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Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset

A shortcoming noted in fused deposition modelling (FDM) 3D printing technology refers to lack of intelligent monitoring and intervention during the printing process. Fail prints can still occur during the printing procedure even though the printer is of industrial grade and far more expens...

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Main Authors: Liwauddin, Muhammad Lut, Ayob, Mohammad Afif, Rohaziat, Nurasyeera
Format: Conference or Workshop Item
Language:English
Published: 2022
Subjects:
T Technology (General)
Online Access:http://eprints.uthm.edu.my/8106/1/P14513_a02973403ca65611a170a9c1731afe3e.pdf
http://eprints.uthm.edu.my/8106/
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spelling my.uthm.eprints.81062022-12-06T03:00:05Z http://eprints.uthm.edu.my/8106/ Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset Liwauddin, Muhammad Lut Ayob, Mohammad Afif Rohaziat, Nurasyeera T Technology (General) A shortcoming noted in fused deposition modelling (FDM) 3D printing technology refers to lack of intelligent monitoring and intervention during the printing process. Fail prints can still occur during the printing procedure even though the printer is of industrial grade and far more expensive than that of hobby grades. Under extrusion has been determined as one of the frequent failures in 3D printing. Such failure stems from insufficient extrusion rate and/or inadequate melting temperature of filament during the print. Under extrusion failure may result in undesired layer gaps, missing layers, unbalanced layers, and even holes in the printed models that would make the models completely unusable. Hence, an effective method that can reduce waste materials and overall costs is by integrating artificial intelligence (AI) into 3D printers. However, a large dataset is required prior to the training process of deep learning. Hence, this study proposes an automated and continuous data collection of under extrusion samples in FDM 3D printers using Raspberry Pi and webcam. As a result, adjustment of the G-code of the standard tessellation language (STL) models and repeated process of printing 3D models can effectively achieve the desired images. 2022 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/8106/1/P14513_a02973403ca65611a170a9c1731afe3e.pdf Liwauddin, Muhammad Lut and Ayob, Mohammad Afif and Rohaziat, Nurasyeera (2022) Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset. In: IEEE 5th International Symposium in Robotics and Manufacturing Automation, 6-8 August 2022, Malacca.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Liwauddin, Muhammad Lut
Ayob, Mohammad Afif
Rohaziat, Nurasyeera
Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
description A shortcoming noted in fused deposition modelling (FDM) 3D printing technology refers to lack of intelligent monitoring and intervention during the printing process. Fail prints can still occur during the printing procedure even though the printer is of industrial grade and far more expensive than that of hobby grades. Under extrusion has been determined as one of the frequent failures in 3D printing. Such failure stems from insufficient extrusion rate and/or inadequate melting temperature of filament during the print. Under extrusion failure may result in undesired layer gaps, missing layers, unbalanced layers, and even holes in the printed models that would make the models completely unusable. Hence, an effective method that can reduce waste materials and overall costs is by integrating artificial intelligence (AI) into 3D printers. However, a large dataset is required prior to the training process of deep learning. Hence, this study proposes an automated and continuous data collection of under extrusion samples in FDM 3D printers using Raspberry Pi and webcam. As a result, adjustment of the G-code of the standard tessellation language (STL) models and repeated process of printing 3D models can effectively achieve the desired images.
format Conference or Workshop Item
author Liwauddin, Muhammad Lut
Ayob, Mohammad Afif
Rohaziat, Nurasyeera
author_facet Liwauddin, Muhammad Lut
Ayob, Mohammad Afif
Rohaziat, Nurasyeera
author_sort Liwauddin, Muhammad Lut
title Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
title_short Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
title_full Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
title_fullStr Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
title_full_unstemmed Continuous data collection of under extrusion in FDM 3D printers for deep-learning dataset
title_sort continuous data collection of under extrusion in fdm 3d printers for deep-learning dataset
publishDate 2022
url http://eprints.uthm.edu.my/8106/1/P14513_a02973403ca65611a170a9c1731afe3e.pdf
http://eprints.uthm.edu.my/8106/
_version_ 1751538658674999296
score 13.211869

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