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Prediction of Thrust Force and Torque for High-Speed Drilling of AL6061 with TMPTO-Based Bio-Lubricants Using Machine Learning

Authors :
Pramod Kathmore
Bhanudas Bachchhav
Somnath Nandi
Sachin Salunkhe
Palanisamy Chandrakumar
Emad Abouel Nasr
Ali Kamrani
Source :
Lubricants, Vol 11, Iss 9, p 356 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

This study was designed to examine the effects of a trimethylolpropane trioleate (TMPTO)-based lubricant on thrust force and torque under the high-speed drilling of Al-6061 as an effective environmentally friendly cutting fluid. The tribological performance of three lubricant blends was evaluated based on ASTM standards. TMPTO base oil, notably enhances load-carrying capacity under extreme pressure conditions, with a seizer load of 7848 N. The best-performing oil was further optimized using a Taguchi-based design experiment to investigate the effect of different additive concentrations on thrust force and torque under actual contact conditions. Experiments were conducted using three critical machining parameters: additive concentration, spindle speed, and feed rate. The results of the ANOVA analysis reveal that spindle speed contributes most substantially (62.99%) to torque, with feed rate (23.72%) and additive concentration (7.74%) also showing significant impacts. On the other hand, thrust force is primarily influenced by feed rate (73.52%), followed by spindle speed (16.82%), and additive concentration (6.28%). Furthermore, a machine learning model was developed to predict and compare a few significant aspects of high-speed drilling machinability, including thrust force and torque. Three different error metrics were utilized in order to assess the performance of the predicted values, namely the coefficient of determination (R2), mean absolute percentage error (MAPE) and mean square error (MSE), which are all based on the coefficient of determination. Compared to other models, decision tree produces more accurate prediction values for cutting forces. The present study provides a novel approach for evaluating the most promising biodegradable lube oils and predicting cutting forces by formulating a perfect blend.

Details

Language :
English
ISSN :
20754442
Volume :
11
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Lubricants
Publication Type :
Academic Journal
Accession number :
edsdoj.96ad58b1b57040c38bcd3ab573b3476c
Document Type :
article
Full Text :
https://doi.org/10.3390/lubricants11090356