Research Article
| Open Access |
https://doi.org/10.55640/corr-1-1-3
Neural Network Analysis for High-Speed Machining Predictions: Unraveling Cutting and Process Parameters Correlation
Abstract
In the realm of high-speed machining, achieving optimal cutting and process parameters is paramount for efficiency, precision, and tool longevity. This study employs a neural network approach to unravel the intricate correlation between cutting and process parameters, offering predictive insights that can enhance machining operations. By leveraging machine learning techniques, we explore large datasets encompassing various machining scenarios to establish patterns and relationships between cutting speeds, tool materials, feed rates, and other parameters. The resulting neural network model provides a valuable tool for engineers and manufacturers to optimize their machining processes, reduce waste, and improve productivity in high-speed machining environments.
Keywords
High-Speed Machining, Neural Network Analysis, Machining Predictions, Cutting Parameters
References
Abukhshim NA, Mativenga PT, Sheikh MA. Heat generation and temperature prediction in metal cutting: A review and implication for high speed machining. International Journal of Machine Tools & Manufacture2006; 46:782-800.
Alavuden A, Venkateswaran N, Jeyaraj P. Manufacturing technology. Anuradha Agencies, Ed-1, 2.
Altin A, Nalbant M, Taskesen A. The effects of cutting speed on tool wear and tool life when machining Inconel718 with ceramic tools. Journal of material and design2007; 28:2518-2522.
Aslan E, Camus N, Birgoren B. Design optimization of cutting parameters when turning hardened AISI 4140 steel with Al2O3+ TiCN mixed ceramic tool. Journal of material and design 2007; 28:1618-1622.
Coelho RT, Silva LR, Braghini AJr, Bezerra AA. Some effects of cutting edge preparation and geo metric modifications when turning INCONEL 718 at high cutting speeds. Journal of Materials Processing Technology 2004;148:147-153.
Dalela S. Manufacturing science and technology. Umesh Publication, Ed-1, 2.80.
Devillez A, Schneider F, Dominiak S, Dudzinski D, Larrouquere D. Cutting forces and wear in dry machining of Inconel 718 with coated carbide tools. Journal of wear2007; 262:931-942.
John FC, Lim BS, Lennie EN. Optimal design of neural networks using the Taguchi method. Journal of neurocomputing 1995; 7:225-245.
Article Statistics
Downloads
Copyright License
Copyright (c) 2023 Anwar Hussain (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.