters.
The aim of the research work is to achieve higher material removal rate in CNC Turning of AA2024 via Taguchi Technique. The present work examines the effects of cutting parameters like speed, feed and depth of cut on material removal rate of AA2024. Taguchi methodology has been applied to optimize cutting parameters. The experiments were conducted using L16 orthogonal array. The research exposures that the material removal rate is directly driven by the speed, feed rate and depth of cut. It was identified that the material removal rate increases with related to feed rate, spindle speed and frequently for all depth of cut.
Present work aim to study the influence of feed rate, cutting speed and depth of cut on MRR and surface roughness of AISI 316 stainless steel during turning process. The tool used in the study was a generally used uncoated carbide tool. The experimental trial was planned by using full factorial method of design of experiments. Further the ANOVA analysis was also carried out to rank the input variables. For MRR cutting speed is the major controlling factor while surface roughness is mainly affected by feed rate. The confirmation test was also performed an error of about 4.5% was observed.
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 foundat 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.
Inconel 718, a nickel based super-alloy is an extensively used alloy, accounting for about 50% by weight of materials used in an aerospace engine, mainly in the gas turbine compartment. This is owing to their outstanding strength and oxidation resistance at elevated temperatures in excess of 550 0 C. Machining is a requisite operation in the aircraft industries for the manufacture of the components especially for gas turbines. This paper is concerned with optimization of the surface roughness when turning Inconel 718 with cermet inserts. Optimization of turning operation is very useful to reduce cost and time for machining. The approach is based on Response Surface Method (RSM). In this work, second-order quadratic models are developed for surface roughness, considering the cutting speed, feed rate and depth of cut as the cutting parameters, using central composite design. The developed models are used to determine the optimum machining parameters. These optimized machining parameters are validated experimentally, and it is observed that the response values are in reasonable agreement with the predicted values.
Purpose: The purpose of this research paper is focused on the analysis of optimum cutting conditions to getlowest surface roughness in turning SCM 440 alloy steel by Taguchi method.Design/methodology/approach: Experiment was designed using Taguchi method and 18 experiments weredesigned by this process and experiments conducted. The results are analyzed using analysis of variance(ANOVA) method.Findings: Taguchi method has shown that the depth of cut has significant role to play in producing lower surfaceroughness followed by feed. The Cutting speed has lesser role on surface roughness from the tests.Research limitations/implications: The vibrations of the machine tool, tool chattering are the other factorswhich may contribute poor surface roughness to the results and such factors ignored for analyses.Originality/value: The results obtained by this method will be useful to other researches for similar type ofstudy and may be eye opening for further research on tool vibrations, cutting forces etc.
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