APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE OF METAL-CUTTING EQUIPMENT: ANALYSIS OF PRACTICAL EXAMPLES AND DEVELOPMENT PROSPECTS

This article provides a systematic analysis of modern research on predictive maintenance of metal-cutting equipment using machine learning algorithms from 2015 to 2025. The economic feasibility of implementing machine learning systems in production was considered. During the formation of the sample of analyzed works, the emphasis was placed on the practical applicability of the systems. Online and offline systems were described, covering the main stages of development - from data collection to model quality assessment. Technical limitations of offline systems for real production conditions were discussed. Based on the results of the analysis, a promising direction for further research can be considered the creation of a multisensor online system, including the use of an inexpensive and low-power electronic component base, as well as synthetic data to reduce the cost of the system and speed up deployment. Keywords: machine learning; predictive maintenance; maintenance optimization; metalworking ; smart manufacturing.