Real-Time Stringing Detection for Additive Manufacturing

Abstract

Additive Manufacturing (AM), commonly known as 3D printing, has gained significant traction across various industries due to its versatility and customization potential. However, the process remains time-consuming, with print durations ranging from hours to days depending on the complexity and size of the object. In many cases, errors occur due to object misalignment, material stringing due to nozzle overflow, and filament blockages, which can lead to complete print failures. Such errors often go undetected for extended periods, resulting in substantial losses of time and material. This study explores the implementation of traditional computer vision, image processing, and machine learning techniques to enable real-time error detection, specifically focusing on stringing-related anomalies. To address data scarcity in training machine learning models, we also release a new dataset and improve upon the results achieved by the Obico server model, one of the most prominent tools for stringing detection. Our contributions aim to enhance process reliability, reduce material wastage, and optimize time efficiency in AM workflows