Dual visual inspection for automated quality detection and printing optimization of two-photon polymerization based on deep learning

Abstract

Two-photon polymerization (TPP) has emerged as an advanced additive manufacturing technique, allowing for the creation of three-dimensional micro-nano structures with high precision based on two-photon absorption principle. Precisely control light dosage determined by the printing parameters, is crucial for inducing photopolymerization across different photocurable materials and various structures. To address the challenges of parameter optimization, deep learning models were employed to quickly obtained the ideal printing parameters through automated visual inspection during TPP printing process and after post-processing. A dataset was collected from the video recordings during printing process and the images obtained from after post-processing of samples. Data augmentation techniques were applied to enhance the dataset. For the TPP printing process, the mean prediction accuracy increasing from 95.1% to 96.8% for the 3D-CNN model and from 95.4% to 97.8% for the CNN-LSTM model. For the post-processing, the mean prediction accuracy with CNN model increases from 94.5% to 95.2%. Consequently, spatial–temporal DL models were trained based on these datasets, and the results of dual visual inspection method demonstrated a high accuracy of 93.1% and a rapid recognition time of 48 ms. And an analysis of the failure cases of the deep learning models was conducted. Additionally, the optimal printing parameter ranges was determination for various combinations of materials and structures. This system plays a crucial role in accelerating the optimization of TPP process parameters and quality inspection, effectively addressing the challenges in the industrialization process of TPP technology.