DEVELOPMENT OF NUMERICAL MODELS FOR THE PREDICTION OF TEMPERATURE AND SURFACE ROUGHNESS DURING THE MACHINING OPERATION OF TITANIUM ALLOY (Ti6Al14V)
Keywords:ANN, algorithm, RSM, surface roughness, temperature
Temperature and surface roughness are important factors, which determine the degree of machinability and the performance of both the cutting tool and the work piece material. In this study, numerical models obtained from the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques were used for predicting the magnitude of the temperature and surface roughness during the machining operation of titanium alloy (Ti6Al4V). The design of the numerical experiment was carried out using the Response Surface Methodology (RSM) for the combination of the process parameters while the Artificial Neural Network (ANN) with 3 input layers, 10 sigmoid hidden neurons and 3 linear output neurons were employed for the prediction of the values of temperature. The ANN was iteratively trained using the Levenberg-Marquardt backpropagation algorithm. The physical experiments were carried out using a DMU80monoBLOCK Deckel Maho 5-axis CNC milling machine with a maximum spindle speed of 18 000 rpm. A carbide-cutting insert (RCKT1204MO-PM S40T) was used for the machining operation. A professional infrared video thermometer with an LCD display and camera function (MT 696) with infrared temperature range of −50−1000 °C, was employed for the temperature measurement while the surface roughness of the work pieces were measured using the Mitutoyo SJ – 201, surface roughness machine. The results obtained indicate that there is high degree of agreement between the values of temperature and surface roughness measured from the physical experiments and the predicted values obtained using the ANN and RSM. This signifies that the developed RSM and ANN models are highly suitable for predictive purposes. This work can find application in the production and manufacturing industries especially for the control, optimization and process monitoring of process parameters.
Mhamdi, M.B., Boujelbene, M., Bayraktar, E. and Zghal, A. Surface integrity of titanium alloy Ti-6Al-4 V in ball end-milling. Physics Procedia, 2012, 25: 355–362.
Koohestani, A., Mo, J. and Yang, S. Stability prediction of titanium milling with data driven reconstruction of phase-space. Machining Science and Technology, 2014, 18: 78–98.
Arrazola, P. J., Garay, A., Iriate, L. M., Armendia, M., Marya, S. and Le Maitre, L. “Machinability of Titanium Alloys (Ti6Al4V and Ti555.3)”. J. of Mater Process. Technol., 2009, 209:223-230.
Haghighi, S. E., Lu, H., Jian, G., Cao, G., Habibi, D. and Zhang, L. Eﬀect of α ″martensite on the microstructure and mechanical properties of beta-type Ti–Fe–Ta alloys, Mater. Des. 2015, 76:47–54.
Ehtemam-Haghighi, S., Liu, Y., Cao, G., and Zhang, L.-C. Inﬂuence of Nb on the β→ α″martensitic phase transformation and properties of the newly designed Ti–Fe–Nb alloys, Mater. Sci. Eng., 2016, 60:503–510.
Matsushita, T., Kokubo, T. and Matsuda, S. Eﬀect of pore size on bone ingrowth into porous titanium implants fabricated by additive manufacturing: an in vivo experiment, Mater. Sci. Eng. 2016, 59: 690–701.
Quintana, G. and Ciurana, J. Chatter in machining processes: A review. International Journal of Machine Tools & Manufacture, 2011, 51(5): 363–376.
Bandapalli, C., Sutaria, B.M. and Bhatt, D.V. High speed machining of Ti-alloys- A critical review. National Conference on Machines and Mechanisms, 2013, 1(16): 324–331.
Benedetti, M., Cazzolli, M., Fontanari, V. and Leoni, M. Fatigue limit of Ti6Al4V alloy produced by selective laser sintering, Procedia Structural Integrity , 2016, 2: 3158–3167.
Dixit, U. S., Joshi, S. N., and Davim, J. P. Incorporation of material behavior in modeling of metal forming and machining processes: a review, Mater. Des. 2011, 32:3655-3670.
Kara, F., Aslantas¸ K. and Cicek, A. Prediction of cutting temperature in orthogonal machining of AISI 316L using artiﬁcial neural network. Applied Soft Computing, 2016, 38: 64–74.
Lauro, C. H., Brandão, L. C. and Moni Ribeiro Filho, S. L. Monitoring the temperature of the milling process using infrared camera. Academic Journals, 2013 8(23), pp. 1112-1120,
Le Coz, G., Marinescu, M., Devillez, A., Dudzinski, D. and Velnom L. Measuring temperature of rotating cutting tools: Application to MQL drilling and dry milling of aerospace alloys. Appl. Theor. Eng., 2012, 36:434- 441.
Chen, J., Chandrashekhara, K., Mahimkar, C., Lekakh, S. N., and Richards, V. L. Void closure prediction in cold rolling using ﬁnite element analysis and neural network, J. of Mater. Process. Technol. 2011, 211:245–255.
Molinari, A., Cheriguene, R. and Miguelez, H. Numerical and analytical modeling of orthogonal cutting: the link between local variables and global contact characteristics, Int. J. Mech. Sci. 2011, 53:183–206.
Maiyar, L. M., Ramanujam, R., Venkatesan, K. and Jerald, J. Optimization of machining parameters for end-milling of Inconel 718 Super Alloy using Taguchi based Grey Relational Analysis. Procedia Engineering, 2013, 64:1276–1282.
Ravi, A. M., Murigendrappa, S. M., and Mukunda, P. G. Experimental investigation on thermally enhanced machining of high-chrome white cast iron and to study its machinability characteristics using Taguchi method and artiﬁcial neural network. Int. J. Adv. Manuf. Technol. 2014, 72(9-12):1439-1454.
Kannan, T. D. B., Kannan, G. R., Kumar, B. S. and Baskar, N. Application of artificial neural network modelling for machining parameter optimization in drilling operation. Procedia Material Science, 2014, 5:2242-2249.
Prajina, N. V. Multi response optimization of CNC end-milling using response surface methodology and desirability function. Int’l. J. of Eng. Res. and Techn., 2013, 6(6): 739-746.
Daniyan, I. A., Tlhabadira, I., Phokobye, S. N., Siviwe, M. and Mpofu, K. Modelling and optimization of the cutting forces during Ti6Al4V milling process using the response surface methodology and dynamometer. MM Science Journal, 2019, 128:3353-3363.
Saidi, R, Fathallah, B. B, Mabrouki, T., Belhadi, S., Yallese, M. A. (2019). Modeling and optimization of the turning parameters of cobalt alloy (Stellite 6) based on RSM and desirability function. Int J Adv Manuf Technol. 100(9 12):2945 68.
Xu, Y., Zhang, Q., Zhang, W. and Zhang, P. Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact. The International Journal of Advanced Manufacturing Technology, 2015, 76(9-12):2199-2208.
Kashyap, S. and Datta, D. Process parameter optimization of plastic injection molding: a review. International Journal of Plastics Technology, 2015, 19(1):1-18.
Jafari, M., Soroushian, S. and Khayati, G.R. Hardness optimization for al6061-mwcnt nanocomposite prepared by mechanical alloying using artificial neural networks and genetic algorithm. Journal of Ultrafine Grained and Nanostructured Materials, 2017, 50(1):23-32.
Altarazi, S., Ammouri, M. and Hijazi, A. Artificial neural network modeling to evaluate polyvinylchloride composites’ properties. Computational Materials Science, 2018, 153:1-9.
Tlhabadira, I., Daniyan, I. A., Machaka, R. , Machio, C ., Masu, L. and VanStaden L. R. Modelling and optimization of surface roughness during AISI P20 milling process using Taguchi method. Int. Journal of Advanced Manufacturing Technology, 2019, 102(9-12):3707-3718.
Kanta, G. and Sangwan, K. S. Predictive modelling and optimization of machining parameters to minimize surface roughness using artificial neural network coupled with genetic algorithm. Procedia CIRP, 2015, 31: 305 – 310.
Kovac, P., Rodic, D., Pucovsky, V., Savkovic, B. and Gostimirovic M. Application of fuzzy logic and regression analysis for modeling surface roughness in face milliing, J. Intell. Manuf. 2012, 24:755.
Campatelli, G., Lorenzini, L. and Scippa, A. Optimization of process parameters using a Response Surface Method for minimizing power consumption in the milling of carbon steel. J. Clean. Prod. 2014, 66:309-316.
Djavanroodi, F., Omranpour, B. and Sedighi, M. Artificial neural network modeling of ECAP process, Materials and Manufacturing Processes, 2013, 28:276–281.
Fathallah, B., Saidi, R., Dakhli, C., Belhadi, S. and Yallese, M. (2019). Mathematical modelling and optimization of surface quality and productivity in turning process of AISI 12L14 free-cutting Steel. International Journal of Industrial Engineering Computations,10(4):557 76.
U.S. Titanium Industry Inc. Titanium Alloys - Ti6Al4V Grade 5. AzoM, 2017. Retrieved on July 02, 2019 from https://www.azom.com/article.aspx?ArticleID=1547.
Daniyan, I. A., Tlhabadira, I. Daramola, O. O., Phokobye, S. N., Siviwe, M. and K. Mpofu (2020). Measurement and Optimization of Cutting Forces during MS 200TS Milling Process using the Response Surface Methodology and Dynamometer. Procedia CIRP, 88:288-293.
Daniyan, I. A., Fameso, F., Ale, F., Bello, K. and Tlhabadira, I. (2020). Modelling, simulation and experimental validation of the milling operation of titanium alloy (Ti6Al4V). The International Journal of Advanced Manufacturing Technology, 109(7):1853-1866.
Aggarwal, V., S.S. Khangura, and R. Garg. Parametric modeling and optimization for wire electrical discharge machining of Inconel 718 using response surface methodology. The International Journal of Advanced Manufacturing Technology, 2015, 79(1-4):31-47.
Vineela, M.G., Dave, A. and Chaganti, P.K. Artificial neural network based prediction of tensile strength of hybrid composites. Materials Today: Proceedings, 2018, 5(9):19908-19915.
Daniyan, I. A., Tlhabadira, I., Phokobye, S. N., Mrausi, S., Mpofu, K. and Masu, L. Modelling and Optimization of the Cutting Parameters for the Milling Operation of Titanium Alloy (Ti6Al4V). 2020 IEEE 11th International Conference on Mechanical and Intelligent Manufacturing Technologies (ICMIMT 2020), 2020, IEEE Xplore, pp. 68-73.
Kumar, N. S., Shetty, A., Shetty, A. Ananth, K. and Shetty, H. (2012). Effect of spindle speed and feed rate on the surface roughness of carbon steels in CNC turning. Procedia Engineering, 38:691-697.
Daniyan, I. A., Tlhabadira, I., Daramola, O. O. and Mpofu K. (2019). Design and Optimization of Machining Parameters for Effective AISI P20 Removal Rate during Milling Operation. Procedia CIRP 84:861–867.
Phokobye, S.N., Daniyan, I. A., Tlhabadira, I. A., Masu, L. and Van Staden, L. R. (2019). Model Design and Optimization of Carbide Milling Cutter for Milling Operation of M200 Tool Steel. Procedia CIRP 84:954–959.
Copyright (c) 2020 Ilesanmi Daniyan, Isaac Tlhabadira, JC Fwamba, DD Desai, Solomon Phokobye, Siviwe Mrausi, Khumbulani Mpofu
This work is licensed under a Creative Commons Attribution 4.0 International License.Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).