Abstract Additive manufacturing (AM), also known as 3D printing, has revolutionized the landscape of advanced manufacturing. Among various AM techniques, material extrusion (MEX) stands out for its exceptional design freedom, unmatched potential for customization, and significantly reduced product development times. Thermoplastic materials are deposited layer-by-layer in a meticulously controlled manner, enabling the creation of intricate geometries and functional prototypes with remarkable ease. This process simplicity, coupled with the vast array of available materials, has cemented MEX’s position as the most widely adopted AM technique. However, the success of MEX hinges on a critical factor: the precise extrusion and subsequent deposition of material onto the printing surface. Inconsistencies in this process can lead to a multitude of defects, compromising the final product’s structural strength, aesthetics, and functionality. Unfortunately, current limitations exist in real-time monitoring and error detection within MEX processes. Traditional methods often rely on manual inspections, which are time-consuming, prone to human error, and incapable of immediate corrective actions. This research addresses this critical gap by proposing a novel approach that leverages the power of machine learning and cost-effective vision systems. A customized camera module was installed on the MEX extruder, providing a clear view of the localized material deposition area. This strategic placement allowed the camera to capture high-resolution images under various process parameters and layer heights. Flow rate and z-height platform offset were first identified as the primary factors influencing MEX’s material extrusion quality. The total of 291,444 captured images, from different combinations of the flow rates and z-height offsets, formed the foundation for training a modified deep learning algorithm (DL) with convolutional neural network (CNN) and multioutput classification capabilities. By meticulously analyzing the vast dataset of captured images, the proposed DL model learned to identify subtle variations and patterns associated with different material deposition scenarios. This empowered the algorithm to predict multiple process parameter values simultaneously. The trained model demonstrated remarkable performance in real-time prediction of the critical process parameters in a separate test dataset. These exceptional results showcased the immense potential of this novel approach in ensuring consistent and high-quality material deposition within MEX processes. This research presents an effective approach that harnesses the combined power of machine learning and vision systems to revolutionize MEX processes. This work offers a significant advancement over existing literature in material extrusion quality categorical prediction. The proposed system surpasses prior approaches by directly inferring process parameters with high accuracy. This capability paves the way for the development and implementation of automated error detection and correction systems, offering a novel approach for real-time quality control in material extrusion processes. Such advancements will not only minimize process inconsistencies and improve product quality but also unlock the potential for increased automation and intelligence within MEX.
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