Open Access
Yogesh Navnath Jagdale
Mandar Kirti Tambat
Krushna Bhagvant Nemlekar
Aditya Chandrashekhar Savekar
Nalini Deepthi
- Students Saraswati College of Engineering Navi Mumbai, Maharashtra India
- Students Saraswati College of Engineering Navi Mumbai, Maharashtra India
- Students Saraswati College of Engineering Navi Mumbai, Maharashtra India
- Students Saraswati College of Engineering Navi Mumbai, Maharashtra India
- Professor Saraswati College of Engineering Navi Mumbai, Maharashtra India
Abstract
Artificial neural networks (ANNs) offer a practical and efficient way to choose the best machining parameters for the turning process to reduce surface roughness, the resulting cutting forces, and maximize tool life. Surface roughness is a significant aspect in the evaluation of cutting performance and plays a significant role in the manufacturing process. The goal of this project is to create a model based on an ANNs that can replicate hard turning of EN-19 steel with only a small amount of cutting fluid. In terms of cutting parameters, this model is meant to forecast the surface roughness. Following training with a set of training data for a specified number of cycles, various network topologies are assessed using input/output data sets specifically designated for this purpose. The root means the square error is determined for the selected architectures. Utilizing linear regression, the regression equation is established. By applying ANN to this equation, we can forecast the surface roughness of test data.
Keywords: Neuron network, artificial intelligence, linear regression, prediction of surface roughness
References
1. Ranganath MS, Vipin RS. Mishra application of ANN for prediction of surface roughness in turning process: a review. 2013;1(3):229–33.
2. Deshpande YV, Andhare AB, Padole PM. Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. SN Appl Sci. 2019;1(1):104. doi: 10.1007/s42452-018-0098-4.
3. Chandrasekaran M, Devarasiddappa D. Artificial neural network modeling for surface roughness prediction in cylindrical grinding of Al-SiCp metal matrix composites and ANOVA analysis. Adv Produc Engineer Manag. 2014;9(2):59–70. doi: 10.14743/apem2014.2.176.
4. Xiao M, Shen X, You MA, Fei Yang, Nong Gao, Weihua Wei, Dan Wu. Prediction of surface roughness and optimization of cutting parameters of stainless steel turning based on RSM. 2018. 15p:Article ID 9051084.
5. Paul BKM et al. Optimization of cutting parameters in hard turning of AISI 4340 steel. Int J Innov Res Adv Eng. 2014;1(8).
6. Hossain MI, Amin AKMN, Patwari AU. Development of an artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 Alloy, IEEE ICCCE 2008. Proceedings of the International Conference on Computer and Communication Engineering 2008, May 13–15, 2008, Kuala Lumpur, Malaysia; 2008. p. 1321-4.
7. Assarzadeh S, Ghoreishi M. Neural-network-based modeling and optimization of the electro- discharge machining process. Int J Adv Manuf Technol. 2008;39(5–6):488–500. doi: 10.1007/s00170-007-1235-1.
8. Malakooti B, Raman V. An interactive multi-objective artificial neural network approach for machine setup optimization. J Intell Manuf. 2000;11(1):41–50. doi: 10.1023/A:1008999907768.
9. Sarma DK, Dixit US. A comparison of dry and air-cooled turning of grey cast iron with mixed oxide ceramic tool. J Mater Process Technol. 2007;190(1–3):160–72. doi: 10.1016/j.jmatprotec.2007.02.049.
10. Rosenblatt F. The Perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev. 1958;65(6):386–408. doi: 10.1037/h0042519.
International Journal of Computer Aided Manufacturing
| Volume | |
| Received | May 26, 2022 |
| Accepted | July 30, 2022 |
| Published | January 30, 2023 |