Machining parameters optimization for free-form blade milling based on time-position mapping of process signals

Low efficiency and poor consistency are serious problems in the machining of complex thin-walled parts with difficult-to-cut materials. The main reason is that the data aware ability of the machining process is weak, and the analysis and decision-making ability is insufficient. It is difficult to optimize the process parameters according to the time-varying working conditions. This study presents a machining parameters optimization for the milling of the aero engine blade, which is based on time-position mapping of processing signal. The machining process is monitored through the open interface of the numerical control system. It realizes the time and space mapping of the monitoring data and the cutting position of the part, as well as the knowledge correlation of the cutting parameter and the response characteristic. In order to improve the machining efficiency, spindle power response cloud map of machining surface is constructed based on time-position mapping, and the feedrate is optimized by adaptive network-based fuzzy inference system (ANFIS). The milling experiment shows that the optimization proposed in this paper has applicability to process parameters under different constraints, as well as greatly improving the utilization of monitoring data and the efficiency of process parameter optimization.