Defect Detection and Closed-loop Feedback Using Machine Learning for Fused Filament Fabrication
Publication Date
1-1-2024
Document Type
Conference Proceeding
Publication Title
Procedia CIRP
Volume
126
DOI
10.1016/j.procir.2024.08.247
First Page
603
Last Page
608
Abstract
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.
Keywords
Convolutional neural network, Extrusion-based additive manufacturing, Keras, Transfer learning
Department
Mechanical Engineering
Recommended Citation
Amaris De La Rosa, Amir Armani, and Marcia Golmohamadi. "Defect Detection and Closed-loop Feedback Using Machine Learning for Fused Filament Fabrication" Procedia CIRP (2024): 603-608. https://doi.org/10.1016/j.procir.2024.08.247
