Typical applications and perspectives of machine learning for advanced precision machining: A comprehensive review

Abstract

Advanced precision machining technologies, such as micro/ultraprecision mechanical machining and atomic and close-to-atomic scale manufacturing, are critical to high-value industries like aerospace and defense. However, extreme precision requirements and nonlinear dynamics pose significant challenges for accurate modeling, as traditional methods often struggle to capture intricate interactions and inherent variability. Machine learning emerges as a transformative solution, enabling data-driven modeling with unprecedented accuracy. This paper provides a comprehensive overview of the significant advancements and typical applications of machine learning in advanced precision machining, focusing on model architectures and methodologies to guide industrial implementation. For instance, this paper presents various examples, such as the application of LSTM networks in predicting tool life by capturing temporal dependencies in force signals, which illustrates how machine learning models are tailored to address specific challenges in precision machining. However, industrial adoption of machine learning remains hindered by limited datasets and computational constraints. This paper offers forward-looking recommendations to address these issues, integrating machine learning into precision machining within the framework of Industry 5.0 and providing robust support for the further promotion and application of machine learning in actual production environments. Furthermore, this research establishes a robust framework for recognizing similarities in machine learning applications across diverse machining domains, facilitating transfer learning among various advanced precision machining processes. By bridging the gap between theoretical models and industrial scalability, this review highlights the transformative role of machine learning in advanced precision machining toward intelligent, sustainable production, ultimately supporting high-performance component manufacturing.