AUTOMATED SYSTEM FOR MONITORING THE MACHINING PROCESS

Abstract

This paper examines the development and implementation of an automated system for monitoring the machining process, utilizing methods of acoustic emission analysis and elastic deformation measurement. Its primary goal is to detect tool wear and predict the remaining tool life, which enhances machining quality, reduces defect rates, and optimizes production processes. The system integrates into the manufacturing infrastructure, improving equipment efficiency and minimizing the risk of emergency situations.  The proposed algorithms enable real-time responses to changes in machining parameters, preventing tool failures. The system provides real-time data analysis, allowing for machining mode adjustments that reduce equipment load and enhance overall productivity. The application of machine learning methods and big data analysis improves wear prediction accuracy and adapts technological parameters to changing conditions. Determining critical threshold values helps prevent emergency situations and ensures stable equipment operation. A key advantage of the system is its integration with CNC systems, enabling automatic machining mode adjustments without operator intervention. Additionally, it supports cloud technologies for centralized data storage and analysis, improving decision-making accuracy and speed. Tool condition data is used for statistical analysis of tool efficiency and machining parameter optimization. Research has confirmed that the system contributes to increased tool life, reduced defects, and lower energy consumption. Waste reduction and improved final product quality enhance the economic efficiency of production. Future developments may include the implementation of multisensor technologies, digital twins, and neural network algorithms for even more accurate process prediction and adaptation.