ADAPTIVE SYSTEM FOR MILLING

Abstract

The present work is dedicated to the development and analysis of a real-time diagnostic and evaluation system for monitoring the condition of both the cutting tool and the technological process during machining operations on milling machines. The core objective is to enhance the performance and reliability of the machining process by implementing intelligent diagnostic methods capable of accurately recognizing the technological state of the material being processed as well as the condition of the cutting tool throughout the manufacturing cycle.

Special attention is paid to the implementation of efficient and adaptive data processing techniques to analyze diagnostic signals, allowing for more precise recognition and interpretation of the machining environment. These diagnostic signals, particularly acoustic emissions, provide valuable insight into the interaction between the cutting tool and the workpiece. By analyzing the acoustic signatures generated during the milling process, it becomes possible to identify abnormal conditions such as tool wear, tool breakage, or material inconsistencies, which are critical for maintaining product quality and process stability.

The research further explores the concept and design of an adaptive control system tailored specifically for milling operations. This system dynamically adjusts process parameters based on real-time feedback from diagnostic sensors, effectively creating a closed-loop control architecture. The adaptation is grounded in the correlation between acoustic signal behavior and cutting dynamics, ensuring that the process remains within optimal operational thresholds even under varying machining conditions.

The outcomes of this research demonstrate the feasibility of integrating such an adaptive system into modern CNC milling machines. As a result, manufacturers can achieve significant improvements in productivity, precision, and cost-efficiency by minimizing downtime, reducing the frequency of tool changes, and optimizing machining parameters. Overall, this study contributes to the advancement of intelligent manufacturing systems and paves the way for more autonomous, reliable, and efficient machining processes.