Process modeling and optimization in laser drilling of bulk metallic glasses based on GABPNN and machine vision

•Hole features were extracted using machine vision technology and fractal theory.•A neural network model for laser drilling BMGs was established.•The model exhibits high accuracy and reliability in parameter prediction.•Based on the predicted parameters, high-quality microholes were obtained. It is a challenging to ensure the quality of microholes during the laser drilling of bulk metallic glasses (BMGs), where the defects such as heat-affected zones, burrs, and low smoothness are usually obtained. Based on machine vision, back-propagation neural network (BPNN), and genetic algorithm (GA) in artificial intelligence technology, a relationship model between the microhole features and laser processing parameters was proposed, so as to predict and optimize the processing parameters during the laser drilling of BMGs. Firstly, the geometric parameters and texture information of microholes were obtained using fractal theory and machine vision techniques, including the gray-level co-occurrence matrix (GLCM) algorithm, image binarization, and edge extraction. A comprehensive numerical characterization of various microhole features was conducted, providing the basis for subsequent prediction and optimization. Secondly, the previously-extracted microhole features were set as input layer, and the processing parameters were set as output layer. After proper training, the BP neural network optimization model (GABPNN), based on the genetic algorithm, was established. As compared to the original BPNN model, the GABPNN model exhibited improved performance, reducing the maximum prediction error from 4.860% to 2.285%. Thirdly, with a set of input layer parameters, the required processing parameters were predicted by the model. The predicted results were verified in experiments, where good surface quality of the laser-drilled BMG microholes with good roundness and small ablation was obtained. This shows that it is feasible to predict and optimize processing parameters by GABPNN based on feature parameters extracted by machine vision.