Artificial intelligence based design of multiple friction modifiers dispersed castor oil and evaluating its tribological properties

• Published in: Tribology international Vol. 140; p. 105813
• Main Authors: Bhaumik, Shubrajit, Pathak, S.D., Dey, Swati, Datta, Shubhabrata
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2019; Elsevier BV
• Subjects: Artificial intelligence; Artificial neural networks; Artificial neural networks (ANN); Castor oil; Coefficient of friction; Design modifications; Friction; Genetic algorithms; Lubricants; Lubricants & lubrication; Mineral oils; Nano particles; Optimization; Product design; Tribology; Artificial neural networks (ANN); Castor oil; Nano particles; Tribology
• ISSN: 0301-679X; 1879-2464
• DOI: 10.1016/j.triboint.2019.06.006

This present work deals with designing a new lubricant with multiple friction modifiers (FM) having better tribological properties using artificial neural network (ANN) and genetic algorithm (GA). The input variables considered are load, speed and concentration of the FMs, and coefficient of friction (CoF) is the output. Experimental data generated in pin-on-disc tribometer is used for the ANN model, whereas GA is used for optimization using the ANN models as the objective function. The experimental trials of the computationally designed lubricants achieving the minimum CoF were conducted. It has been observed that the coefficient of friction by the new lubricant containing multiple friction modifiers is 45–50% less and wear scar diameter is 87.5% less compared to commercial mineral oil samples. [Display omitted] •Biodegradable lubricant using castor oil and multiple friction modifiers is designed.•Designing is done using neural network models and genetic algorithm optimization.•Tribological properties of the designed and developed lubricant are tested.•Significant enhancement of anti-wear properties is observed.•Reduction in coefficient of friction and wear scar diameter are by 50% and 87% respectively.