Experimental investigation on machining of titanium composite using artificial neural network

被引：0

作者：

Perumal, B. ^{[1
]}

Kannan, R. ^{[2
]}

Arunkumar, R. ^{[3
]}

Kumaresan, T. ^{[4
]}

Subbiah, Ram ^{[5
]}

机构：

[1] Department of Mechanical Engineering, Sri Venkateshwara College of Engineering, Andhra Pradesh, Tirupati

[2] Department of Mechanical Engineering, PSNA College of Engineering and Technology, Dindigul

[3] Department of Mechanical Engineering, MRK Institute of Technology, Tamilnadu, Kattumannarkoil

[4] Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode

[5] Department of Mechanical Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad

来源：

Interactions | 2025年 / 246卷 / 01期

关键词：

Artificial intelligence; Machining; RSM; Surface roughness; Titanium composite;

D O I：

10.1007/s10751-025-02262-3

中图分类号：

学科分类号：

摘要：

Manufacturers must be able to figure out the most suitable technique capable of generating rapid and accurate performance when developing a precise modelling approach for the development of an efficient machining process. This paper assesses the predictive capabilities of the Artificial Neural Network (ANN) and the response surface methodology in the titanium machining process with respect to tool tip temperature and tool wear. The adequacy of the ANN in modeling and forecasting reactions were rigorously examined and contrasted using data collected from properly prepared machining experiments. Both approaches performed admirably in terms of forecasting machining process responses. The coefficient of correlation (R2) obtained from the analysis verifies the ANN’s choice, with a maximum value of 99.9% and with the response surface methodology with a maximum value of 99.8% was found. The experiment also demonstrates that when the proper parameters are used, the ANN approach can produce the best results. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

引用

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