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Herein, a deep\u2010learning\u2010based computer vision system to correct under\u2010 and over\u2010extrusion issues commonly found in 3D printing technology such as the fused deposition modeling (FDM) is developed. The adaptive correction system is created to acquire recurring images of print\u2010in\u2010progress, allowing pretrained convolutional neural network (CNN) models to classify the printing condition. Then the classification data allow the adaptive system to make subsequent changes of printing parameters in a simple feedback loop to correct printing extrusion in an average of four to eight printed layers. The result shows that the system can improve the strength consistency of the prints by reducing yield strength variance by a\u2009factor of six through in situ correction. This system strengthens weaker prints by up to 200% and can save up to 40% material amount in extreme over\u2010extruded cases. 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