MonolayerFFF: An Image Dataset of MonolayerFFF 3D Printed Parts With Different Fabrication Conditions

Abstract

This research presents the MonolayerFFF dataset, a novel addition to Fused Filament Fabrication (FFF) 3D printing image-based datasets, focusing on monolayer parts with unique geometric variations. Unlike existing multi-layered datasets, MonolayerFFF comprises three specific monolayer part conditions: Regular, Defect 1 (with reduced filament deposition), and Defect 2 (featuring filament retraction). This approach offers a different perspective on 3D printing analysis, capturing a range of typical and atypical scenarios. Utilizing the GoogLeNet architecture for Convolutional Neural Network (CNN) classification, the study achieved a notable validation accuracy of 95.81% on the MonolayerFFF dataset, demonstrating its quality and diversity without relying on data augmentation. The high accuracy, coupled with minimal misclassifications as evidenced in both test and validation confusion matrices, underscores the effectiveness of the proposed backbone for feature extraction and classification in identifying subtle differences in part conditions. The study’s outcomes, including the absence of overfitting and aligned loss curves, highlight the model’s capability to generalize from training to unseen data. The MonolayerFFF dataset, with its focus on monolayer parts and detailed variation in conditions, emerges as a significant contribution to advancing CNN analysis in the field of 3D printing. Its potential for real-time monitoring and quality assurance in additive manufacturing promises to enhance the FFF process efficiency and accuracy, making it a valuable tool for researchers in the field.