Laser offline measurement method based on self-mixing interference for thin-wall fused deposition modelling component

Lin, Feng, Ali, Mubasher, Tan, Yuanfu, Su, Zhou and Wong, Hay ORCID: 0000-0003-1717-2653 (2023) Laser offline measurement method based on self-mixing interference for thin-wall fused deposition modelling component. JOURNAL OF MANUFACTURING PROCESSES, 102. pp. 131-142.

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Abstract

Fused Deposition Modelling (FDM) is affected by various factors such as process parameters during the layer-by-layer printing process, and the parts will have serious defects and unstable mechanical properties. Therefore, the real-time monitoring of the processing process can better study the relationship between the parameters and defects. In particular, the direct measurement of thin-wall component dimensions and surface defects can better realize the adjustment of process parameters to improve the mechanical properties. However, existing monitoring methods either indirectly predict part quality by checking equipment health situation or directly inspect the component with inflexible field-of-view. Hence, this paper proposes a novel monitoring proof-of-concept prototype based on the phenomenon of laser self-mixing to directly inspect for part quality. The system primarily uses a laser diode with a self-encapsulated photodiode which is capable of collecting optical feedback signal once the laser hits a target. And data processing and calculation are performed on the feedback signal to obtain the geometric size and defect size of the target. The measurement object is FDM single-wall plates. The results show that the laser scanning monitoring system can successfully measure the target, and the measurement accuracy of the average size of waviness defects can reach up to 99.36 %. To the best of our knowledge, this study is the first to apply the self-mixing phenomenon as a metrology technique for the monitoring of an additive manufacturing process.

Item Type:	Article
Uncontrolled Keywords:	Laser measurement, Self -mixing interference, Monitoring, Fused deposition modelling
Divisions:	Faculty of Science and Engineering > School of Engineering
Depositing User:	Symplectic Admin
Date Deposited:	23 Aug 2023 08:49
Last Modified:	08 Sep 2023 07:53
DOI:	10.1016/j.jmapro.2023.07.041
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172301