A data mining approach in real-time measurement for polymer additive manufacturing process with exposure controlled projection lithography

Real-time inspection and part dimensions determination during the manufacturing process can improve production of qualified parts. Exposure Controlled Projection Lithography (ECPL) is a bottom-up mask-projection additive manufacturing (AM) process, in which micro parts are fabricated from photopolymers on a stationary transparent substrate. An in-situ interferometric curing monitoring and measuring (ICM&M) system has been developed to infer the output of cured height. Successful ICM&M practice of data acquisition and analysis for retrieving useful information is central to the success of real-time measurement and control for the ECPL process. As the photopolymerization phenomena occur continuously over a range of space and time scales, the ICM&M data analysis is complicated with computation speed and cost. The large amount of video data, which is usually noisy and cumbersome, requires efficient data analysis methods to unleash the ICM&M capability. In this paper, we designed a pragmatic approach of ICM&M data mining to intelligently decipher part height across the cured part. As a data-driven measurement method, the ICM&M algorithms are strengthened by incorporating empirical values obtained from experimental observations to guarantee realistic solutions, and they are particularly useful in real time when limited resource is accessible for online computation. Experimental results indicate that the data-enabled ICM&M method could estimate the height profile of cured parts with accuracy and precision. The study exemplifies that data mining techniques can help realize the desired real time measurement for AM processes, and help unveil more insights about the process dynamics for advanced modeling and control.