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

Author:

Kathmore Pramod1,Bachchhav Bhanudas2ORCID,Nandi Somnath1,Salunkhe Sachin3ORCID,Chandrakumar Palanisamy3,Nasr Emad Abouel4ORCID,Kamrani Ali5

Affiliation:

1. Department of Technology, Savitribai Phule Pune University, Pune 411007, India

2. Department of Mechanical Engineering, All India Shri Shivaji Memorial Society’s College of Engineering, Pune 411001, India

3. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India

4. Department of Industrial Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

5. Industrial Engineering Department, College of Engineering, University of Houston, Houston, TX 77204, USA

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.

Funder

King Saud University

Publisher

MDPI AG

Subject

Surfaces, Coatings and Films,Mechanical Engineering

Reference37 articles.

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