喷墨打印纸印刷适性预测模型

目的研究广义回归神经网络对喷墨打印质量进行预测的可能性。方法测试不同喷墨打印纸的定量、平滑度、白度、光泽度、粗糙度等印刷适性,在相同条件下打印后测量印刷品色度,利用广义回归神经网络结合印刷品色度指标与喷墨打印纸印刷适性指标,并建立预测模型。结果基于广义回归神经网络的预测模型预测得到印刷品最小色差达到4.7215,最大色差达到4.8638。结论该模型可以定量描述喷墨打印纸印刷适性对印刷品色差的影响,为选纸提供试验及理论依据。

Prediction Model of Ink Jet Printing Paper Printability

Printing adaptabilities like quantitativeness, smoothness, whiteness, gloss and roughness of different inkjet printing paper were tested in order to study the feasibility of predicting ink jet printing quality by generalized regression neural network. Chroma was measured after printed under the same printing conditions. And a prediction model was established by utilizing generalized regression neural network combined with printing chroma and printing printability of ink jet printing paper. As predicted by the prediction model based on generalized regression neural network, the minimum average color difference of the GRNN model was 4.7215, and the average maximum color difference was 4.8638. The results show that the model can quantitatively describe the effect of ink jet printing paper printability on chromatic aberration of printing products and provide experimental and theoretical basis for the selection of paper.