Two-photon lithography (TPL)—a widely used 3-D nanoprinting technique that uses laser light to create 3-D 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.
Capable of printing nanoscale features at a very high resolution, TPL uses a laser beam to build parts, focusing an intense beam of light on a precise spot within a liquid photopolymer material. The volumetric pixels, or "voxels," harden the liquid to a solid at each point the beam hits and the uncured liquid is removed, leaving behind a 3-D structure. Building a high-quality part with the technique requires walking a fine line: too little light and a part can't form, too much and it causes damage. For operators and engineers, determining the correct light dosage can be a laborious manual process.
Lawrence Livermore National Laboratory (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. The team's machine learning algorithm was trained on thousands of video images of builds labeled as "uncured," "cured," and "damaged," to identify the optimal parameters for settings such as exposure and laser intensity and to automatically detect part quality at high accuracy. The work was recently published in the journal Additive Manufacturing.To read more, click here.