Exploring Machine Learning and Machine Vision in Femtosecond Laser Machining

Abstract

Abstract To achieve optimal results, femtosecond laser machining requires precise control of system variables such as Regenerative Amplifier Divider, Frequency, and Laser Power. To this end, two regression models, multi-layer perceptron (MLP) regression and Gaussian process regression (GPR) were used to define the complex relationships between these parameters of the laser system and the resulting diameter of a dimple fabricated on a 304 stainless-steel substrate by a 0.2-second laser pulse. In order to quantify dimple diameter accurately and quickly, machine vision was implemented as a processing step while incorporating minimal error. Both regression models were investigated by training with datasets containing 300, 600, 900, and 1210 data points to assess the effect of the dataset size on the training time and accuracy. Results showed that the GPR was approximately six times faster than the MLP model for all of the datasets evaluated. The GPR model accuracy stabilized at approximately 20% error when using more than 300 data points and training times of less than 5 s. In contrast, the MLP model accuracy stabilized at roughly 33% error when using more than 900 data points and training times ranging from 30 to 40 s. It was concluded that GPR performed much faster and more accurately than MLP regression and is more suitable for work with femtosecond laser machining.