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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/8106/ http://eprints.uthm.edu.my/8106/1/P14513_a02973403ca65611a170a9c1731afe3e.pdf |
| _version_ | 1848889300758822912 |
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| author | Liwauddin, Muhammad Lut Ayob, Mohammad Afif Rohaziat, Nurasyeera |
| author_facet | Liwauddin, Muhammad Lut Ayob, Mohammad Afif Rohaziat, Nurasyeera |
| author_sort | Liwauddin, Muhammad Lut |
| building | UTHM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T20:23:59Z |
| format | Conference or Workshop Item |
| id | uthm-8106 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:23:59Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-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. |
| 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 |
| title | 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_short | 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 |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/8106/ http://eprints.uthm.edu.my/8106/1/P14513_a02973403ca65611a170a9c1731afe3e.pdf |