Multisensor fusion-based digital twin in additive manufacturing for in-situ quality monitoring and defect correction
| Autor: | Chen, Lequn, Yao, Xiling, Liu, Kui, Tan, Chaolin, Moon, Seung Ki |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| Popis: | Early detection and correction of defects are critical in additive manufacturing (AM) to avoid build failures. In this paper, we present a multisensor fusion-based digital twin for in-situ quality monitoring and defect correction in a robotic laser direct energy deposition process. Multisensor fusion sources consist of an acoustic sensor, an infrared thermal camera, a coaxial vision camera, and a laser line scanner. The key novelty and contribution of this work are to develop a spatiotemporal data fusion method that synchronizes and registers the multisensor features within the part's 3D volume. The fused dataset can be used to predict location-specific quality using machine learning. On-the-fly identification of regions requiring material addition or removal is feasible. Robot toolpath and auto-tuned process parameters are generated for defecting correction. In contrast to traditional single-sensor-based monitoring, multisensor fusion allows for a more in-depth understanding of underlying process physics, such as pore formation and laser-material interactions. The proposed methods pave the way for self-adaptation AM with higher efficiency, less waste, and cleaner production. Comment: 11 pages, 9 figures. Accepted at 24th International Conference on Engineering Design (ICED23) |
| Databáze: | arXiv |
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