Additive
manufacturing technologies have progressed in the past
decades, especially when used to print biofunctional structures such
as scaffolds and vessels with living cells for tissue engineering
applications. Part quality and reliability are essential to maintaining
the biocompatibility and structural integrity needed for engineered
tissue constructs. As a result, it is critical to detect for any anomalies
that may occur in the 3D-bioprinting process that can cause a mismatch
between the desired designs and printed shapes. However, challenges
exist in detecting the imperfections within oftentimes transparent
bioprinted and complex printing features accurately and efficiently.
In this study, an anomaly detection system based on layer-by-layer
sensor images and machine learning algorithms is developed to distinguish
and classify imperfections for transparent hydrogel-based bioprinted
materials. High anomaly detection accuracy is obtained by utilizing
convolutional neural network methods as well as advanced image processing
and augmentation techniques on extracted small image patches. Along
with the prediction of various anomalies, the category of infill pattern
and location information on the image patches can be accurately determined.
It is envisioned that using our detection system to categorize and
localize printing anomalies, real-time autonomous correction of process
parameters can be realized to achieve high-quality tissue constructs
in 3D-bioprinting processes.
Although fused deposition modeling (FDM) additive manufacturing technologies have advanced in the past decade, interlayer imperfections such as delamination and warping are still dominant when printing complex parts. Herein, a selfmonitoring system based on real-time camera images and deep learning algorithms is developed to classify the various extents of delamination in a printed part. In addition, a novel method incorporating strain measurements is established to measure and predict the onset of warping. Results show that the machine-learning model is capable of detecting different levels of delamination conditions, and the strain measurements setup successfully reflects and determines the extent and tendency of warping before it actually occurs in the print job. This multifunctional system can be applied to assess other manufacturing processes to realize autocalibration and prediagnosis of imperfections without human interaction.
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