Aprimorando processos de usinagem com inteligência artificial : uma revisão abrangente

Abstract

This work consists of a comprehensive bibliometric review on the application of artificial intelligence techniques in machining processes. The central objective of the study was to map the scientific research scenario from 2021 to 2025, aiming to identify the main trends and research gaps, quantifying the impact of various artificial intelligence techniques on processes such as milling, turning, drilling, and grinding. To enable this research, 85 articles were statistically analyzed to generate data on the topic. As a result, milling and turning were the most investigated processes, while machine learning and artificial neural networks are the most dominant techniques. It was revealed that the main focus of the research lies in optimizing surface roughness and tool wear, although a notable growth in the evaluation of environmental and economic impacts is observed. It was also possible to identify persistent challenges, such as big data management and the difficulty of generalizing artificial intelligence models for different industrial scenarios. It was concluded that the research field is mature in quality and wear optimization but points to new promising directions, such as the use of hybrid approaches and digital twins to overcome existing limitations. It is suggested for future work to focus on more accessible and standardized solutions that allow the transition to intelligent manufacturing, ensuring maximum efficiency, quality, and sustainability.