To address the issues of inaccurate color reproduction and declining print quality caused by dot deformation in offset printing, the work aims to propose a machine learning-based dot compensation method to achieve precise local dot compensation, thereby improving print quality and color reproduction. By acquiring material parameters and equipment parameters of the target print sheet, combined with recent digital print data within one month, the PDC model was trained to generate dot compensation values, enabling accurate local dot compensation. Experimental validation demonstrated that the proposed method reduced the arithmetic mean of dot deformation rate by 30.1% and decreased the arithmetic mean of color reproduction error (ΔE) by 28.2%. Additionally, the method exhibited stable performance across different materials and equipment conditions, demonstrating strong generalizability and adaptability. Further experimental data confirmed the precision and efficiency of the PDC model in local dot compensation, providing robust technical support for the digital and intelligent advancement of the printing industry. The machine learning-based dot compensation method overcomes the limitations of traditional compensation techniques, offering a novel approach for the digital and intelligent development of the printing industry, with broad application prospects.
Key words
print dot compensation /
machine learning /
PDC model /
dot deformation /
color reproduction /
intelligent printing
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