This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Empirical and Artificial Neural Network Modeling of Laser Assisted Hybrid Machining Parameters of Inconel 718 Alloy
ISSN: 0148-7191, e-ISSN: 2688-3627
Published July 09, 2018 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Event: International Conference on Advances in Design, Materials, Manufacturing and Surface Engineering for Mobility
In the present paper, to predict the process relation between laser-assisted machining parameters and machinability characteristics, statistical models are formulated by employing surface response methodology along with artificial neural network. Machining parameters such as speed of cut; the rate of feed; along with the power of laser are taken as model input variables. For developing confidence limit in collected raw experimental data, the full factorial experimental design was applied to cutting force; surface roughness; along with flank wear. Response surface method (RSM) with the least square method is used to develop the theoretical equation. Furthermore, artificial neural network method has been done to model the laser-assisted machining process. Then, both the models (RSM and ANN) are compared for accuracy regarding root mean square error (RMSE); model predicted error (MPE) along with the coefficient of determination (R2). The results show that the ANN model estimates the machinability indices with high accuracy that provides a maximum precision benefit range of about 10% - 22% than RSM model.
CitationKannan, V. and Kannan, V., "Empirical and Artificial Neural Network Modeling of Laser Assisted Hybrid Machining Parameters of Inconel 718 Alloy," SAE Technical Paper 2018-28-0023, 2018, https://doi.org/10.4271/2018-28-0023.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
|[Unnamed Dataset 4]|
|[Unnamed Dataset 5]|
|[Unnamed Dataset 6]|
|[Unnamed Dataset 7]|
|[Unnamed Dataset 8]|
|[Unnamed Dataset 9]|
|[Unnamed Dataset 10]|
|[Unnamed Dataset 11]|
|[Unnamed Dataset 12]|
- Ezugwu, E.O. and Okeke, C.I. , “Behavior of Coated Carbide Tools in High-Speed Machining of a Nickel Base Alloy,” Tribology Transaction 1:122-126, 2002.
- Ulutan, D. and Ozel, T. , “Machining Induced Surface Integrity in Titanium and Nickel Alloys: A Review,” International Journal of Machine Tools and Manufacture 51(3):250-280, 2011 https://doi.org/10.1016/j.ijmachtools.2010.11.003.
- Thakur, A. and Gangopadhyay, S. , “State-of-the-Art in Surface Integrity in Machining of Nickel-Based Super Alloys,” International Journal of Machine Tools and Manufacture 100:25-54, 2016 https://doi.org/10.1016/j.ijmachtools.2015.10.001.
- Ezilarasan, C. and Velayudham, A. , “Effect of Machining Parameters on Surface Integrity in Machining Nimonic C-263 Super Alloy Using Whisker-Reinforced Ceramic Insert,” Journal of Materials Engineering and Performance 22(6):1619-1628, 2013, doi:10.1007/s11665-012-0439-1.
- Pawade, R.S., Joshi, S.S., and Brahmankar, P.K. , “Effect of Machining Parameters and Cutting Edge Geometry on Surface Integrity of High-Speed Turned Inconel 718,” International Journal of Machine Tools and Manufacture 48(1):5-28, 2008 https://doi.org/10.1016/j.ijmachtools.2007.08.004.
- Ezilarasan, C. Zhu, K., Velayudham, A., and Palanikumar, K., “Assessment of Factors Influencing Tool Wear on the Machining of Nimonic C-263 Alloy with PVD Coated Carbide Inserts,” in Advanced Materials Research, (Trans Tech Publications, 2011), vol. 291, 794-799 https://doi.org/10.4028/www.scientific.net/AMR.291-294.794.
- Bushlya, V., Zhou, J., and Ståhl, J.-E. , “Effect of Cutting Conditions on Machinability of Superalloy Inconel 718 during High Speed Turning with Coated and Uncoated PCBN Tools,” Procedia CIRP 3:370-375, 2012 https://doi.org/10.1016/j.procir.2012.07.064.
- Niaki, F.A. and Mears, L. , “A Comprehensive Study on the Effects of Tool Wear on Surface Roughness, Dimensional Integrity and Residual Stress in Turning IN718 Hard-to-Machine Alloy,” Journal of Manufacturing Processes 30:268-280, 2017 https://doi.org/10.1016/j.jmapro.2017.09.016.
- Bhatt, A., Attia, H., Vargas, R., and Thomson, V. , “Wear Mechanisms of WC Coated and Uncoated Tools in Finish Turning of Inconel 718,” Tribology International 43(5):1113-1121, 2010 https://doi.org/10.1016/j.triboint.2009.12.053.
- Venkatesan, K., Ramanujam, R., and Kuppan, P. , “Parametric Modeling and Optimization of Laser Scanning Parameters during Laser-Assisted Machining of Inconel 718,” Optics and Laser Technology 78:10-18, 2016 https://doi.org/10.1016/j.optlastec.2015.09.021.
- Navas, V.G., Arriola, I., Gonzalo, O., and Leunda, J. , “Mechanisms Involved in the Improvement of Inconel 718 Machinability by Laser-Assisted Machining (LAM),” International Journal of Advanced Manufacturing Technology 74:19-28, 2013 https://doi.org/10.1016/j.optlastec.2015.09.021.
- Anderson, M., Patwa, R., and Shin, Y.C. , “Laser-Assisted Machining of Inconel 718 with an Economic Analysis,” International Journal of Machine Tools and Manufacture 46:1879-1891, 2006 https://doi.org/10.1016/j.ijmachtools.2005.11.005.
- Attia, H., Tavakoli, S., Vargas, R., and Thomson, V. , “Laser-Assisted High-Speed Finish Turning of Superalloy Inconel 718 under Dry Conditions,” Annals of the CIRP 59:83-88, 2010 https://doi.org/10.1016/j.cirp.2010.03.093.
- Davoodi, B. and Eskandari, B. , “Tool Wear Mechanisms and Multi-Response Optimization of Tool Life and Volume of Material Removed in Turning of N-155 Iron-Nickel-Base Superalloy Using RSM,” Measurement 68:286-294, 2015 https://doi.org/10.1016/j.measurement.2015.03.006.
- Jafarian, F., Amirabadi, H., and Fattahi, M. , “Improving Surface Integrity in Finish Machining of Inconel 718 Alloy Using Intelligent Systems,” International Journal of Advanced Manufacturing Technology 71:817-827, 2014 https://doi.org/10.1007/s00170-013-5528-2.
- D’Addona, D., Segreto, T., Simeone, A., and Teti, R. , “ANN Tool Wear Modeling in the Machining of Nickel Superalloy Industrial Products,” CIRP Journal of Manufacturing Science and Technology 4:33-37, 2011 https://doi.org/10.1016/j.cirpj.2011.07.003.
- Niaki, F.A., Feng, L., Ulutan, D., and Mears, L. , “A Wavelet-Based Data-Driven Modelling for Tool Wear Assessment of Difficult to Machine Materials,” International Journal of Mechatronics and Manufacturing Systems 9(2):97-121, 2016 https://doi.org/10.1504/IJMMS.2016.076168.
- Sahoo, A., Rout, A., and Das, D. , “Response Surface and Artificial Neural Network Prediction Model and Optimization for Surface Roughness in Machining,” International Journal of Industrial Engineering Computations 6:229-240, 2015, doi:10.5267/j.ijiec.2014.11.001.
- Ranganathan, S., Senthilvelan, T., and Sriram, G. , “Evaluation of Machining Parameters of Hot Turning of Stainless Steel (Type 316) by Applying ANN and RSM,” Materials and manufacturing processes 25:1131-1141, 2010 https://doi.org/10.1080/10426914.2010.489790.
- Kurt, A. , “Modelling of the Cutting Tool Stresses in Machining of Inconel 718 Using Artificial Neural Networks,” Expert Systems with Applications 36:9645-9657, 2009 https://doi.org/10.1016/j.eswa.2008.12.054.
- Asiltürk, I. and Çunkas, M. , “Modeling, and Prediction of Surface Roughness in Turning Operations Using Artificial Neural Network and Multiple Regression Methods,” Expert Systems with Applications 38:5826-5832, 2011 https://doi.org/10.1016/j.eswa.2010.11.041.
- Sangwana, K.S., Saxenaa, S., and Kanta, G. , “Optimization of Machining Parameters to Minimize Surface Roughness Using Integrated ANN-GA Approach,” Procedia CIRP 29:305-310, 2015 https://doi.org/10.1016/j.procir.2015.02.002.
- Venkatesan, K. and Ramanujam, R. , “Improvement of Machinability Using Laser Aided Hybrid Machining for Inconel Alloy,” Materials and Manufacturing Process 3(4):1825-1835, 2016 https://doi.org/10.1080/10426914.2015.1117626.
- Box and Wilson , “On the Experimental Attainment of Optimum Conditions,” Journal of the Royal Statistical Society Series B 13(1):1-35, 1951.
- Tebassi, H., Yallese, M., Khettabi, R., Belhadi, S. et al. , “Multi-Objective Optimization of Surface Roughness, Cutting Forces, Productivity and Power Consumption When Turning of Inconel 718,” International Journal of Industrial Engineering Computations 7:111-134, 2016, doi:10.5267/j.ijiec.2015.7.003.
- Ramezani, M. and Afsari, A. , “Surface Roughness and Cutting Force Estimation in the CNC Turning Using Artificial Neural Networks,” Management Science Letters 5:357-362, 2015.
- Munoz-Escalona, P. and Maropoulos, P.G. , “Artificial Neural Networks for Surface Roughness Prediction When Face Milling Al 7075-T7351,” Journal of Materials Engineering and Performance 19(2):185-193, 2010 https://doi.org/10.1007/s11665-009-9452-4.
- Tebassi, H., Yallese, M.A., Meddour, I., Girardin, F., and Mabrouki, T. , “On the Modeling of Surface Roughness and Cutting Force When Turning of Inconel 718 Using Artificial Neural Network and Response Surface Methodology: Accuracy and Benefit,” Periodica Polytechnica. Engineering. Mechanical Engineering 61(1):1-10, 2017, doi:10.3311/PPme.8742.
- Sanjay, C. and Jyothi, C. , “A Study of Surface Roughness in Drilling Using Mathematical Analysis and Neural Networks,” International Journal of Advanced Manufacturing Technology 29:846-852, 2006 https://doi.org/10.1007/s00170-005-2538-8.
- Upadhyay, V., Jain, P.K., and Mehta, N.K. , “In-Process Prediction of Surface Roughness in Turning of Ti-6Al-4V Alloy Using Cutting Parameters and Vibration Signals,” Measurement 46(1):154-160, 2013 https://doi.org/10.1016/j.measurement.2012.06.002.
- Yazdi, M.S. and Khorram, A. , “Modeling and Optimization of Milling Process by using RSM and ANN Methods,” IACSIT International Journal of Engineering and Technology 2:474-380, 2010.
- Mia, M., Khan, M.A., and Dhar, N.R. , “Study of Surface Roughness and Cutting Forces Using ANN, RSM, and ANOVA in Turning of Ti-6Al-4V under Cryogenic Jets Applied at Flank and Rake Faces of Coated WC Tool,” The International Journal of Advanced Manufacturing Technology 93(1-4):975-991, 2017, doi:10.1007/s00170-017-0566-9.