Machine Learning-Assisted Feature Prediction of Micro Pit Arrays in Femtosecond Laser Processing

With the characteristics of wear resistance， corrosion resistance， improved biocompatibility and antibacterial properties， the micropits array structure has been widely used.femtosecond laser has unique advantages in high-quality micro pit processing， because of its unique ultra-fast processing effect.In this work， Random Forest Regression (RFR) algorithm and Artificial Neural Network (ANN) algorithm were applied to predict the geometry and quality of micro-pit arrays processed by femtosecond laser.Additionally， the effects of laser processing parameters on the diameter， depth and surface roughness (Ra) of micro-pits were analyzed.The predictive capabilities of the RFR and ANN models were evaluated through the root mean square error， coefficient of determination and mean absolute error.Results showed that the overall prediction accuracy of ANN model was slightly higher than RFR model.The R2 for ANN model was 0.81.For diameter， depth and surface roughness， the R2 was 0.67， 0.79 and 0.85， respectively.Data augm