Improving Precision Of Material Extrusion 3d Printing By In-Situ Monitoring & Predicting 3d Geometric Deviation Using Conditional Adversarial Networks

Material extrusion 3D printing has long been established for rapid prototyping and functional testing in many research and industry fields. However, its inconsistency and intrinsic defects (surface roughness and geometric inaccuracies) hinder its application in several areas, most notably "certify-as-you-build" small-batch prototyping and large-batch production. In this study, we present an approach to reduce both inconsistency and the 3D geometric inaccuracies of products fabricated by material extrusion. To achieve these improvements in print quality, we developed an in situ metrology system, which scans each layer at the time of printing, providing a 3D model of the as-printed part. We then trained machine learning algorithms with data from this scanning system and predicted 3D geometric inaccuracies in new designs. Eight conditional adversarial network (CAN) machine learning models were trained on a limited number of scanned profile images of different layers, consisting of less than 50 actual images and 50 generated images, to predict the 3D geometric deviations of freeform shapes. The generated images were produced by randomly combining and cropping the actual images without any distortion. These CAN models produced predictions where at least 44.4%, 87.6%, 99.2% of data were within +/- 0.05 mm, +/- 0.10 mm, +/- 0.15 mm of the actual measured value, respectively. A laser sensor was integrated into a material extrusion 3D printer to achieve in situ monitoring of dimensional inaccuracies during printing, which leaves the door open to implement a closed-loop feedback system to compensate geometric errors during printing in the future and fabricate "certify-as-you-build" products.