Numerical Modeling and Multi Objective Optimization of Face Milling of AISI 304 Steel

Document Type: Research Paper


Department of Mechanical Engineering, Amrita school of Engineering, Coimbatore, Amrtia Vishwa Vidyapeetham, India


There is a requirement to find accurate parameters to accomplish precise dimensional accuracy, excellent surface integrity and maximum MRR. This work studies the influence of various cutting parameters on output parameters like Cutting force, Surface roughness, Flatness, and Material removal rate while face milling. A detailed finite element model was developed to simulate the face milling process. The material constitutive behavior is described by Johnson-Cook material model and the damage criteria is established by Johnson-Cook damage model. The result indicate significant effects of all three cutting parameters on MRR and both feed rate and depth of cut have significant effect on cutting force. Also, feed rate has significant effect on PEEQ and none of the parameters have effect on flatness.


Main Subjects

[1] Kannan, S., Baskar, N., Sureshkumar, B., Selection of Machining Parameters in Face Milling Operations for Copper Work Piece Material Using Response Surface Methodology and Genetic Algorithm, All India Manufacturing Technology, Design and Research Conference, 2014.
[2] Prajapati, V., Thakkar, K., Thakkar, S., Parikh, H., Study and Investigate Effects of Cutting Parameters in CNC Milling process for Aluminium alloy-8011H14 through Taguchi design method, International Journal of Innovative Research in Science, Engineering and Technology, 2, 2013, 3271-3276.
[3] Yasir, M., Ginta, T., Ariwahj, B., Alkali, A., Danish, M., Effect of Spindle speed (N) and Feed rate (f) On Ra of AISI 316L SS Using End-Milling, ARPN Journal of Engineering and Applied Sciences, 11, 2016, 2496-2500.
[4] Shaik, J., Srinivas, J., Optimal selection of operating parameters in end milling of Al-6061 work materials using multi-objective approach, Mechanics of Advanced Materials and Modern Processes, 3(5), 2017, 1-11.
[5] Balasubramanyam, N., Gnana Prakash, P., Palvannan, V., Yugandhar, M., Effect of Machining Parameters on Ra of End Milling, International Journal for Research in Applied Science &Engineering Technology, 3(2), 2015, 1-10.
[6] Vijay, S., Krishnraj, V., Machining parameters optimization in End milling ofTi-6Al-4V, International Conference on Design and Manufacturing, 2013, 1079-1088.
[7] Rameshkumar, K., Krishnakumar, P., Ramachandran, K.I., Machine Learning Based Tool Condition classification using AE and Vibration data in a High Speed Milling Process Using Wavelet Features, Intelligent Decision Technologies: An International Journal, 12(1), 2018, 1-18.
[8] Kumar, A., Paswan, M.K., Optimization of Cutting Parameters Of AISI H13 with Multiple Performance Characteristics, International Journal of Mechanical Engineering and Robotics Research, 2(3), 2013, 45-54.
[9] Ninase, R.N., Khodke, P.M., Effect of Machining Parameters on Ra of Al-7075 Alloy in End Milling, International Research Journal of Engineering and Technology, 2(3), 2015, 1505-1508.
[10] Wathore, H.D., Adwani, P.S., Investigation of Optimum Cutting Parameters for End Milling of H13 Die Steel using Taguchi based Grey Relational Analysis, International Journal of Scientific Engineering and Applied Science, 1(4), 2015, 309-318.
[11] Kumbhar, A., Bhosale, R., Modi, A., Jadhav, S., Nippanikar, S., Kulkarni, A., Multi-objective Optimization of Machining Parameters in CNC End Milling of Stainless Steel 304, International Journal of Innovative Research in Science, Engineering and Technology, 4(9), 2015, 8419-8426.
[12] Marimuthu, K.P., Kumar, C.S.C., Prasada, H.P.T., Mathematical modelling to predict the residual stresses induced in milling process, International Journal of Mechanical and Production Engineering Research and Development, 8, 2018, 423-428.
[13] Rawangwong, S., Chatthonga, J., Boonchouytan, W., Burapa, R., An Investigation of Optimum Cutting Conditions in Face Milling Aluminum Semi Solid 2024 Using Carbide Tool, Energy Procedia, 34, 2013, 854-862.
[14] Rawang, S., Chatthong, J., Boonchouytan, W., Burapa, R., Influence of Cutting Parameters in Face Milling Semi-Solid AA 7075 Using Carbide Tool Affected the Ra and Tool Wear, 11th Eco-Energy and Materials Science and Engineering, 56, 2014, 448-457.
[15] Lakshmi, V., Subbaiah, K., Modelling and Optimization of Process Parameters during End Milling of Hardened Steel, International Journal of Engineering Research and Applications, 2, 2012, 674-679.
[16] Shelar, A., Shaikh, A., Optimization of CNC Milling Process by using Different Coatings- A Review, International Advanced Research Journal in Science, Engineering and Technology, 4(1), 2017, 143-147.
[17] Pramod, M., Reddy, N.V., Talluru, V., Reddy, Y.G., Marimuthu, K.P., Coupled temperature displacement model to predict residual stresses in milling process, IOP Conference Series: Materials Science and Engineering, 225, 2017.
[18] Subramanian, N., Veerapriya, C., Optimization of process parameters in milling process for AISI 4340 steel by Taguchi method, International Journal of Research and Engineering, Science and Technology, 1(1), 2015, 13-19.
[19] Sheth, S., George, P.M., Experimental Investigation and Prediction of Flatness and Ra during Face Milling Operation of WCB Material, Procedia Technology, 23, 2016, 344-351.
[20] Moaz, H.A., Basim, A., Khidhir, M.N.M., Ansari, B.M., FEM to predict the effect of feed rate (f) on Ra with cutting force (Fc) during face milling of titanium alloy, HBRC Journal, 9, 2013, 263-269.
[21] Krishna, K.P., Sripathi, J., Vijay, P., Ramachandran, K.I., Finite Element Modelling and Residual Stress Prediction in End Milling of Ti6Al4Valloy, IOP Conference Series: Materials Science and Engineering, 149, 2016, 012154.
[22] Ramesh, A., Sumesh, C.S., Abhilash, P.M., Rakesh, S., Finite element modelling of orthogonal machining of hard to machine materials, International Journal of Machining and Machinability of Materials, 17(6), 2015, 543-568.
[23] Sumesh, C.S., Ramesh, A., Numerical Modelling and Optimization of Dry Orthogonal Turning of Al6061 T6 Alloy, Periodica Polytechnica Mechanical Engineering, 62(3), 2018, 196-202.
[24] Reimer, A., Fitzpatrick, S., Luo, X.C., Zhao, J., Numerical Investigation of Mechanical Induced Stress during Precision End Milling Hardened Tool Steel, Solid State Phenomena, 261, 2017, 362-369.
[25] Ducobu, F., Arrazola, P.J.E., Rivière, L., Ortiz, G., de Zarate, A., Filippia, M.E., The CEL Method as an Alternative to the Current Modelling Approaches for Ti6Al4V Orthogonal Cutting Simulation, Procedia CIRP, 58, 2017, 245-250.
[26] Rezaei, H., Sadaghi, M.H., Budak, E., Determination of minimum uncut chip thickness under various machining condition during micro milling of Ti-6Al-4V, International Journal of Advanced Manufacturing Technology, 95(5-8), 2018, 1617-1634.
[27] Aydin, M., Koklu, U., Identification and modelling of cutting forces in ball end milling based on two different finite element models with Arbitrary Lagrangian Eulerian technique, International Journal of Advanced Manufacturing Technology, 92(1-4), 2017, 1465-1480.
[28] Peng, Z., Li, J., Yan, P., Gao, S., Zhang, C., Wang, X., Experimental and Simulation research on micro milling temperature and cutting deformations of heat resistance stainless steel, International Journal of Advanced Manufacturing Technology, 95(5-8), 2018, 2495-2508.
[29] Gao, Y., Ko, J.H., Lee, H.P., 3D coupled Eulerian Lagrangian finite element analysis of end milling, International Journal of Advanced Manufacturing Technology, 98(1-4), 2018, 849-857.
[30] Pittala, G.M., Monno, M., 3D finite element modelling of face milling of continuous chip material, International Journal of Advanced Manufacturing Technology, 47(5-8), 2010, 543-555.
[31] Krasauskas, P., Kilikevičius, S., Česnavičius, R., Pačenga, D., Experimental analysis and numerical simulation of the stainless AISI 304 steel friction drilling process, Mechanika, 20(6), 2014, 590-595.
[32] Pimenov, D.Y., Guzeev, V.I., Mathematical model of plowing forces to account for flank wear using FME modeling for orthogonal cutting scheme, International Journal of Advanced Manufacturing Technology, 89(9-12), 2017, 3149-3159.
[33] Pimenov, D.Y., Experimental research of face mill wear effect to flat surface roughness, Journal of Friction and Wear, 35(3), 2014, 250-254.
[34] Pimenov, D.Y., Guzeev, V.I., Krolczyk, G., Mia, M., Wojciechowski, S., Modeling flatness deviation in face milling considering angular movement of the machine tool system components and tool flank wear, Precision Engineering, 54, 2018, 327-337.
[35] Grzenda, M., Bustillo, A., Semi-supervised roughness prediction with partly unlabeled vibration data streams, Journal of Intelligent Manufacturing, 30(2), 2019, 933-945.
[36] Rodríguez, J.J., Quintana, G., Bustillo, A., Ciurana, J., A decision-making tool based on decision trees for roughness prediction in face milling, International Journal of Computer Integrated Manufacturing, 30(9), 2017, 943-957.
[37] Wojciechowski, S., Maruda, R.W., Nieslony, P., Krolczyk, G.M., Application of signal to noise ratio and grey relational analysis to minimize forces and vibrations during precise ball end milling, Precision Engineering, 51, 2018, 582-596.
[38] Wojciechowski, S., Wiackiewicz, M., Krolczyk, G.M., Study on metrological relations between instant tool displacements and surface roughness during precise ball end milling, Measurement, 129, 2018, 686-694.
[39] Wojciechowski, S., Maruda, R.W., Barranas, S., Nieslony, P., Krolczyk, G.M., Optimisation of machining parameters during ball end milling of hardened steel with various surface inclinations, Measurement, 111, 2017, 18-28.
[40] Chen, G., Ren, C., Yang, X., Jin, X., Guo, T., Finite Element Simulation of high speed machining of Titanium alloy (Ti-6Al-4V) based on ductile failure model, International Journal of Advanced Manufacturing Technology, 56, 2011, 1027-1038.
[41] Abaqus Inc. Analysis user’s manual, Version 6.6 USA, 2006.