AI-assisted reliability assessment for gravure offset printing system

Gafurov, Anton Nailevich; Phung, Thanh Huy; Kim, Inyoung; Lee, Taik-Min

doi:10.1038/s41598-022-07048-z

Cited by 6 publications

(5 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The inspection accuracy of small cracks is low, 79.24%. Presumably, small cracks are generally complex, and the defects are not obvious, which has higher requirements for the detection model [ 39 ]. The inspection accuracy of Xia et al research on small cracks in ceramic defects is higher than in this work.…”

Section: Discussionmentioning

confidence: 99%

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Jin

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The intelligent inspection of ceramic decorative defects is one of the hot research at present. This work aims to improve the defect inspection automation of finished decorative ceramic workpieces. First, it introduces the multi-target detection algorithm and compares the performance of different network models on the public data set. Second, the initial images are collected on the spot. The initial pictures are easy to produce noise in actual deployment, affecting the image quality. Therefore, image preprocessing is performed for the initial images, and a median filtering method is used to calculate the denoising. Finally, the original You Only Look Once version 3 network model is realized. Based on this, the decorative ceramic-oriented Automated Surface Defect Inspection model is proposed. Then, decorative ceramic defect images are inputted for model training. The experimental conclusions are deeply studied and analyzed. The results show that the proposed decorative ceramic-oriented Automated Surface Defect Inspection model based on Deep Learning technology has good feature extraction and inspection ability. The detection accuracy is 94.90% on the test set, and the detection speed reaches 25 frames per second. Compared with the traditional manual inspection method, the proposed model greatly improves the inspection effect and can meet the on-site inspection requirements of surface defects of decorative ceramics under complex backgrounds. It is of great significance to improve the quality inspection efficiency and economic benefits of China’s decorative ceramics industry.

show abstract

Section: Discussionmentioning

confidence: 99%

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Jin

Wang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…With the printing speed increase, Even though the smearing defect was almost zero at the 100th time print for 50 mm/s and 100 mm/s printing speeds, a different kind of defect, the bulge defect [27], occurred. The smearing data were obtained for sequential printing using the chosen AI segmentation model, as shown in Figs.…”

Section: Smearing Area Measuringmentioning

confidence: 95%

“…In this regard, AI approaches [24] became an attractive field in machine vision, and deep neural networks (DNN), including the image classification, object detection, and image segmentation, can solve complex tasks in defect detection. Depending on the size of the inspected area and the type of the defect, the assessment may be a defect classification using the whole image or image patches [25], a local defect detection or localization [26,27], and a continuous or irregular shape defect image segmentation [28]. These DNN methods mainly require supervised training using a custom-built data set with the images of the defects and corresponding labels.…”

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

“…More recently, with the advent of printed electronics technology, which uses functional inks to produce electronic circuits, Brishty et al [28] used machine learning models to analyze the movement of ink droplets and predict the quality of jetting in inkjet printing methods. Gafurov et al [29] proposed a print quality assessment system for printed electronics using gravure offset printing. The system uses a DCNN with skip connections to classify print quality and a pre-trained object detection model to detect defects in the print.…”

Section: Related Workmentioning

confidence: 99%

A Study on DNN-Based Practical Model for Predicting Spot Color

Moon,

Yang,

Tae

2023

Applied Sciences

View full text Add to dashboard Cite

The color of product packaging plays an important role in brand awareness and consistency. Given the importance of consistent color reproduction, the use of standardized spot colors is essential. However, the reproduction of specific spot colors in offset packaging printing involves additional processes and costs. This study presents an efficient approach to predict the color result of spot color inks in the packaging printing industry, using only the amount of ink involved in the mixing process as an input. Using a neural network-based model, our approach uses the CIEDE2000 color difference formula as a loss function to accurately estimate the final color. This method provides a simplified alternative to traditional color mixing techniques, which often involve subjective judgment and can be resource-intensive. Particularly beneficial for smaller companies, our approach reduces the complexity and cost associated with achieving accurate spot colors. The significance of this work lies in its practical application, providing a simpler, more objective and cost-effective solution for consistent color reproduction in packaging printing.

show abstract

AI-assisted reliability assessment for gravure offset printing system

Cited by 6 publications

References 26 publications

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

Inspecting Decorative Ceramic Defects by Fusing Convolutional Neural Network and Image Recognition

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

A Study on DNN-Based Practical Model for Predicting Spot Color

Contact Info

Product

Resources

About