Introduction
In the current CNC machining process, the detection of tool state has an important impact on the quality of the machining product. The state of the cutting tool is mainly related to the following aspects: tool life, hardness, size, wear and corrosion. The detection of these factors is important to optimize the machining process and reduce costs. In this paper, an overview of the state of CNC tool detection and its application in CNC machining is presented.
Tool status Detection
The most commonly used methods for detecting the state of CNC tools include acoustic emission, vibration, thermal imaging, wear analysis, and wear particles analysis. Acoustic emission parameters, such as decay time, rise time and peak to peak amplitude can reveal tool wear and determine the quality of machined surfaces. Vibration analysis can be used to detect changes in wear and debris from wear. Thermal imaging can detect changes in tool temperatures during operation, providing insight into tool wear. Wear analysis is used to detect material transfer from the tool to the machined surface and to quantify wear. Wear particles analysis is used to detect and measure wear particles, which can indicate the degree of tool wear.
Application of tool status detection
In CNC machining, the detection of tool status is essential to optimize machining processes and minimize cost. Accurately measuring tool wear can reduce tool inventory costs as well as optimize tool selection and tool paths. Accurate detection of tool state can also reduce cutting forces, resulting in improved cutting performance, improved part accuracy and higher productivity.
Conclusions
In this paper, an overview of the state of CNC tool detection and its application in CNC machining is presented. The most commonly used methods for detecting tool state include acoustic emission, vibration, thermal imaging, wear analysis and wear particles analysis. Accurate detection of tool state is essential to optimize CNC machining processes and reduce costs, as it can lead to improved part accuracy, higher productivity, and minimized tool inventory costs.
