| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Layer-wise In-process Monitoring-and-Feedback System Based on Surface Characteristics Evaluated by Machine-Learning-Generated Criteria |
| Author(s) |
Toshi-Taka Ikeshoji, Makiko Yonehara, Kenta Aoyagi, Kenta Yamanaka, Akihiko Chiba, Hideki Kyogoku, Michiaki Hashitanani |
| On-Site Speaker (Planned) |
Toshi-Taka Ikeshoji |
| Abstract Scope |
In the laser powder bed fusion (PBF-LB) process, a set of parameters that are considered optimal are selected. Still, a set of parameters cannot accommodate complex model geometries, model placement in the build chamber, and unforeseen circumstances, leading to internal defects. Therefore, a new in-situ monitoring and feedback system has been developed to suppress the occurrence of lack-of-fusion (LOF) defects in the PBF-LB process. This system measures surface properties after each laser irradiation to predict whether LOF defects occur. Then, if necessary, a feedback process is performed to re-melt the same surface. Evaluation thresholds are defined by a combination of aerial surface texture parameters created in advance by machine learning of surface properties and defect occurrence. For example, a square pillar of Inconel 718 alloy built with feedback had a higher relative density than one without feedback. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |