Forecasting the Friction Coefficient of Rubbing Zirconia Ceramics by Titanium Alloy

• Published in: International journal of intelligent systems 2023-12, Vol.2023, p.1-15
• Main Authors: Salah, Ahmad, Fathalla, Ahmed, Eldesouky, Esraa, Li, Wei, Mahmoud Ibrahim, Ahmed Mohamed
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial neural networks; Ceramics; Coefficient of friction; Cutting energy; Cutting tools; Deep learning; Defective products; Efficiency; Energy consumption; Energy industry; Experiments; Fluid flow; Fluid pressure; Forecasting; Friction; Heat generation; Lubricants & lubrication; Lubrication; Machine learning; Machining; Mathematical models; Neural networks; Parameters; Power consumption; Prediction models; Rubbing; Sensors; Temperature; Time series; Titanium alloys; Titanium base alloys; Workpieces; Zirconium dioxide
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1155/2023/6681886

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.