Intelligent Recognition of Tool Wear with Artificial Intelligence Agent

Abstract

Tool wear, closely linked to operational efficiency and economic viability, must be detected and managed promptly to prevent significant losses. Traditional methods for tool wear detection, though somewhat effective, often lack precision and require extensive manual effort. Advancements in artificial intelligence (AI), especially through deep learning, have significantly progressed, providing enhanced performance when combined with tool wear management systems. Recent developments have seen a notable increase in the use of AI agents that utilise large language models (LLMs) for specific tasks, indicating a shift towards their integration into manufacturing processes. This paper provides a comprehensive review of the latest advancements in AI-driven tool wear recognition and explores the integration of AI agents in manufacturing. It highlights the LLMS and the various types of AI agents that enhance AI’s autonomous capabilities, discusses the potential benefits, and examines the challenges of this integrative approach. Finally, it outlines future research directions in this rapidly evolving field.