Optimizing the Transition: Replacing Conventional Lubricants with Biological Alternatives through Artificial Intelligence - Soares, Gustavo [UNESP]; Chavarette, Fabio Roberto [UNESP]; Goncalves, Aparecido Carlos [UNESP]; Faria, Henrique Antonio Mendonca [UNESP]; Outa, Roberto; Mishra, Vishnu Narayan (Shahid Chamran Univ Ahvaz, Iran, 2025)
In today's modern industry, artificial intelligence (AI) is revolutionizing the formulation, performance optimization, and monitoring of lubricants. By enabling the analysis of large datasets, AI facilitates the development of customized formulations and predictive maintenance strategies. Traditionally, synthetic lubricants have been widely used due to their superior performance characteristics; however, they pose significant environmental and health risks. In contrast, bio-based lubricants offer a sustainable and biodegradable alternative, aligning with growing environmental and health-conscious trends. This study aims to leverage AI to assess the feasibility of replacing conventional synthetic lubricants with bio-based lubricants in vibrating mechanical structures. By employing AI-driven analysis, the research investigates the performance characteristics of bio-greases compared to their synthetic counterparts, focusing on signal vibration responses. The findings demonstrate that AI can effectively optimize lubricant performance, reduce operational costs, and enhance sustainability in the lubricant industry. The present study underscores the critical importance of evaluating the differences between conventional commercial and bio-based lubricants in an innovative way through vibration signals, highlighting their potential applications across various industrial sectors. The integration of AI not only enhances performance and sustainability but also paves the way for innovative advancements in lubricant technology.
Keywords: Vibration; Lubricants; Artificial Intelligence; Artificial Immune Systems; Negative Selection Algorithm