Detecção de Defeitos em Impressões 3D com Visão Computacional e Aprendizado Não Supervisionado - Hattori, Marcelo Batalha (Universidade Federal de São Carlos, 2025)
The quality monitoring of 3D-printed components is essential to ensure product reliability and optimize manufacturing processes. Traditional inspection methods often rely on expensive equipment, making them unfeasible for many industries. This study explores the use of accessible and low-cost technologies for defect detection in 3D-printed parts. Images of the manufactured components were captured and analyzed using analytical algorithms, with the assistance of OpenCV for edge detection and image segmentation. The analysis identiﬁed defects such as bubbles, under-extrusion, and over-extrusion. Unsupervised learning, utilizing Kmeans clustering, enabled the classiﬁcation and characterization of defects based on their visual patterns and spatial distribution. The results demonstrate the potential of integrating low-cost computer vision techniques with unsupervised learning to enhance inspection in additive manufacturing.
Defect Detection in 3D Printing with Computer Vision and Unsupervised Learning
Keywords: Impressão 3D; Detecção de defeitos; OpenCV; Aprendizado de máquina; Aprendizado não supervisionado; Manufatura aditiva; 3D printing; Defect detection; Machine learning; Unsupervised learning; Additive manufacturing; ENGENHARIAS::ENGENHARIA MECANICA::PROCESSOS DE FABRICACAO