In-process monitoring of selective laser melting
Metal powder additive manufacturing is one of the fast-growing technologies with a compound annual growth rate (CAGR) of 20.4% growth projected between 2023 to 2032 with a market size predicted to fetch approximately $95.6 billion by 2032. Considering the advantages of creating complex and near-to-f...
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| Format: | Thesis-Doctor of Philosophy |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/179755 |
| _version_ | 1826120795658649600 |
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| author | Hum, Allen Jun Wee |
| author2 | Tuan Tran |
| author_facet | Tuan Tran Hum, Allen Jun Wee |
| author_sort | Hum, Allen Jun Wee |
| collection | NTU |
| description | Metal powder additive manufacturing is one of the fast-growing technologies with a compound annual growth rate (CAGR) of 20.4% growth projected between 2023 to 2032 with a market size predicted to fetch approximately $95.6 billion by 2032. Considering the advantages of creating complex and near-to-finish products in a single process with a shorter lead time, addi- tive manufacturing is well-received by many industries. Most of the market share value went to consumer electronics, automobile, medical, aerospace and transportation industries. However, there is still resistance to adopting additive manufacturing. One of the main concerns is the lack of established quality control methods and standardisation in the printing process. As the me- chanical properties of the printed products depend on process parameters and build conditions setting, their quality is associated with each printed layer. Our research goal in this dissertation is to develop experimental, and analytical methods to monitor the powder bed fusion build pro- cess and use the required monitoring data to establish quality metrics for printed parts.
For our thesis, we begin by examining how anomalies are formed during printing and after the accumulation of several sintered layers. We have reviewed methods for in-situ monitoring, i.e., coaxial and off-axis, during sintering and selected the off-axis method to monitor anomalies present after sintering and recoating the fresh powder layer. As for sensors to acquire the data, We selected a visible light (VI) sensor to provide unstructured data, i.e., image data. We also used another sensor, i.e., thermal imaging (TI), to obtain time-serial temperature information of the entire fabrication process. We then derived methods to extract the data from VI and TI sensors and showed that the correlation between surface quality and thermal data enabled quantitative characterisation of the deformation of the printed specimens. Using the developed methods, We concluded that surface quality strongly depends on the laser energy density.
From our experiments and results, we demonstrate the efficacy of our proposed in-situ process monitoring system, the algorithms and the analytical approach to identify anomalies during the printing process. This work could pave the way for data labelling and engineering for machine learning applications. |
| first_indexed | 2024-10-01T05:22:18Z |
| format | Thesis-Doctor of Philosophy |
| id | ntu-10356/179755 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:22:18Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1797552024-09-04T07:56:36Z In-process monitoring of selective laser melting Hum, Allen Jun Wee Tuan Tran School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing ttran@ntu.edu.sg Engineering Metal powder additive manufacturing is one of the fast-growing technologies with a compound annual growth rate (CAGR) of 20.4% growth projected between 2023 to 2032 with a market size predicted to fetch approximately $95.6 billion by 2032. Considering the advantages of creating complex and near-to-finish products in a single process with a shorter lead time, addi- tive manufacturing is well-received by many industries. Most of the market share value went to consumer electronics, automobile, medical, aerospace and transportation industries. However, there is still resistance to adopting additive manufacturing. One of the main concerns is the lack of established quality control methods and standardisation in the printing process. As the me- chanical properties of the printed products depend on process parameters and build conditions setting, their quality is associated with each printed layer. Our research goal in this dissertation is to develop experimental, and analytical methods to monitor the powder bed fusion build pro- cess and use the required monitoring data to establish quality metrics for printed parts. For our thesis, we begin by examining how anomalies are formed during printing and after the accumulation of several sintered layers. We have reviewed methods for in-situ monitoring, i.e., coaxial and off-axis, during sintering and selected the off-axis method to monitor anomalies present after sintering and recoating the fresh powder layer. As for sensors to acquire the data, We selected a visible light (VI) sensor to provide unstructured data, i.e., image data. We also used another sensor, i.e., thermal imaging (TI), to obtain time-serial temperature information of the entire fabrication process. We then derived methods to extract the data from VI and TI sensors and showed that the correlation between surface quality and thermal data enabled quantitative characterisation of the deformation of the printed specimens. Using the developed methods, We concluded that surface quality strongly depends on the laser energy density. From our experiments and results, we demonstrate the efficacy of our proposed in-situ process monitoring system, the algorithms and the analytical approach to identify anomalies during the printing process. This work could pave the way for data labelling and engineering for machine learning applications. Doctor of Philosophy 2024-08-21T02:21:08Z 2024-08-21T02:21:08Z 2024 Thesis-Doctor of Philosophy Hum, A. J. W. (2024). In-process monitoring of selective laser melting. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/179755 https://hdl.handle.net/10356/179755 10.32657/10356/179755 en This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). application/pdf Nanyang Technological University |
| spellingShingle | Engineering Hum, Allen Jun Wee In-process monitoring of selective laser melting |
| title | In-process monitoring of selective laser melting |
| title_full | In-process monitoring of selective laser melting |
| title_fullStr | In-process monitoring of selective laser melting |
| title_full_unstemmed | In-process monitoring of selective laser melting |
| title_short | In-process monitoring of selective laser melting |
| title_sort | in process monitoring of selective laser melting |
| topic | Engineering |
| url | https://hdl.handle.net/10356/179755 |
| work_keys_str_mv | AT humallenjunwee inprocessmonitoringofselectivelasermelting |