Research on Modeling and Optimization for Surface Roughness of Al6061 Spherical in Ultra-Precision Turning Based on Neural Network and Bee Colony Optimization Algorithm
Main Article Content
Abstract
The article focuses on developing a predictive and optimization model for surface roughness in Ultra-precision turning (UPT) using a diamond cutter on the spherical surface of Al6061 material. An experimental model with 30 experiments was established, considering three input parameters: spindle speed, feed rate, and depth of cut. The results from this model were collected to create an input dataset for an Artificial neural network (ANN) to build a surface roughness prediction model. The ANN structure 3-5-10-1 provided the best prediction results, with Coefficient of Determination (R2) was 0.98, Mean Absolute Percent Error (MAPE) was 13.36%, Mean Square Error (MSE) was 0.68, and Root Mean Square Error (RMSE) was 0.82. Additionally, the Artificial Bee Colony (ABC) algorithm was employed to determine the optimal cutting parameters that minimize surface roughness. The results indicated that the minimum roughness value achieved was 0.76 nm with the cutting parameters: spindle speed of 823 rev/min, feed rate of 13 mm/min, and depth of cut of 1 μm. Moreover, the effects of different cutting parameter combinations on surface roughness were analyzed and evaluated. The integration of the ANN model with the ABC algorithm enables a reliable prediction model for surface roughness and demonstrates high efficiency in optimizing the objective function. This research contributes valuable insights into surface roughness prediction and optimization in ultra-precision turning of Al6061 material. Furthermore, the proposed modeling and optimization approach can be extended to other materials and the processing of aspherical and diffractive surfaces.
Keywords
ABC, ANN, surface roughness, ultra-precision turning, spherical.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
https://doi.org/10.1016/j.jmsy.2024.03.008
[2] A. Gupta, A. Saini, N. Khatri, and A. Juyal, Review of single-point diamond turning process on IR optical materials, Materials Today: Proceedings, vol. 69, pp. 435-440, 2022.
https://doi.org/10.1016/j.matpr.2022.09.073
[3] W. Huang and J. Yan, Mechanisms of tool-workpiece interaction in ultraprecision DT of single-crystal SiC for curved microstructures, International Journal of Machine Tools and Manufacture, vol. 191, p. 104063, Oct. 2023.
https://doi.org/10.1016/j.ijmachtools.2023.104063
[4] Z. Liu, J. Wang, R. Leng, Xiaokun Wang, Min Zhang, Jing Wang, Mengxue Cai, Wenhan Li, Bin Liu, Lingzhong Li, Qiang Cheng, Longxiang Li, Xia Luo, and Xuejun Zhang, Impact of mirror local defects on system scattering in telescopes, Results in Physics, vol. 56, p. 107265, Jan. 2024.
https://doi.org/10.1016/j.rinp.2023.107265
[5] W. Gao, Soichi Ibaraki, M. Alkan Donmez, Daisuke Kono, J. R. Mayer, Y. L. Chen, Karoly Szipka, Andreas Archenti, J. M. Linares, Norikazu Suzuki, Machine tool calibration: measurement, modeling, and compensation of machine tool errors, International Journal of Machine Tools Manufacture, vol. 187, p. 104017, Apr. 2023.
https://doi.org/10.1016/j.ijmachtools.2023.104017
[6] K. A. E. Hossein and O. A. Olufayo, Diamond machining of rapidly solidified aluminium for optical mould inserts, Procedia Materials Science, vol. 6, pp. 1077-1082, 2014.
https://doi.org/10.1016/j.mspro.2014.07.178
[7] M. Mukaida and J. Yan, Ductile machining of single-crystal silicon for microlens arrays by ultraprecision diamond turning using a slow tool servo, International Journal of Machine Tools Manufacture, vol. 115, pp. 2-14, Apr. 2017. https://doi.org/10.1016/j.ijmachtools.2016.11.004
[8] M. Mia and N. R. Dhar, Prediction of surface roughness in hard turning under high pressure coolant using artificial neural network, Measurement, vol. 92, pp. 464-474, Oct. 2016.
https://doi.org/10.1016/j.measurement.2016.06.04
[9] M. M. Liman, K. A. E. Hossein, and P. B. Odedeyi, Modeling and prediction of surface roughness in ultra-high precision diamond turning of contact lens polymer using RSM and ANN methods, Materials Science Forum, vol. 928, pp. 139-143, Aug. 2018. https://doi.org/10.4028/www.scientific.net/MSF.928.139
[10] G. S. Khan, Characterization of surface roughness and shape deviations of aspheric surfaces, Ph.D. dissertation, University of Erlangen-Nuremberg, Germany, 2008.
[11] S. J. Zhang, S. To, S. J. Wang, and Z. W. Zhu, A review of surface roughness generation in ultra-precision machining, International Journal of Machine Tools Manufacture, vol. 91, pp. 76-95, Apr. 2015. https://doi.org/10.1016/j.ijmachtools.2015.02.001
[12] S. Hatefi and K. A. E. Hossein, Review of single-point diamond turning process in terms of ultra-precision optical surface roughness, The International Journal of Advanced Manufacturing Technology, vol. 106, pp. 2167-2187, Dec. 2019. https://doi.org/10.1007/s00170-019-04700-3
[13] L. H. Li, N. H. Yu, C. Y. Chan, and W. B. Lee, Al6061 surface roughness and optical reflectance when machined by single point diamond turning at a low feed rate, PLoS ONE 13(4): e0195083, Apr. 2018.
https://doi.org/10.1371/journal.pone.0195083
[14] W. Gao, H. Haitjema, F. Z. Fang, R. K. Leach, C. F. Cheung, E. Savio, J. M. Linares, On-machine and in-process surface metrology for precision manufacturing, CIRP Annals, vol. 68, iss. 2, pp. 843–866, 2019.
https://doi.org/10.1016/j.cirp.2019.05.005
[15] R. J. Bensingh, R. Machavaram, S. R. Boopathy, and C. Jebaraj, Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO), Measurement, vol. 134, pp. 359-374, Feb. 2019.
https://doi.org/10.1016/j.measurement.2018.10.066
[16] S. Roy, A. Khanra, S. Maity, R. K. Pal, and M. Maiti, GA-ABC hybridization for profit maximization of green 4DTSPs with discrete and continuous variables, Engineering Applications of Artificial Intelligence, vol. 123, part B, p. 106293, Aug. 2023. https://doi.org/10.1016/j.engappai.2023.106293
[17] M. R. Sankar, S. Saxena, S. R. Banik, I. M. Iqbal, R. Nath, L. J. Bora, K. K. Gajrani., Experimental study and artificial neural network modeling of machining with minimum quantity cutting fluid, Materials Today: Proceedings, vol. 18, pp. 4921-4931, 2019. https://doi.org/10.1016/j.matpr.2019.07.484
[18] L. Fan, Y. Ren, M. Tan, B. Wu, and L. Gao, GA-BP neural network-based nonlinear regression model for machining errors of compressor blades, Aerospace Science and Technology, vol. 151, p. 109256, Aug. 2024. https://doi.org/10.1016/j.ast.2024.109256
[19] Engineering Faculty, Computer Engineering Dapertment, Erciyes University, Kayseri, Turkey. [Online]. Available: http://mf.erciyes.edu.tr/abc/pub/tr06_2005.pdf [20] M. Aamir, M. T. Rad, K. Giasin, and A. Vafadar, Machinability of Al2024, Al6061, and Al5083 alloys using multi-hole simultaneous drilling approach, Journal of Materials Research Technology, vol. 9, iss. 5, pp. 10991-11002, Sep-Oct. 2020.