Quantitative and Real‐Time Control of 3D Printing Material Flow Through Deep Learning

Abstract

3D printing could revolutionize manufacturing through local and on‐demand production while enabling uniquely complex and custom products. However, 3D printing's propensity for production errors prevents autonomous operation and the quality assurance necessary to realize this vision. Human operators cannot continuously monitor or correct errors in real time, while automated approaches predominantly only detect errors. New methodologies correct parameters either offline or with slow response times and poor prediction granularity, limiting their utility. A commonly available 3D printing process metadata is harnessed, alongside the video of the printing process, to build a unique image dataset. Regression models are trained to precisely predict how printing material flow should be altered to correct errors and this should be used to build a fast control loop capable of 3D printing parameter discovery and few‐shot correction. Demonstrations show that the system can learn optimal parameters for unseen complex materials, and achieve rapid error correction on new parts. Similar metadata exists in many manufacturing processes and this approach could enable the adoption of fast data‐driven control systems more widely in manufacturing.