(155a) “Autonomous 3D Printing” for Novel Food and Pharmaceutical Applications

The additive approach of 3D printing is well suited for precision medicine and personalized nutrition applications, where multiple drugs or nutrients are combined at controlled levels to tailor for individual needs. However, wider adoption of 3D printing has frequently been hampered by the relatively low throughput and the lack of validated materials. The latter further calls for more efficient material and process optimization that goes beyond a heuristics-based or grid search approach. The development costs go up quickly as the 3D printing process becomes more sophisticated with many tunable process parameters and consequently the “curse of dimensionality”. In this presentation, we will first share our recent work in developing a binder jet printing (BJP) test bed for pilot-scale 3D printing of food and pharmaceutical products [Int. J. Pharm. 605, 120791 (2021)]. With the open architecture of this test bed, we have further explored a rather ambitious concept of “autonomous 3D printing” to speed up the optimization process, leveraging the latest (near) real-time, in-situ metrology and machine learning methods. [This work is partially supported by the USDA National Institute of Food and Agriculture, AFRI project 2019-06721.]