Intelligent Control System Of Milling Based On Real-time Monitoring Of Cutting Force

Nowadays,real-time feedback technology is rare for the machining accuracy and machining quality of products in precision machining,so it is impossible to monitor the cutting state changes such as tool wear in the machining process in real time.However,with the progress of cutting,the wear of the tool is increasing,and the actual machining cutting performance are also changing.Especially when the tool is in the stage of severe wear,it will greatly affect the dimensional accuracy and surface quality of the machined surface of the workpiece.Therefore,this topic proposes an intelligent control strategy of milling based on real-time monitoring of cutting force.Through the on-line monitoring module,the machining process is monitored and the features are extracted to monitor the tool wear.The machining quality such as surface roughness will be predicted,and the cutting parameters are adjusted to maintain the stability of cutting state and machining quality.Firstly,this paper designs the structure of our system with two main strategies:tool wear monitoring strategy and cutting parameter optimization strategy.In the tool wear monitoring strategy,the method of cutting force feature extraction and selection is developed based on tool wear experiment.In the cutting parameter optimization strategy,through the machining process experiment,the cutting force adaptive adjustment strategy and cutting parameter optimization strategy are proposed,and the surface roughness prediction model is established.In addition,the optimization model of cutting parameters is established based on the prediction model of surface roughness.This paper analyzes the cutting force data of tool wear experiment,extract the features of cutting force data,and use Pearson correlation coefficient method to select the features with the strongest correlation with tool wear.The characteristic quantity of cutting force were set as the input and the tool wear state as the output,and the tool wear state prediction model is established.The tool state and cutting parameters was set as inputs and the surface roughness and cutting force as outputs,the surface roughness prediction model and cutting force prediction model considering tool wear are established.The overall model of parameter optimization is established by using alternating iterative optimization algorithm with the cutting force prediction model and surface roughness prediction model.The intelligent control system is developed by C++,Visual Studio 2019 and Qt 5.9.The system can monitor the changes of cutting force and cutting heat in real time.The intelligent decision-making strategy ensures the stability in the ideal milling state and the stability of product machining quality.The intelligent testing and control system meets the needs of the development of intelligent testing and control system.