高温合金皮秒激光打孔质量评估与智能预测研究OA

Superalloys are extensively utilized in high-end manufacturing fields such as aerospace owing to their superior mechanical properties.The machining quality of micro-holes in superalloy components directly influences their service performance and operating lifespan.To achieve high-quality machining of micro-holes in superalloys,taking the GH4169 superalloy as the object,the trepanning drilling experiments were conducted using picosecond laser multiple scanning technology.Initially,the single-factor experimental approach was employed to systematically investigate the effects of four key process parameters,namely laser power,scanning speed,defocusing distance,and number of scans,on the roundness of the micro-hole entrance and exit,and the underlying physical mechanisms were also thoroughly analyzed.Subsequently,to facilitate precise prediction and optimization of the machining quality of micro-holes,a prediction model for entrance roundness was developed,leveraging a BP(back propagation)neural network optimized by genetic algorithm(GA).The results indicated that the effect of each process parameter on the entrance and exit roundness of micro-holes differed significantly,necessitating synergistic control to achieve a micro-hole morphology with superior overall quality.Compared with the conventional BP neural network,the prediction accuracy of the GA-BP neural network model was substantially enhanced.The maximum relative error between the predicted and actual values on the test set was reduced from-16.464 5%to-1.880 4%,with the error fluctuation range stabilized within±2%.The research results provide a reliable theoretical foundation and technical support for the high-quality and high-efficiency laser machining of micro-holes in superalloys.