Detection of Material Extrusion In-Process Failures via Deep Learning
Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and proble...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Inventions |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2411-5134/5/3/25 |
| _version_ | 1797563580557557760 |
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| author | Zhicheng Zhang Ismail Fidan Michael Allen |
| author_facet | Zhicheng Zhang Ismail Fidan Michael Allen |
| author_sort | Zhicheng Zhang |
| collection | DOAJ |
| description | Additive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm. |
| first_indexed | 2024-03-10T18:45:36Z |
| format | Article |
| id | doaj.art-816767ec28154c699e1163cd0aac0510 |
| institution | Directory Open Access Journal |
| issn | 2411-5134 |
| language | English |
| last_indexed | 2024-03-10T18:45:36Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Inventions |
| spelling | doaj.art-816767ec28154c699e1163cd0aac05102023-11-20T05:31:59ZengMDPI AGInventions2411-51342020-07-01532510.3390/inventions5030025Detection of Material Extrusion In-Process Failures via Deep LearningZhicheng Zhang0Ismail Fidan1Michael Allen2Department of Mechanical Engineering, College of Engineering, Tennessee Tech University, Cookeville, TN 38505, USADepartment of Manufacturing and Engineering Technology, College of Engineering, Tennessee Tech University, Cookeville, TN 38505, USADepartment of Mathematics, College of Arts and Sciences, Tennessee Tech University, Cookeville, TN 38505, USAAdditive manufacturing (AM) is evolving rapidly and this trend is creating a number of growth opportunities for several industries. Recent studies on AM have focused mainly on developing new machines and materials, with only a limited number of studies on the troubleshooting, maintenance, and problem-solving aspects of AM processes. Deep learning (DL) is an emerging machine learning (ML) type that has widely been used in several research studies. This research team believes that applying DL can help make AM processes smoother and make AM-printed objects more accurate. In this research, a new DL application is developed and implemented to minimize the material consumption of a failed print. The material used in this research is polylactic acid (PLA) and the DL method is the convolutional neural network (CNN). This study reports the nature of this newly developed DL application and the relationships between various algorithm parameters and the accuracy of the algorithm.https://www.mdpi.com/2411-5134/5/3/25additive manufacturingmachine learningdeep learningconvolutional neural networkfailure detectionaccuracy |
| spellingShingle | Zhicheng Zhang Ismail Fidan Michael Allen Detection of Material Extrusion In-Process Failures via Deep Learning Inventions additive manufacturing machine learning deep learning convolutional neural network failure detection accuracy |
| title | Detection of Material Extrusion In-Process Failures via Deep Learning |
| title_full | Detection of Material Extrusion In-Process Failures via Deep Learning |
| title_fullStr | Detection of Material Extrusion In-Process Failures via Deep Learning |
| title_full_unstemmed | Detection of Material Extrusion In-Process Failures via Deep Learning |
| title_short | Detection of Material Extrusion In-Process Failures via Deep Learning |
| title_sort | detection of material extrusion in process failures via deep learning |
| topic | additive manufacturing machine learning deep learning convolutional neural network failure detection accuracy |
| url | https://www.mdpi.com/2411-5134/5/3/25 |
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