Real-time structural validation for material extrusion additive manufacturing

Material extrusion additive manufacturing is a technology that produces a part by controlling the melting and extrusion of a thermoplastic filament. A void defect in a specific position can significantly impact the whole product's structural quality and mechanical properties. Different defect detection systems have been built and have achieved good detection accuracy. However, a challenge remains in determining if the structural performance of a part with defects will fail to meet design requirements. This research develops a real-time product structural quality validation system using a multi-dimensional accumulation-thresholdbased decision-making approach. The proposed system is validated on a consumer-grade 3D printer with an optical camera. The layer-wise damage information is output from a trained convolutional neural network. The developed structural quality validation system links the obtained defect information with the decision boundary developed by the accumulation-threshold-based decision-making approach to evaluate the effect of the defect. The accumulation-threshold-based decision-making approach is trained on a novel component health index that links the location and size of defects across layers. Experimental results show that the decision boundary obtained from the accumulation-threshold-based decision-making approach performs well on test data with 96% recall and a 91% F1-score, which means the decision boundary can provide good overall results while being biased to limit false negative results. The proposed real-time structural quality validation system is validated online for a dog-bone test specimen. Results show that the computational time of the structural quality validation system requires at most 688 ms, while a dog-bone layer takes 75 s to print, thus demonstrating that the proposed system can meet required real-time constraints. The capability to run online enables users to cancel a print mid-process for specimens that will not meet structural loading requirements. The developed system is demonstrated through a video, which is provided in the supplemental materials. The dataset for this work is published as a public repository containing 450 samples with 221 failure classes.

Automated summary

This research develops a real-time product structural quality validation system using a multi-dimensional accumulation-threshold-based decision-making approach. The proposed system can effectively evaluate the impact of defects on the structural performance of a part and meet real-time constraints.