Abstract
In machining industries, sustainable production activities are reduced because of the tool wear effect. The machining input parameters are mainly responsible for the effect of flank wear attained in the tool and the workpiece's surface quality. This work aims to determine and predict the machining performances of EN2-BS970/Mild steel by varying the input parameters such as cutting speed (V), feed (F), nose radius (r), and depth of cut (d). The hybrid Deep Convolutional Neural Network-based Manta-Ray Optimization (DCNN-MRO) is used to predict the machining outcomes, and it is performed in Matlab software version 2020a. The input machining parameters are designed by response surface methodology of box behnken design performed in Design-Expert software version 11. The experimented different cutting forces are feed force (Fx), radial force (Fy), cutting force (Fz), and the machining performances are tool flank wear, surface roughness, and Tool chip thickness. In which, the machining input parameter, namely cutting speed effectively influences the turning outcomes. The effect of tool flank wear and surface roughness by varying the cutting forces are also analyzed. The observed optimal surface roughness is 3.105 \(\mu m\), tool wear rate is 0.139mm, and tool chip thickness is 0.11mm. The measured outcomes are closer to the predicted outcomes obtained from hybrid DCNN. The average RMSE obtained from the proposed DCNN-MRFO is 0.03, and the non-hybrid DCNN is 0.3.
Similar content being viewed by others
Data Availability
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
H. Aouici, M.A. Yallese, B. Fnides and K. Chaoui, Modelling Optimization of Hard Turning of x38CrMoV5-1 Steel with CBN Tool Machining Parameters Effects on Flank Wear and Surface Roughness, J. Mech. Sci. Technol., 2011, 25(11), p 2843–2851.
S. Kumar, and B. Singh, A New Approach to Explore Tool Chatter in Turning Operation on the Lathe. Australian J. Mech. Eng. 1–20 (2019)
S. Dinesh, V. Vijayan, A. Parthiban, C. Saravanan, and B.S. Kumar, Modeling and optimization of machining parameters for turning of mild steel using single-point cutting tool made of P20 tool steel, In Advances in Industrial Automation and Smart Manufacturing. Springer, Singapore, 2021, p. 285–295.
M. Rafighi, M. Özdemir, S. Al Shehabi, et al., Sustainable Hard Turning of High Chromium AISI D2 Tool Steel Using CBN and Ceramic Inserts. Trans. Indian Inst. Met. 2021, 74, p 1639–1653.
J. Rajaparthiban, M. Ravichandran, B. Stalin, P.R. Kumar and V. Mohanavel, Machining of EN31 Steel Using Carbide Insert–A Statistical Approach, Materials Today: Proceedings, 2020, 22, p 2559–2564.
K. Arunkarthikeyan and K. Balamurugan, Experimental studies on deep cryo treated plus tempered tungsten carbide inserts in turning operation. In Advances in industrial automation and smart manufacturing. Springer, Singapore, 2021. 313-323.
A. Das, M. Kamal, S.R. Das, S.K. Patel, A. Panda, M. Rafighi and B.B. Biswal, Comparative assessment between AlTiN and AlTiSiN coated carbide tools towards machinability improvement of AISI D6 steel in dry hard turning. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2021, p.09544062211037373.
M.E. Korkmaz, N. Yaşar and M. Günay, Numerical and Experimental Investigation of Cutting Forces in Turning of Nimonic 80A Superalloy, Eng. Sci. Technol. Int. J., 2020, 23(3), p 664–673.
L.B. Abhang and M. Hameedullah, Modeling and analysis of tool wear based on cutting force and chip-tool interface temperatures in turning, Advanced manufacturing and materials science. Springer, Cham, 2018, p 411–420
I.P. Okokpujie, O.S. Ohunakin, C.A. Bolu and K.O. Okokpujie, Experimental Data-Set for Prediction of Tool Wear During Turning of Al-1061 Alloy by High Speed Steel Cutting Tools, Data Brief, 2018, 18, p 1196–1203.
A.B. Bijithmon and N.G. Smitha, Optimization of Surface Roughness of EN24T Steel Using Genetic Algorithm in Turning Operation, Int. J. Eng. Res. Technol., 2017, 6(5), p 504–510.
N.C. Ghuge and A.M. Mahalle, Experimental Investigation on the Performance of Soyabean Oil and Blassocut-4000 During Turning of AISI in Terms of Cutting Forces, Int. J. Scientific Res. Sci. Eng. Technol. (IJSRSET), 2016, 2(3), p 330–333.
Z. Zhu, S. To, W.L. Zhu, P. Huang and X. Zhou, Cutting Forces in Fast-/Slow Tool Servo Diamond Turning of Micro-Structured Surfaces, Int. J. Mach. Tools Manuf, 2019, 136, p 62–75.
Y. Wei, M.R. Kim, D.W. Lee, C. Park and S.S. Park, Effects of Micro Textured Sapphire Tool Regarding Cutting Forces in Turning Operations, Int. J. Precis.Eng. Manuf. Green Technol., 2017, 4(2), p 141–147.
M. Dumas, G. Kermouche, F. Valiorgue, A.V. Robaeys, F. Lefebvre, A. Brosse, H. Karaouni and J. Rech, Turning-Induced Surface Integrity for a Fillet Radius in a 316L Austenitic Stainless Steel, J. Manuf. Process., 2021, 68, p 222–230.
R.A. Laghari, J. Li, Z. Xie and S.Q. Wang, Modeling and Optimization of Tool Wear and Surface Roughness in Turning of Al/Sicp Using Response Surface Methodology, 3D Res., 2018, 9(4), p 46.
M.D. Selvam, P. Senthil and N.M. Sivaram, Parametric Optimization for Surface Roughness of AISI 4340 Steel During Turning Under Near Dry Machining Condition, Int. J. Mach. Mach. Mater., 2017, 19(6), p 554–569.
S.S. Babu and B.K. Vinayagam, Surface Roughness Prediction Model Using Adaptive Particle Swarm Optimization (APSO) Algorithm, J. Intell. Fuzzy Syst., 2015, 28(1), p 345–360.
A. Şahinoğlu and M. Rafighi, Investigation of Vibration, Sound Intensity, Machine Current and Surface Roughness Values of AISI 4140 During Machining on the Lathe, Arab J Sci Eng, 2020, 45, p 765–778.
S. Atla and M.S. Surya, Influence of Cutting Fluids on Tool Wear and Surface Roughness During Turning of Aisi 316 Austenitic Stainless Steel, IJERT, 2017, 6(07), p 112–115.
M. Mia and N.R. Dhar, Optimization of Surface Roughness and Cutting Temperature in Highpressure Coolant-Assisted Hard Turning Using Taguchi Method, Int. J. Adv. Manuf. Technol., 2017, 88(1–4), p 739–753.
A. Şahinoğlu and M. Rafighi, Optimization of Cutting Parameters with Respect to Roughness for Machining of Hardened AISI 1040 Steel, Mater. Test., 2020, 62(1), p 85–95.
F. Bayraktar and F. Kara, Investigation of the Effect on Surface Roughness of Cryogenic Process Applied to Cutting Tool, Int. J. Anal. Exp. Finite Element Anal., 2020, 7(2), p 19–27.
A. Das, S.K. Patel, B.B. Biswal, N. Sahoo and A. Pradhan, Performance Evaluation of Various Cutting Fluids Using MQL Technique in Hard Turning of AISI 4340 Alloy Steel, Measurement, 2020, 150, p 107079.
A.A. Selaimia, H. Bensouilah, M.A. Yallese and I.K. Meddour, Modeling and optimization in Dry Face Milling of X2CrNi18-9 Austenitic Stainless Steel Using RMS and Desirability Approach, Measurement, 2017, 107, p 53–67.
F. Kara, Optimization of Cutting Parameters in Finishing Milling of Hardox 400 Steel, Int. J. Anal. Exp. Finite Element Anal., 2018, 5(3), p 44–49.
B.S. Prasad and M.P. Babu, Correlation Between Vibration Amplitude and Tool Wear in Turning: Numerical and Experimental Analysis, Eng. Sci. Technol. Int. J., 2017, 20(1), p 197–211.
D.S.C. Kishore, K.P. Rao and A. Mahamani, Investigation of Cutting Force, Surface Roughness and Flank Wear in Turning of In-situ Al6061-TiC Metal Matrix Composite, Procedia Mater. Sci., 2014, 6, p 1040–1050.
M. Kuntoğlu and H. Sağlam, Investigation of Progressive Tool Wear for Determining of Optimized Machining Parameters in Turning, Measurement, 2019, 140, p 427–436.
D. Manivel and R. Gandhinathan, Optimization of Surface Roughness and Tool Wear in Hard Turning of Austempered Ductile Iron (Grade 3) Using Taguchi Method, Measurement, 2016, 93, p 108–116.
M. Mia, P.R. Dey, M.S. Hossain, M.T. Arafat, M. Asaduzzaman, M.S. Ullah and S.T. Zobaer, Taguchi S/N Based Optimization of Machining Parameters for Surface Roughness, Tool Wear and Material Removal Rate in Hard Turning Under MQL Cutting Condition, Measurement, 2018, 122, p 380–391.
E.O. Ezugwua, D.A. Fadera, J. Bonneya, R.B. Da Silva and W.F. Salesa, Modelling the Correation between Cutting and Process Parameters in High Speed Machining of Inconel 718 Alloy Using an Artificial Neural Network, Int. J. Mach. Tools Manuf., 2005, 45, p 1375–1385.
J. Senveter, S. Klancnik, J. Balic and F. Cus, Prediction of Surface Roughness Using A Feed-Forward Neural Network, Manag. Prod. Eng. Rev., 2010, 1(2), p 47–55.
T. Sk and S. Shankar, Tool wear prediction in hard turning of EN8 steel using cutting force and surface roughness with artificial neural network, Proc. Inst. Mech. Eng. C J. Mech. Eng. Sci., 2020, 234(1), p 329–342.
A. Salimi, A. Erdem and M. Rafighi, Applying a Multi Sensor System to Predict and Simulate the Tool Wear Using of Artificial Neural Networks, Scientia Iranica, 2017, 24(6), p 2864–2874.
S.O. Sada, Improving the Predictive Accuracy of Artificial Neural Network (ANN) Approach in a Mild Steel Turning Operation, Int. J. Adv. Manuf. Technol., 2021, 112, p 2389–2398
G.S. Babu, P. Zhao and X.L. Li, Deep convolutional neural network based regression approach for estimation of remaining useful life, In International conference on database systems for advanced applications Springer, Cham. 2016 April, p 214-228
Ö. Erkan, B. Işık, A. Çiçek and F. Kara, Prediction of Damage Factor in End Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network, Appl. Compos. Mater., 2013, 20(4), p 517–536.
W. Zhao, Z. Zhang and L. Wang, Manta Ray Foraging Optimization: An Effective Bio-Inspired Optimizer for Engineering Applications, Eng. Appl. Artif. Intell., 2020, 87, p 103300.
B. Sheng, T. Pan, Y. Luo and K. Jermsittiparsert, System Identification of the PEMFCs based on Balanced Manta-Ray Foraging Optimization algorithm, Energy Rep., 2020, 6, p 2887–2896.
F. Kara, K. Aslantas and A. Çiçek, ANN and Multiple Regression Method-Based Modelling of Cutting Forces in Orthogonal Machining of AISI 316L Stainless Steel, Neural Comput. Appl., 2015, 26(1), p 237–250.
Funding
No funding is provided for the preparation of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors *1Thangavel Palaniappan, 2Prakasam Subramaniam declared that they have no conflict of interest.
Ethical Standards
This article does not contain any studies with human participants or animals performed by any of the authors.
Consent to Participate
All the authors involved have agreed to participate in this submitted article.
Consent for Publication
All the authors involved in this manuscript give full consent for publication of this submitted article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Palaniappan, T., Subramaniam, P. Experimental Investigation and Prediction of Mild Steel Turning Performances Using Hybrid Deep Convolutional Neural Network-Based Manta-Ray Foraging Optimizer. J. of Materi Eng and Perform 31, 4848–4863 (2022). https://doi.org/10.1007/s11665-021-06552-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11665-021-06552-z