Defect Detection and Closed-loop Feedback Using Machine Learning for Fused Filament Fabrication

The objective of this study was to develop a closed-loop system for a commercial fused filament fabrication printer based on visual machine learning inspection of common defects. Convolutional neural network was used to identify levels of common defects: stringing, over/under-extrusion, and weak infill. Transfer learning was used to adapt a pre-trained model to fit this problem, as it involves incrementally fine-tuning the model parameters to new data. The observation model achieved an accuracy of 92.86% on validation data set and 90.0% on the testing data set. By modifying the input G-code, the custom program could adjust the feed-rate, nozzle temperature, material extrusion amount, and fan speed to correct for identified extrusion defects.