Abstract

Additive manufacturing is experiencing a surge in popularity, with affordable 3D printers utilizing Fused Deposition Modelling (FDM) technology becoming commonplace among hobbyists. While once a niche technology, these printers are now accessible at reduced costs. However, the quality and reliability of 3D-printed objects are susceptible to numerous variables, posing risks of harm, material wastage, and time loss in case of failures. Addressing this, the integration of computer vision and machine learning for failure detection is crucial. Existing 3D print failure datasets face limitations in detecting failures in diverse environments and lighting conditions. To overcome this, our proposed architecture framework enhances modularity within applications, enabling customization for machine learning models and datasets. This allows end-users to train the model with their specific printing environment dataset and seamlessly integrate it with currently available control software. Our architecture aims to bridge the gap between theoretical concepts and real-world applications, ultimately improving the overall reliability and effectiveness of 3D printing technology. In experimental trials, the detection model trained with a custom dataset demonstrated superior performance. Using the same printing configuration, the pretrained model achieved 100% stringing test failure detection, 80% for boats, 42.86% for name plates, and 12.5% for action figures. With additional custom data, detection rates improved to 100% for stringing tests, 90% for boats, 71.43% for name plates, and 62.5% for action figures.

Original languageEnglish
Title of host publicationProceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023
EditorsHsing-Chung Chen, Cahya Damarjati, Christian Blum, Yessi Jusman, Siti Nurul Aqmariah Mohd Kanafiah, Waleed Ejaz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages227-231
Number of pages5
ISBN (Electronic)9798350359633
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Information Technology and Computing, ICITCOM 2023 - Hybrid, Yogyakarta, Indonesia
Duration: 1 Dec 20232 Dec 2023

Publication series

NameProceeding - International Conference on Information Technology and Computing 2023, ICITCOM 2023

Conference

Conference2023 International Conference on Information Technology and Computing, ICITCOM 2023
Country/TerritoryIndonesia
CityHybrid, Yogyakarta
Period1/12/232/12/23

Keywords

  • 3D printer
  • computer vision
  • dataset customization
  • failure detection
  • software architecture

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