Prediction of Surface Roughness of Monel k 500 Super Alloy by Using Artificial Neural Network

Abstract:

The surface roughness is a feature that is of tremendous relevance in the assessment of cutting performance, and it plays an essential part in the manufacturing process as well. In this research, an effort was made to construct a model based on artificial neural networks to replicate the hard turning of Monel K 500 in dry conditions. The results of this endeavor are presented. This model is anticipated to accurately estimate the surface roughness for various cutting settings. Networks that use Scaled Conjugate Gradient (SCG) were trained using a set of training data for several cycles. Then they were tested with a collection of input/output data that was specifically reserved for this purpose. For each of the designs that were considered, the root mean square error was calculated. As compared with other models, the RMSE that the SCG Produces better value-. Analysis was done on the ability of the ANN model to predict surface roughness (Ra). It was discovered that the predictions produced by the ANN model had a high degree of congruence with the experiment’s findings.

• Periodical: Materials Science Forum (Volume 1098)
• Pages: 41-50
• DOI: https://doi.org/10.4028/p-QaGi3U
• Online since: September 2023
• Authors: V. Dilli Ganesh*, R.M Bommi
• Keywords: ANN, Monel K500, Prediction Error, Scaled Conjugate Gradient, Surface Roughness

References

[1] A. Kosarac, C. Mladjenovic, M. Zeljkovic, S. Tabakovic, and M. Knezev, "Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset," Materials (Basel)., vol. 15, no. 3, 2022.

DOI: 10.3390/ma15030700

[2] R. U. Baig, S. Javed, M. Khaisar, M. Shakoor, and P. Raja, "Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy," Adv. Mech. Eng., vol. 13, no. 6, p.1–14, 2021.

DOI: 10.1177/16878140211026720

[3] S. Dahbi, L. Ezzine, and H. EL Moussami, "Modeling of cutting performances in turning process using artificial neural networks," Int. J. Eng. Bus. Manag., vol. 9, p.1–13, 2017.

DOI: 10.1177/1847979017718988

[4] S. Lakshmana Kumar, M. Thenmozhi, R. M. Bommi, C. Ezilarasan, V. Sivaraman, and S. Palani, "Surface Roughness Evaluation in Turning of Nimonic C263 Super Alloy Using 2D DWT Histogram Equalization," J. Nanomater., vol. 2022, 2022.

DOI: 10.1155/2022/9378487

[5] Y. V. Deshpande, A. B. Andhare, and P. M. Padole, "Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718," SN Appl. Sci., vol. 1, no. 1, p.1–9, 2019.

DOI: 10.1007/s42452-018-0098-4

[6] F. Boukezzi, R. Noureddine, A. Benamar, and F. Noureddine, "Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network," Int. J. Ind. Syst. Eng., vol. 26, no. 4, p.567–583, 2017.

DOI: 10.1504/IJISE.2017.085227

[7] S. Al-Zubaidi, J. A. Ghani, and C. H. Che Haron, "Application of ANN in milling process: A review," Model. Simul. Eng., vol. 2011, 2011.

DOI: 10.1155/2011/696275

[8] V. Dilli Ganesh and R. M. Bommi, "'Prediction of Tool Wear by Using RGB Techniques in Comparison with Experimental Analysis,'" 2022 Int. Conf. Data Sci. Agents Artif. Intell. (ICDSAAI), Chennai, India, 2022, pp., 2022.

DOI: 10.1109/icdsaai55433.2022.10028899

[9] S. O. Sada and S. C. Ikpeseni, "Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance," Heliyon, vol. 7, no. 2, p. e06136, 2021.

DOI: 10.1016/j.heliyon.2021.e06136

[10] U. M. R. Paturi, S. Cheruku, V. P. K. Pasunuri, S. Salike, N. S. Reddy, and S. Cheruku, "Machine learning and statistical approach in modeling and optimization of surface roughness in wire electrical discharge machining," Mach. Learn. with Appl., vol. 6, no. May, p.100099, 2021.

DOI: 10.1016/j.mlwa.2021.100099

[11] R. M. Bommi, C. Ezilarasan, M. P. Sudeshkumar, and T. Vinoth, "Estimation of Flank Wear in Turning of Nimonic C263 Super Alloy Based on Novel MSER Algorithm and Deep Patten Network," Russ. J. Nondestruct. Test., vol. 58, no. 2, p.140–156, 2022.

DOI: 10.1134/S1061830922020073

[12] Y. M. Arisoy and T. Özel, "Machine learning based predictive modeling of machining induced microhardness and grain size in Ti-6Al-4V alloy," Mater. Manuf. Process., vol. 30, no. 4, p.425–433, 2015.

DOI: 10.1080/10426914.2014.961476

[13] A. H. Elsheikh et al., "Fine-tuned artificial intelligence model using pigeon optimizer for prediction of residual stresses during turning of Inconel 718," J. Mater. Res. Technol., vol. 15, p.3622–3634, 2021.

DOI: 10.1016/j.jmrt.2021.09.119

[14] B. Bhandari, "Comparative study of popular deep learning models for machining roughness classification using sound and force signals," Micromachines, vol. 12, no. 12, 2021.

DOI: 10.3390/mi12121484

[15] V. H. Nguyen et al., "Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate," Math. Probl. Eng., vol. 2021, 2021.

DOI: 10.1155/2021/6815802