LLNL scientists and collaborators are using machine learning to address two key barriers to industrialization of two-photon lithography (TPL): monitoring of part quality during printing and determining the right light dosage for a given material. The team developed a machine learning algorithm trained on thousands of video images of TPL builds to identify the optimal parameters for settings such as exposure and laser intensity and to automatically detect part quality at high accuracy.
(Download Image)Machine learning model may perfect 3D nanoprinting
Two-photon lithography (TPL)—a widely used 3D nanoprinting technique that uses laser light to create 3D objects—has shown promise in research applications but has yet to achieve widespread industry acceptance due to limitations on large-scale part production and time-intensive setup. LLNL scientists and collaborators turned to machine learning to address two key barriers to industrialization of TPL: monitoring of part quality during printing and determining the right light dosage for a given material. Read more at LLNL News.
