IRJET- Review on Cutting Process Parameter for Surface Roughness During Turning Operation
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This document reviews the effects of cutting parameters like cutting speed, feed rate, nose radius, and depth of cut on surface roughness during turning operations. It discusses previous research that has investigated these effects on various materials like steel, aluminum alloys, and cast iron. The key findings of previous studies are that feed rate often has the greatest influence on surface roughness, while depth of cut and cutting speed also significantly affect results. Response surface methodology and Taguchi experimental design approaches have been used to optimize cutting parameters and predict surface roughness. Overall, controlling cutting parameters is important for achieving the desired surface quality during turning.
![International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1062
optimization method was employed in order to optimize the
experimental result and the effect of each parameter on the
obtained results was determined by use of analysis of
variance (ANOVA).From the analysis, feedrateis foundtobe
the most influential process parameters which influence the
surface roughness followedbycuttingspeedanddepthofcut.
Increase in feed rate and depth of cut is found to increase the
surface roughness
The effects of the feed rate, cutting speed and depth of cut on
surface roughness, cutting temperature and MRR can be
accessed after reviewing the literature. In order to optimize
the experimental results, application of Design of
Experiments (full factorialorpartial),Taguchi’sMethod,RSM
etc. are sufficiently found in literature. The effects of each
parameter on obtained results were determinedfordifferent
materials, tools and machining operations. .
3. CONCLUSION
Most of the study in turning operation is to reduce surface
roughness during machining. Various methods, used to
design experiments are suggested such as Factorial design,
Taguchi and Response Surface Methodology. Low feed rate
produces a good quality of surface as it is the most affecting
parameter in decidingsurfaceroughness.Mostofthestudyto
reduce surface roughness is done using considerable feed
rate, not much work has been done on feed rates below 0.05
mm/rev.
REFERENCES
[1] A. Esteves Correia etal,Surfaceroughnessmeasurement
in turning carbon steel AISI 1045 using wiper inserts.
Measurement xxx (2011) xxx–xxx.
[2] Suleyman Neseli, Suleyman Yaldız, Erol Turkes,
Optimization of tool geometry parameters for turning
operations based on the response surface methodology.
Measurement 44 (2011).
[3] Harsh Y Valera, Sanket N Bhavsar “Experimental
Investigation of Surface Roughness and Power
Consumption in Turning Operation of EN 31AlloySteel”
2nd International Conference on Innovations in
Automation and Mechatronics Engineering, ICIAME
2014.
[4] Sayak Mukherjeea, Anurag Kamala, Kaushik Kumar
“Optimization of Material Removal Rate DuringTurning
of SAE 1020 Material in CNC Lathe using Taguchi
Technique” 12th GLOBAL CONGRESS ON
MANUFACTURING AND MANAGEMENT, GCMM 2014.
[5] P. Jayaraman, L.Mahesh kumar “Multi-response
Optimization of Machining Parameters of Turning
AA6063 T6 Aluminium Alloy using Grey Relational
Analysis in Taguchi Method” 12th GLOBAl CONGRESS
ON MANUFACTURING AND MANAGEMENT, GCMM
2014.
[6] Shreemoy Kumar Nayak, Jatin Kumar Patro, Shailesh
Dewangan, Soumya Gangopadhyay “Multi-Objective
Optimization of Machining Parameters During
DryTurning of AISI 304 Austenitic Stainless Steel Using
Grey Relational Analysis” 3rd International Conference
on Materials Processing and Characterisation (ICMPC
2014).
[7] R. Deepak Joel Johnson, K. Leo Dev Wins,, Anil Raj, B.
Anuja Beatrice “Optimization of CuttingParametersand
Fluid Application Parameters during Turning of OHNS
Steel” 12th GLOBAL CONGRESS ON MANUFACTURING
AND MANAGEMENT, GCMM 2014.
[8] Carmita Camposeco-Negrete“Optimization of cutting
parameters using Response Surface Method for
minimizing energyconsumptionandmaximizingcutting
quality in turning of AISI 6061 T6 aluminum” Journal of
Cleaner Production xxx (2014).
[9] Md.Maksudul Islam ,Sayed Shafayat Hossain
“Optimization of Metal Removal RateforASTMA48Grey
Cast Iron in Turning Operation Using Taguchi Method”
international journal of material science and
engineering 2015.
[10] S.J. Raykara,D.M.D'Addonab,A.M.Mane“Multi-objective
optimization of high speed turning ofAl 7075usinggrey
relational analysis” 9th CIRP Conference on Intelligent
Computation in Manufacturing Engineering.
[11] G.M.Sayeed Ahmed, S. Sibghatullah Hussaini Quadri,Md
Sadiq Mohiuddin“OptimizationofFeedandRadial Force
in Turning Process by using Taguchi Design Approach”
4th International Conference on Materials Processing
and Characterisation (ICMPC 2015).
[12] Choudhury, I.A., EI-Baradie, M.A., 1997, Surface
roughness predictionintheturningofhighstrengthsteel
by factorial design of experiments, Journal of Materials
Processing Technology, Vol. 67, pp. 55-61.
[13] Nalbant, M., Gokkaya, H., Sur, G., 2007, Application of
Taguchi method in the optimization of cutting
parameters for surface roughness in turning, Materials
and Design Vol. 28, pp. 1379–1385.
[14] Dr. S.S.Mahapatra, Amar Patnaik, Prabina Ku. Patnaik,
Parametric Analysis and Optimization of Cutting
Parameters for Turning Operations based on Taguchi
Method. Proceedings of the International Conferenceon
Global Manufacturing and Innovation - July 27-29,
(2006), pp.1-9.](https://image.slidesharecdn.com/irjet-v6i3194-190814062702/85/IRJET-Review-on-Cutting-Process-Parameter-for-Surface-Roughness-During-Turning-Operation-3-320.jpg)
IRJET- Review on Cutting Process Parameter for Surface Roughness During Turning Operation
- 1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1060 Review on Cutting Process Parameter for Surface Roughness during Turning Operation Sachin Dewangan1, R. K. Rathore2 1Mtech Student, Dept. of Production Engineering, Rungta College of Engineering and Technology Bhilai, Chhattisgarh, India 2Assistant Professor, Dept. of Production Engineering, Rungta College of Engineering and Technology Bhilai, Chhattisgarh, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - The focus of the present study is to review the effect of cutting process parameter for minimum surface roughness and to design an experiment for better understanding of the effect of theprocessparametersandtheir interactions on surface roughness. Thecuttingparametersuch as cutting speed, feedrate, nose radius, depth ofcutweretaken into consideration. Key Words: Cutting speed, Feed rate, Nose radius, Depth of cut, Surface roughness, MRR, Taguchi. 1. INTRODUCTION In today competitive environment it is a continuously challenged for manufacturing industry is to ensuring higher productivity and high quality product. The most important objectives for manufacturing companies have always been costs, timeand quality. The quality of the machinedpartscan be assured when the cutting parameters are controlled. The characteristics of the machined parts are influenced by two types of parameters: the parameters of the cutting tool and the ones related to the cutting operation. The selection of these parameters is associated to the material of the work piece, and this selection is also linked to the machine tool. The suitable cutting parameters are determined based on experience or by use ofahandbookwhichdoesnotguarantee optimal performance. Hence, the proper selection of cutting tools and process parameters is essential to achieve the desired quality of the product with low manufacturing cost and higher productivity. Roughness plays an important role in determining how a real object will interact with its environments. Surface roughness is the critical quality indicator for machined surfaces,good quality turning surface can lead to improvement in strength properties suchasfatiguestrength, corrosion resistance, assembly tolerance, wear rate, coefficient of friction, thermal resistance and aesthetics etc. Turning is the removal of metal from the outer diameter of a rotating cylindrical work piece. Turningisusedtoreducethe diameter of the work piece, usually to a specified dimension, and to produce a smooth finish on the metal. Turning is the machining operation that producescylindrical parts. In its basic form, it can be defined as the machining of an external surface: With the work piece rotating. With a single-point cutting tool, and With the cutting tool feeding parallel to the axis of the work piece and at a distance that will remove the outer surface of the work. Fig. -1: Turning terminologies The desired shape, size and finished ferrous and non ferrous materials are conventionally produced through turning the preformed blanks with the help of cutting tools that moved past the work piece in a machine tool. 2. LITERATURE SURVEY A.EstevesCorreiaetal.(2011)analyzedtheeffectonsurface roughness in turning steel AISI1045 using wiper inserts. Finish machining with high feed rate conventional insets present high values of surface roughness when compared with wiper inserts. Consequently it is possible to get surface quality in workpiece of mechanics precision without cylindrical grinding operations. Süleyman Neseli et al. (2011), investigates the influence of tool geometry on the surfacefinishobtainedinturningofAISI 1040 steel and also find out the effect of tool geometry
- 2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1061 parameters on the surface roughness during turning, response surface methodology (RSM) was used and a prediction model was developed related to average surface roughness (Ra) using experimental data. A good agreement between the predicted and measuredsurface roughnesswas observed. Therefore, the developed model can be effectively used to predict the surface roughness on the machining of AISI 1040 steel within 95% confidence intervals ranges of parameters studied. Harsh Y Valera (2014) represented the Experimental Investigation of Surface Roughness and PowerConsumption in Turning Operation of EN 31 Alloy Steel. They analyzed the effect of three cutting parameters spindle speed, feed rate and depth of cut for surface roughness and power consumption. Sayak Mukherjeea(2014) investigated the Optimizationof Material Removal Rate during turning operation using Taguchi method and produced a predictive equation for determining MRR with a given set of parameters in CNC turning. The material selected for machining was SAE 1020 with carbidecutting tool. They used L25orthogonalarrayfor performing the experiment.The analysis showed that Depth of Cut had the most significant effect on MRR followed by Feed. P.Jayaraman, L.Mahesh Kumar (2014) used Orthogonal Array of Taguchi method coupled with grey relational analysis consideringthreeparametersviz.cuttingspeed,feed rate, depth of cut etc.For optimizing threeresponses:surface roughness, roundnessand material removalrateinprecision turning of AA6063 Aluminium Alloy. They concluded that feed rate is the most influencing factor in determining the multiple performancecharacteristicsorgreyrelationalgrade (GRG) followed by depth of cut and cutting speed. ShreemoyKumarNayak(2014)usedgreyrelationalanalysis to performed multi-objective optimization of Machining Parameters During Dry Turning of AISI 304 Austenitic Stainless Steel. They investigate the effect of cutting speed, feed and depth of cut on surface roughness,cutting force and material removal rate in turning of AISI 304 Austenitic Stainless Steel using uncoated carbide insert as cutting tool under dry condition. During this study, L27 orthogonalarray Taguchi design was used to study the influence of machining Parameters. R. Deepak Joel Johnson(2014) worked on Optimization of Cutting Parameters and FluidApplicationParametersduring Turning of OHNS Steel for surface roughness using taguchi technique. For this study Design of experiment with orthogonal L27 array has been used for conduct the experiment. The experimental study shows that Turning with minimal cutting fluid application improved the cutting performance by giving improved surface finish and also produced promisingresultswhencomparedwithdryturning and conventional wet turning. They also concluded that feed rate was having more influence on surface roughness and by tuning the fluid application parameters properly, surface roughness can be improved. Carmita Camposeco-Negrete(2014) employed the response surface methodology for optimizing the cutting process parameter for minimizing energy consumption and maximizing cutting quality in turning of AISI 6061 T6 aluminium. They revealed that Feed rate and depth of cut were the most significant factors for minimizing the total specific energy consumed, and , feed rate was the most significant factor for minimizing the surface roughness. Md.Maksudul Islam (2015) focused on to find out the optimal combination of process parameter in turning operation for ASTM A48 Grey Cast iron in Turning operation using Taguchimethodandanalysisofvariance(ANOVAs).For investigation they considered ASTM A48 grey cast iron as work piece and spindle speed, feed rateanddepthofcuthave been considered as cutting parameters, while as HSS (High Speed Steel) has been used as cutting tool. Experimental result showed that spindle speed has the most significant contribution on the materialremovalrateamongallthethree parameters. S.J. Raykar(2015) discussed the Multi-objective optimization of high speed turning of Al 7075 using grey relational analysis. The experiments were carried out on a CNC turning, using coated and uncoated carbide tool for the high speed turning of Al 7075.For this experimental study they considered surface roughness, power consumption, material removal rate and cutting time as response variable. From this analysis they concluded that Grey Relational Analysis is very effective technique for optimization of machining processes which involves multiple responses and also determined the optimized cutting parameter values for high speed turning of Al 7075 as 200 m/min of Speed, 0.1 mm/rev of feed rate and 0.5 mm of depth cut under dry machining conditions. G.M.SayeedAhmed(2015)highlightedtheeffectofFeedand Radial Force in Turning Process by using Taguchi Design Approach. They optimized the Feed and Radial forces and study the effects of process parameters in Lathe turning on Mild Steel work materialindryenvironmentconditionsusing HSS tool. The orthogonal array, signal to noise ratio and analysis of variance are employed to study the performance characteristics in Lathe machine turning operation. The result show that depth of cut, and cutting speed in affecting the variation of feed and radial forces are significantly larger as compared to the contribution of feed rate. Deepak (2015) investigatedthatAluminiumandaluminium alloys were vital to the aerospaceindustry.Theyarethegreat significance to other areas of transportation and building in which durability,strengthandlightweightarerequired.They analyzed the effects of feed rate, cutting speed and depth of cut on surface roughness in turning of Al 6061 alloy. Taguchi
- 3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1062 optimization method was employed in order to optimize the experimental result and the effect of each parameter on the obtained results was determined by use of analysis of variance (ANOVA).From the analysis, feedrateis foundtobe the most influential process parameters which influence the surface roughness followedbycuttingspeedanddepthofcut. Increase in feed rate and depth of cut is found to increase the surface roughness The effects of the feed rate, cutting speed and depth of cut on surface roughness, cutting temperature and MRR can be accessed after reviewing the literature. In order to optimize the experimental results, application of Design of Experiments (full factorialorpartial),Taguchi’sMethod,RSM etc. are sufficiently found in literature. The effects of each parameter on obtained results were determinedfordifferent materials, tools and machining operations. . 3. CONCLUSION Most of the study in turning operation is to reduce surface roughness during machining. Various methods, used to design experiments are suggested such as Factorial design, Taguchi and Response Surface Methodology. Low feed rate produces a good quality of surface as it is the most affecting parameter in decidingsurfaceroughness.Mostofthestudyto reduce surface roughness is done using considerable feed rate, not much work has been done on feed rates below 0.05 mm/rev. REFERENCES [1] A. Esteves Correia etal,Surfaceroughnessmeasurement in turning carbon steel AISI 1045 using wiper inserts. Measurement xxx (2011) xxx–xxx. [2] Suleyman Neseli, Suleyman Yaldız, Erol Turkes, Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44 (2011). [3] Harsh Y Valera, Sanket N Bhavsar “Experimental Investigation of Surface Roughness and Power Consumption in Turning Operation of EN 31AlloySteel” 2nd International Conference on Innovations in Automation and Mechatronics Engineering, ICIAME 2014. [4] Sayak Mukherjeea, Anurag Kamala, Kaushik Kumar “Optimization of Material Removal Rate DuringTurning of SAE 1020 Material in CNC Lathe using Taguchi Technique” 12th GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014. [5] P. Jayaraman, L.Mahesh kumar “Multi-response Optimization of Machining Parameters of Turning AA6063 T6 Aluminium Alloy using Grey Relational Analysis in Taguchi Method” 12th GLOBAl CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014. [6] Shreemoy Kumar Nayak, Jatin Kumar Patro, Shailesh Dewangan, Soumya Gangopadhyay “Multi-Objective Optimization of Machining Parameters During DryTurning of AISI 304 Austenitic Stainless Steel Using Grey Relational Analysis” 3rd International Conference on Materials Processing and Characterisation (ICMPC 2014). [7] R. Deepak Joel Johnson, K. Leo Dev Wins,, Anil Raj, B. Anuja Beatrice “Optimization of CuttingParametersand Fluid Application Parameters during Turning of OHNS Steel” 12th GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT, GCMM 2014. [8] Carmita Camposeco-Negrete“Optimization of cutting parameters using Response Surface Method for minimizing energyconsumptionandmaximizingcutting quality in turning of AISI 6061 T6 aluminum” Journal of Cleaner Production xxx (2014). [9] Md.Maksudul Islam ,Sayed Shafayat Hossain “Optimization of Metal Removal RateforASTMA48Grey Cast Iron in Turning Operation Using Taguchi Method” international journal of material science and engineering 2015. [10] S.J. Raykara,D.M.D'Addonab,A.M.Mane“Multi-objective optimization of high speed turning ofAl 7075usinggrey relational analysis” 9th CIRP Conference on Intelligent Computation in Manufacturing Engineering. [11] G.M.Sayeed Ahmed, S. Sibghatullah Hussaini Quadri,Md Sadiq Mohiuddin“OptimizationofFeedandRadial Force in Turning Process by using Taguchi Design Approach” 4th International Conference on Materials Processing and Characterisation (ICMPC 2015). [12] Choudhury, I.A., EI-Baradie, M.A., 1997, Surface roughness predictionintheturningofhighstrengthsteel by factorial design of experiments, Journal of Materials Processing Technology, Vol. 67, pp. 55-61. [13] Nalbant, M., Gokkaya, H., Sur, G., 2007, Application of Taguchi method in the optimization of cutting parameters for surface roughness in turning, Materials and Design Vol. 28, pp. 1379–1385. [14] Dr. S.S.Mahapatra, Amar Patnaik, Prabina Ku. Patnaik, Parametric Analysis and Optimization of Cutting Parameters for Turning Operations based on Taguchi Method. Proceedings of the International Conferenceon Global Manufacturing and Innovation - July 27-29, (2006), pp.1-9.