Design of printing ink spectral collection system and research on ink proportion prediction method
Wenhao Zhang,
Xinru Liu,
Rui Zhang,
Fei Jiang,
Junjia He,
Shuyang Fang
Affiliations
Wenhao Zhang
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
Xinru Liu
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
Rui Zhang
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
Fei Jiang
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
Junjia He
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
Shuyang Fang
Key Lab for Digital Printing Equipment of Beijing, School of Mechatronic Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
In spectral-based ink content measurement, improving the accuracy of the spectral collection system and reducing the algorithm complexity of the ink content measurement model are important aspects of research. This study designs a spectral collection system with an M-type Czerny–Turner optical path structure, uses a third-order polynomial fitting method for calibration analysis, collects the spectral of the standard color card and denoises it through Savitzky–Golay convolution smoothing, and establishes a functional relationship between the spectral and the logarithm of ink content. Uninformative Variable Elimination (UVE) and Competitive Adaptive Reweighting Sampling are used to perform comparative analysis on feature wavelength extraction, and a prediction model for printing ink content is established. Experimental results show that the R2 of ink C, ink M, and ink Y are 0.9981, 0.9975, and 0.9892, respectively, and the root mean square error is 0.0134, 0.0153, and 0.0317, respectively, showing good performance in ink prediction accuracy.
