State Recognition of Multi-Nozzle Electrospinning Based on Image Processing.

Weiqi Gao, Jiaxin Jiang, Xiang Wang, Wenwang Li, Gaofeng Zheng

Abstract read

In one paragraph

Article in Micromachines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Weiqi GaoDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361102, China.

Jiaxin JiangSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.

Xiang WangSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.ORCID 0000-0002-5718-9575

Wenwang LiSchool of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen 361024, China.

Gaofeng ZhengDepartment of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361102, China.ORCID 0000-0003-0870-6166

Funding

Fujian Province Science and Technology Association 2020H6003Fujian Province Science and Technology Association 2021J011196Fujian Province Science and Technology Association 2022H6036Guangdong Province Science and Technology Library 2022A1515010923Guangdong Province Science and Technology Library 2022A1515010949National Natural Science Foundation of China 52275575

6 · The paper itself

Abstract

The online monitoring of a multi-jet electrospinning process is critical to the achievement of stable mass electrospinning for industrial applications. In this study, the construction of an ejection state recognition system of a multi-jet electrospinning process based on image processing is reported. The ejection behaviors regarding multi-nozzle electrospinning were recorded by CMOS industrial cameras in real time. The characteristic information regarding the multi-jet cone tip was obtained by processing the images regarding Roberts operator edge detection, Hough transform line detection, and mask histogram analysis. The jet anomalies of the hanging droplets in the nozzle outlet area could be obtained and identified by the vision. The identification accuracy towards the target hanging droplets was more than 85%. This work reports the intelligent control of large-scale multi-nozzle electrospinning equipment.

Indexed as

jet-state identificationmulti-nozzle electrospinningopen-cv image processing

Identifiers

PMID36984935

DOI10.3390/mi14030529

PMCPMC10055950

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