A study on the enhancement of the reliability in gravure offset roll printing with blanket swelling control

Kim, Ga Eul; Woo, Kyoohee; Kang, Dongwoo; Jang, Yunseok; Choi, Yun‐Jaie; Lee, Moon Gu; Lee, Taik-Min; Kwon, Sin

doi:10.1088/0960-1317/26/10/105014

Cited by 6 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These may be enhanced by controlling the screen printing process [19] and printing paste [20]. The repeated printing can be analyzed by the reliability assessment, which may help to reveal the faulty regimes with poor printing quality [21,22]. During screen printing, printing defects may happen, in particular, the smearing of the printed lines, which may cause the exaggeration of line width.…”

Section: Introductionmentioning

confidence: 99%

AI-Aided Printed Line Smearing Analysis of the Roll-to-Roll Screen Printing Process for Printed Electronics

Gafurov

Phung

Ryu

et al. 2022

Int. J. of Precis. Eng. and Manuf.-Green Tech.

Self Cite

View full text Add to dashboard Cite

Screen printing has been adopted for fabricating a wide variety of electronic devices. However, the printing defects and reliability have been an obstacle for industrialization of printed electronics. In this research, the artificial intelligence (AI) model was developed and integrated with the in-house roll-to-roll screen printing system to detect smearing defect, which is one of the main defects of screen printing. The U-Net architecture was adopted, and a total of 19 models were designed with model sizes ranging from 8E + 3 to 3E + 7 number of parameters. Their performances as validation mean Intersection over Union (IoU) were analyzed, and the optimal model was chosen with a validation mean IoU of 95.1% and a number of parameters of 8E + 6. The printed line images were evaluated by the AI model for various printing conditions, such as printed line widths, printing paste premixing, printing speeds, and printed line directions, which showed that the model could effectively detect the smearing defects. Also, the AI model capabilities were investigated for repeated printing, which demonstrated that it can be used for the reliability assessment of the screen printing process.

show abstract

Section: Introductionmentioning

confidence: 99%

AI-Aided Printed Line Smearing Analysis of the Roll-to-Roll Screen Printing Process for Printed Electronics

Gafurov

Phung

Ryu

et al. 2022

Int. J. of Precis. Eng. and Manuf.-Green Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…3 ). Kim et al 16 adopted the PDMS blanket swelling control technique with an air blowing unit with humidity- and temperature-controlled airflow, thereby facilitating the evaporation of the solvent from the PDMS blanket. These actions enhanced the gravure offset printing reliability, which was evaluated through the line width measured using a digital camera.…”

Section: Introductionmentioning

confidence: 99%

AI-assisted reliability assessment for gravure offset printing system

Gafurov

Phung

Kim

et al. 2022

Sci Rep

Self Cite

View full text Add to dashboard Cite

In printed electronics, flawless printing quality is crucial for electronic device fabrication. While printing defects may reduce the performance or even cause a failure in the electronic device, there is a challenge in quality evaluation using conventional computer vision tools for printing defect recognition. This study proposed the computer vision approach based on artificial intelligence (AI) and deep convolutional neural networks. First, the data set with printed line images was collected and labeled. Second, the overall printing quality classification model was trained and evaluated using the Grad-CAM visualization technique. Third and last, the pretrained object detection model YOLOv3 was fine-tuned for local printing defect detection. Before fine-tuning, ground truth bounding boxes were analyzed, and anchor box sizes were chosen using the k-means clustering algorithm. The overall printing quality and local defect detection AI models were integrated with the roll-based gravure offset system. This AI approach is also expected to complement more accurate printing reliability analysis firmly.

show abstract

“…PDMS is widely used in various fields and its properties have been studied, [24][25][26] including its characteristics as an offset blanket. 27,28) However, there are no previous reports on its use as a soft blanket.…”

Section: Introductionmentioning

confidence: 99%

Soft blanket gravure printing technology for finely patterned conductive layers on three-dimensional or curved surfaces

Izumi

Yoshida

Tokito

2017

Jpn. J. Appl. Phys.

View full text Add to dashboard Cite

In this paper, we report on a newly developed printing method called the “soft blanket gravure” (SBG) printing method, which is based on a conventional gravure offset printing method but unlike it, SBG printing uses a very soft and thick offset blanket. SBG printing onto various curved, nonplanar surfaces as well as on planar surfaces using silver ink was successfully demonstrated by optimizing printing conditions such as printing pressure and printing speed. Finely printed conductive silver lines with a 30 µm line width were formed on the curved surfaces. This printing method will have new applications in three-dimensional printed electronics.

show abstract

A study on the enhancement of the reliability in gravure offset roll printing with blanket swelling control

Cited by 6 publications

References 16 publications

AI-Aided Printed Line Smearing Analysis of the Roll-to-Roll Screen Printing Process for Printed Electronics

AI-Aided Printed Line Smearing Analysis of the Roll-to-Roll Screen Printing Process for Printed Electronics

AI-assisted reliability assessment for gravure offset printing system

Soft blanket gravure printing technology for finely patterned conductive layers on three-dimensional or curved surfaces

Contact Info

Product

Resources

About