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Item type:Publication, Access status: without files , Automated defect detection in clay printingAdditive manufacturing (AM) of eco-friendly materials has the potential to decarbonize the construction industry by enabling the creation of complex structures with minimal waste. Clay has been integrated into AM processes as a building material, giving rise to an emerging research field referred to as “clay printing”. Defects, such as tearing and sagging, are common in clay printing and could affect the structural integrity, load-bearing capacity, and overall durability of the structures. However, limited research on defect detection in clay printing and lack of datasets restrict the development of defect detection models. This paper presents a tool - the automated defect detection (ADD) preprocessor - developed to generate a dataset for defect detection models in clay printing. The tool uses images and videos as input for preprocessing and labeling images required to build the dataset, meeting the requirements of defect detection models based on convolutional neural networks. The ADD preprocessor is implemented and validated as a proof of concept for clay printing processes. The results demonstrate the capability of the ADD preprocessor to successfully build a dataset for the deployment of defect detection models in clay printing.Publicationtype: Conference Paper12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Automated defect detection in fused filament fabrication coupling deep learning and computer visionFused filament fabrication (FFF) is an additive manufacturing technique, popular due to its versatility and cost-effectiveness. However, FFF machines, such as 3D printers, are prone to runtime errors, wasting time and material, while requiring constant human supervision. This paper presents a defect detection approach for FFF processes based on artificial intelligence, combining convolutional neural networks and computer vision. The defect detection approach is validated using 3D prints designed to mimic common FFF defects. The results demonstrate the capability of the proposed approach to automatically detect defects in FFF processes, thereby reducing time and material waste as well as the need for human supervision.Publicationtype: Conference Paper20