High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning
Direct printing methods are widely recognized as efficient techniques for manufacturing printed electronics. However, several challenges arise when printing on nonplanar surfaces, especially using the drop‐on‐demand (DoD) approach. These challenges include ink flow due to gravity, precise ink deposi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202400621 |
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| _version_ | 1832592614625378304 |
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| author | Shaheer Mohiuddin Khalil Shahzaib Ali Vu Dat Nguyen Dae‐Hyun Cho Doyoung Byun |
| author_facet | Shaheer Mohiuddin Khalil Shahzaib Ali Vu Dat Nguyen Dae‐Hyun Cho Doyoung Byun |
| author_sort | Shaheer Mohiuddin Khalil |
| collection | DOAJ |
| description | Direct printing methods are widely recognized as efficient techniques for manufacturing printed electronics. However, several challenges arise when printing on nonplanar surfaces, especially using the drop‐on‐demand (DoD) approach. These challenges include ink flow due to gravity, precise ink deposition, and reproducibility. This study introduces an innovative method for highly accurate DoD material jetting on nonplanar 3D conductive surfaces, enabling precise production and trajectory control of charged droplets. The technique involves using a grounded 3D substrate as the target, where in‐flight droplets are subjected to an external electric field generated by gate electrode installed on a piezo activated droplet dispenser. Individual droplets are generated and controlled using a complex trigger system that relays variable‐voltage signals to the gate electrode. Moreover, a predictive model for droplet deposition, exhibiting an accuracy of 87%, is developed utilizing supervised machine learning (ML). This approach significantly improves the accuracy and repeatability of droplet deposition. Overall, this study presents an effective method of integrating piezoelectric and electrohydrodynamic printing technologies, complemented by ML. It addresses the challenges associated with printing on nonplanar surfaces using the DoD material jetting technique and shows considerable promise for enhancing efficiency, accuracy, and repeatability in the manufacturing of printed electronics. |
| format | Article |
| id | doaj-art-fd7a826d81f84401855dce8db8f60474 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-fd7a826d81f84401855dce8db8f604742025-01-21T07:26:27ZengWileyAdvanced Intelligent Systems2640-45672025-01-0171n/an/a10.1002/aisy.202400621High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine LearningShaheer Mohiuddin Khalil0Shahzaib Ali1Vu Dat Nguyen2Dae‐Hyun Cho3Doyoung Byun4Department of Mechanical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaDepartment of Mechanical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaR & D ENJET Co. LTD. Suwon‐Si Gyeonggi‐do 16643 Republic of KoreaDepartment of Mechatronics Engineering Gyeongsang National University Jinju 52725 Republic of KoreaDepartment of Mechanical Engineering Sungkyunkwan University Suwon 16419 Republic of KoreaDirect printing methods are widely recognized as efficient techniques for manufacturing printed electronics. However, several challenges arise when printing on nonplanar surfaces, especially using the drop‐on‐demand (DoD) approach. These challenges include ink flow due to gravity, precise ink deposition, and reproducibility. This study introduces an innovative method for highly accurate DoD material jetting on nonplanar 3D conductive surfaces, enabling precise production and trajectory control of charged droplets. The technique involves using a grounded 3D substrate as the target, where in‐flight droplets are subjected to an external electric field generated by gate electrode installed on a piezo activated droplet dispenser. Individual droplets are generated and controlled using a complex trigger system that relays variable‐voltage signals to the gate electrode. Moreover, a predictive model for droplet deposition, exhibiting an accuracy of 87%, is developed utilizing supervised machine learning (ML). This approach significantly improves the accuracy and repeatability of droplet deposition. Overall, this study presents an effective method of integrating piezoelectric and electrohydrodynamic printing technologies, complemented by ML. It addresses the challenges associated with printing on nonplanar surfaces using the DoD material jetting technique and shows considerable promise for enhancing efficiency, accuracy, and repeatability in the manufacturing of printed electronics.https://doi.org/10.1002/aisy.202400621drop on demandelectrohydrodynamic printingnonplanar substratespredictive models |
| spellingShingle | Shaheer Mohiuddin Khalil Shahzaib Ali Vu Dat Nguyen Dae‐Hyun Cho Doyoung Byun High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning Advanced Intelligent Systems drop on demand electrohydrodynamic printing nonplanar substrates predictive models |
| title | High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning |
| title_full | High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning |
| title_fullStr | High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning |
| title_full_unstemmed | High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning |
| title_short | High‐Precision Drop‐on‐Demand Printing of Charged Droplets on Nonplanar Surfaces with Machine Learning |
| title_sort | high precision drop on demand printing of charged droplets on nonplanar surfaces with machine learning |
| topic | drop on demand electrohydrodynamic printing nonplanar substrates predictive models |
| url | https://doi.org/10.1002/aisy.202400621 |
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