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Cutting force prediction in ultrasonic-assisted milling of Ti–6Al–4V with different machining conditions using artificial neural network

Published online by Cambridge University Press:  11 September 2020

Ramazan Hakkı Namlu Open the ORCID record for Ramazan Hakkı Namlu [Opens in a new window] ,
Cihan Turhan ,
Bahram Lotfi Sadigh  and
S. Engin Kılıç
Ramazan Hakkı Namlu*
Affiliation:
Manufacturing Engineering Department, Atılım University, Ankara, Turkey
Cihan Turhan
Affiliation:
Energy Systems Engineering Department, Atılım University, Ankara, Turkey
Bahram Lotfi Sadigh
Affiliation:
Manufacturing Engineering Department, Atılım University, Ankara, Turkey
S. Engin Kılıç
Affiliation:
Manufacturing Engineering Department, Atılım University, Ankara, Turkey
*
Author for correspondence: Ramazan Hakkı Namlu, E-mail: [email protected]
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Abstract

Ti–6Al–4V alloy has superior material properties such as high strength-to-weight ratio, good corrosion resistance, and excellent fracture toughness. Therefore, it is widely used in aerospace, medical, and automotive industries where machining is an essential process for these industries. However, machining of Ti–6Al–4V is a material with extremely low machinability characteristics; thus, conventional machining methods are not appropriate to machine such materials. Ultrasonic-assisted machining (UAM) is a novel hybrid machining method which has numerous advantages over conventional machining processes. In addition, minimum quantity lubrication (MQL) is an alternative type of metal cutting fluid application that is being used instead of conventional lubrication in machining. One of the parameters which could be used to measure the performance of the machining process is the amount of cutting force. Nevertheless, there is a number of limited studies to compare the changes in cutting forces by using UAM and MQL together which are time-consuming and not cost-effective. Artificial neural network (ANN) is an alternative method that may eliminate the limitations mentioned above by estimating the outputs with the limited number of data. In this study, a model was developed and coded in Python programming environment in order to predict cutting forces using ANN. The results showed that experimental cutting forces were estimated with a successful prediction rate of 0.99 with mean absolute percentage error and mean squared error of 1.85% and 13.1, respectively. Moreover, considering too limited experimental data, ANN provided acceptable results in a cost- and time-effective way.

Keywords

Artificial neural networkcutting forceminimum quantity lubricationultrasonic-assisted milling
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 1 , February 2021 , pp. 37 - 48
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Abootorabi Zarchi, MM, Razfar, MR and Abdullah, A (2012) Investigation of the effect of cutting speed and vibration amplitude on cutting forces in ultrasonic-assisted milling. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture 226, 1185–1191.CrossRefGoogle Scholar
Altintas, Y (2012) Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design. New York, NY: Cambridge University Press.Google Scholar
Boyer, R, Welsch, G and Collings, EW (1994) Materials Properties Handbook: Titanium Alloys. Materials Park, OH: ASM International.Google Scholar
Brehl, DE and Dow, TA (2008) Review of vibration-assisted machining. Precision Engineering 32, 153–172.CrossRefGoogle Scholar
Cai, XJ, Liu, ZQ, Chen, M and An, QL (2012) An experimental investigation on effects of minimum quantity lubrication oil supply rate in high-speed end milling of Ti–6Al–4V. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture 226, 1784–1792.CrossRefGoogle Scholar
Chetan, BC, Ghosh, S and Rao, PV (2016) Application of nanofluids during minimum quantity lubrication: a case study in turning process. Tribology International 101, 234–246.CrossRefGoogle Scholar
D'Addona, D, Segreto, T, Simeone, A and Teti, R (2011) ANN tool wear modelling in the machining of nickel superalloy industrial products. CIRP Journal of Manufacturing Science and Technology 4, 33–37.CrossRefGoogle Scholar
Das, B, Roy, S, Rai, RN and Saha, SC (2016) Study on machinability of in situ Al–4.5%Cu–TiC metal matrix composite-surface finish, cutting force prediction using ANN. CIRP Journal of Manufacturing Science and Technology 12, 67–78.Google Scholar
Debnath, S, Reddy, MM and Yi, QS (2014) Environmental friendly cutting fluids and cooling techniques in machining: a review. Journal of Cleaner Production 83, 33–47.CrossRefGoogle Scholar
Ezugwu, EO, Bonney, J and Yamane, Y (2003) An overview of the machinability of aeroengine alloys. Journal of Materials Processing Technology 134, 233–253.CrossRefGoogle Scholar
Ezugwu, EO, Fadare, DA, Bonney, J, Da Silva, RB and Sales, WF (2005) Modelling the correlation between cutting and process parameters in high-speed machining of Inconel 718 alloy using an artificial neural network. International Journal of Machine Tools and Manufacture 45, 1375–1385.CrossRefGoogle Scholar
Fredj, NB and Amamou, R (2006) Ground surface roughness prediction based upon experimental design and neural network models. The International Journal of Advanced Manufacturing Technology 31, 24–36.CrossRefGoogle Scholar
Groover, MP (2019) Automation, Production Systems, and Computer-Integrated Manufacturing. New York, NY: Pearson Education Inc.Google Scholar
Kahles, JF, Field, M, Eylon, D and Froes, FH (1985) Machining of titanium alloys. Journal of Metals 37, 27–35.Google Scholar
Kalla, D, Sheikh-Ahmad, J and Twomey, J (2010) Prediction of cutting forces in helical end milling fiber reinforced polymers. International Journal of Machine Tools and Manufacture 50, 882–891.CrossRefGoogle Scholar
Kalpakjian, S and Schmit, SR (2009) Manufacturing Engineering and Technology, 6th Edn. Upper Saddle River, NJ: Pearson/Prentice Hall.Google Scholar
Karabulut, Ş (2015) Optimization of surface roughness and cutting force during AA7039/Al2O3 metal matrix composites milling using neural networks and Taguchi method. Measurement 66, 139–149.CrossRefGoogle Scholar
Kramar, D, Cica, D, Sredanovic, B and Kopac, J (2015) Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 96–106.CrossRefGoogle Scholar
Lewis, CD (1982) International and Business Forecasting Methods. London: Butterworths.Google Scholar
Li, KM and Wang, SL (2013) Effect of tool wear in ultrasonic vibration-assisted micro-milling. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture 228, 847–855.CrossRefGoogle Scholar
Liu, ZQ, Cai, XJ, Chen, M and An, QL (2011) Investigation of cutting force and temperature of end-milling Ti–6Al–4V with different minimum quantity lubrication (MQL) parameters. Proceedings of the Institution of Mechanical Engineers. Part B: Journal of Engineering Manufacture 225, 1273–1279.CrossRefGoogle Scholar
Lu, Z, Zhang, D, Zhang, X and Peng, Z (2020) Effects of high-pressure coolant on cutting performance of high-speed ultrasonic vibration cutting titanium alloy. Journal of Materials Processing Technology 279, No. 116584.CrossRefGoogle Scholar
Markopoulos, AP, Manolakos, DE and Vaxevanidis, NM (2008) Artificial neural network models for the prediction of surface roughness in electrical discharge machining. Journal of Intelligent Manufacturing 19, 283–292.Google Scholar
Maurotto, A, Muhammad, R, Roy, A and Silberschmidt, VV (2013) Enhanced ultrasonically assisted turning of a β-titanium alloy. Ultrasonics 53, 1242–1250.CrossRefGoogle ScholarPubMed
Nath, C and Rahman, M (2008) Effect of machining parameters in ultrasonic vibration cutting. International Journal of Machine Tools and Manufacture 48, 965–974.CrossRefGoogle Scholar
Ni, C, Zhu, L, Liu, C and Yang, Z (2018) Analytical modeling of tool-workpiece contact rate and experimental study in ultrasonic vibration-assisted milling of Ti–6Al–4V. International Journal of Mechanical Sciences 142–143, 97–111.CrossRefGoogle Scholar
Özel, T and Karpat, Y (2005) Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks. International Journal of Machine Tools and Manufacture 45, 467–479.CrossRefGoogle Scholar
Pourmostaghimi, V, Zadshakoyan, M and Badamchizadeh, MA (2020) Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 26.1725–1730.Google Scholar
Pujana, J, Rivero, A, Celaya, A and López de Lacalle, LN (2009) Analysis of ultrasonic-assisted drilling of Ti6Al4V. International Journal of Machine Tools and Manufacture 49, 500–508.CrossRefGoogle Scholar
Quintana, G, Garcia-Romeu, ML and Ciurana, J (2009) Surface roughness monitoring application based on artificial neural networks for ball-end milling operations. Journal of Intelligent Manufacturing 22, 607–617.CrossRefGoogle Scholar
Radhakrishnan, T and Nandan, U (2005) Milling force prediction using regression and neural networks. Journal of Intelligent Manufacturing 16, 93–102.CrossRefGoogle Scholar
Rao, GKM, Rangajanardhaa, G, Rao, DH and Rao, MS (2009) Development of hybrid model and optimization of surface roughness in electric discharge machining using artificial neural networks and genetic algorithm. Journal of Materials Processing Technology 209, 1512–1520.Google Scholar
Rugen, P and Callahan, B (1996) An overview of Monte Carlo – a fifty year perspective. Human and Ecological Risk Assessment 2, 671–680.CrossRefGoogle Scholar
Sadeghi, MH, Haddad, MJ, Tawakoli, T and Emami, M (2008) Minimal quantity lubrication-MQL in grinding of Ti–6Al–4V titanium alloy. The International Journal of Advanced Manufacturing Technology 44, 487–500.CrossRefGoogle Scholar
Shabgard, MR and Alenabi, H (2015) Ultrasonic assisted electrical discharge machining of Ti–6Al–4V alloy. Materials and Manufacturing Processes 30, 991–1000.CrossRefGoogle Scholar
Sharma, VS, Dhiman, S, Sehgal, R and Sharma, SK (2008) Estimation of cutting forces and surface roughness for hard turning using neural networks. Journal of Intelligent Manufacturing 19, 473–483.CrossRefGoogle Scholar
Sun, J, Wong, YS, Rahman, M, Wang, ZG, Neo, KS, Tan, CH and Onozuka, H (2006) Effects of coolant supply methods and cutting conditions on tool life in end milling titanium alloy. Machining Science and Technology 10, 355–370.CrossRefGoogle Scholar
Szecsi, T (1999) Cutting force modeling using artificial neural networks. Journal of Materials Processing Technology 92–93, 344–349.CrossRefGoogle Scholar
Tai, BL, Stephenson, DA, Furness, RJ and Shih, AJ (2014) Minimum quantity lubrication (MQL) in automotive powertrain machining. Procedia CIRP 14, 523–528.CrossRefGoogle Scholar
Tandon, V and El-Mounayri, H (2001) A novel artificial neural networks force model for end milling. The International Journal of Advanced Manufacturing Technology 18, 693–700.CrossRefGoogle Scholar
Tao, G, Ma, C, Shen, X and Zhang, J (2016) Experimental and modeling study on cutting forces of feed direction ultrasonic vibration-assisted milling. The International Journal of Advanced Manufacturing Technology 90, 709–715.CrossRefGoogle Scholar
Tschätsch, H and Reichelt, A (2009) Cutting fluids (coolants and lubricants). In Tschatsch, H (ed.), Applied Machining Technology. Berlin, Heidelberg: Springer, pp. 349–352.Google Scholar
Turhan, C, Kazanasmaz, T, Uygun, IE, Ekmen, KE and Akkurt, GG (2014) Comparative study of a building energy performance software (KEP-IYTE-ESS) and ANN-based building heat load estimation. Energy and Buildings 85, 115–125.Google Scholar
Turhan, C, Kazanasmaz, T and Akkurt, GG (2017) Performance indices of soft computing models to predict the heat load of buildings in terms of architectural indicators. Journal of Thermal Engineering 3, 1358–1374.Google Scholar
Wong, SV and Hamouda, AMS (2003) Machinability data representation with artificial neural network. Journal of Materials Processing Technology 138, 538–544.CrossRefGoogle Scholar
Yadav, AK and Chandel, SS (2014) Solar radiation prediction using Artificial Neural Network techniques: a review. Renewable and Sustainable Energy Reviews 33, 772–781.CrossRefGoogle Scholar
Yang, X and Liu, CR (1999) Machining titanium and its alloys. Machining Science and Technology 3, 107–139.CrossRefGoogle Scholar
Zeilmann, RP and Weingaertner, WL (2006) Analysis of temperature during drilling of Ti6Al4V with minimal quantity of lubricant. Journal of Materials Processing Technology 179, 124–127.CrossRefGoogle Scholar
Zerti, A, Yallese, MA, Zerti, O, Nouioua, M and Khettabi, R (2019) Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420. Proceedings of the Institution of Mechanical Engineers. Part C: Journal of Mechanical Engineering Science 223, 4439–4462.Google Scholar