Machine-learning Enabled Automation Processes and Prototyping

Dr. Shih-Chi Chen’s team is focusing on implementing advanced artificial intelligence (AI) and machine-learning-based methods to the field of precision machine control and advanced manufacturing. Specifically, we have custom-developed a precision multi-layer roll-to-roll (R2R) printing system and two micro-additive manufacturing platforms, i.e., femtosecond projection two-photon lithography system and digital holography-based 3D printing system, to implement different AI methods. We expect the proposed method may significantly improve the system performance in terms of fabrication rate and resolution by automated system training and parameter optimization, and lower the cost by retiring expensive metrology components such as capacitance probes in precision machines. For example, in our multi-focus 3D printing system, hundreds of laser foci are parallelly and independently controlled to fabricate a design object with optimal laser exposure doses and fabrication trajectories, which cannot be achieved previously without substantial empirical studies and trial and errors. On the R2R platform, low-cost strain sensor arrays are installed on the system to realize deep-learning based multi-axis precision control, achieving 100 nm positioning precision without the need of parameter tuning.