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This technology is essential for the production of next\u2010generation electronic devices due to its high throughput rates and cost\u2010effective fabrication. However, the assessment of R2R gravure printing outcomes still relies on human visual inspection, introducing subjectivity susceptible to human fatigue. To address this challenge, a novel approach is proposed to quantify printing quality in real\u2010time using deep learning and spatial association. This not only reduces the need for human labor in evaluating the performance of the R2R gravure printing system but also ensures objective quantifiability. Quantification facilitates optimal process settings through response surface methodology, as printing quality can be expressed numerically. 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