首页|Forecasting the Friction Coefficient of Rubbing Zirconia Ceramics by Titanium Alloy

Forecasting the Friction Coefficient of Rubbing Zirconia Ceramics by Titanium Alloy

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  • Wiley
The thermal issues generated from friction are the key obstacle in the high-performance machining of titanium alloys. The friction between the workpiece being cut and the cutting tool is the dominant parameter that affects the heat generation during the machining processes, i.e., the temperature inside the cutting zone and the consumed cutting energy. Besides, the complexity is associated with the nature of the friction phenomenon. However, there are limited efforts to forecast the friction coefficient during the machining operations. In this work, the friction coefficients between the titanium alloy against zirconia ceramics lubricated by minimum quantity lubrication were recorded and measured using a universal mechanical tester pin-on-disc tribometer. Then, we proposed two models for forecasting the friction coefficient which are trained and tested on the recorded data. The two predictive models are based on autoregressive integrated moving average and gated recurrent unit deep neural network methods. The proposed models are evaluated through a set of exhaustive experiments. These experiments demonstrated that the proposed models can efficiently be used to reduce power consumption dedicated to monitoring the friction coefficients. Besides, they can reduce or avoid surface thermal damage by predicting the high level of friction coefficients in advance, which can be used as an alert to enable or readjust the lubrication parameters (fluid pressure, fluid flow rate, etc.) to maintain lower ranges of friction coefficients and power consumption.

Ahmad Salah、Ahmed Fathalla、Esraa Eldesouky、Wei Li、Ahmed Mohamed Mahmoud Ibrahim

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College of Computing and Information Sciences, University of Technology and Applied Sciences, Ibri, Oman||Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharkia, Egypt

Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia, Egypt

Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia||Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia 41522, Egypt

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China

College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China ||Department of Mechanical and Aerospace Engineering, College of Engineering, United Arab Emirates University, Al Ain 15551, UAE ||Production Engineering and Mechanical Design Department, Faculty of Engineering, Minia University, Minya 61519, Egypt

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2023

10.1155/2023/6681886

International journal of intelligent systems

International journal of intelligent systems

EISCI
ISSN：0884-8173
年,卷(期)：2023.2023(Pt.6)
  • 61