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Ink preset model research based on matrix singular value decomposition method with ink distribution

机译:基于墨分布矩阵奇异值分解方法的墨液预设模型研究

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摘要

The existing ink preset technology ignores the important influence of ink transfer characteristics such as ink backflow and transverse flow. This study constructs a matrix which can quantitatively express the ink distribution characteristic under corresponding graphic coverage by BP neural network. The fact is ink quantity on the substrate cannot achieve desired uniform requirements under the condition of certain graphic coverage, even if printing condition is admirable. This study uses the matrix singular value decomposition method (to obtain ink preset value by truncating singular value), so that the root mean square errors of the ink quantity on the substrate and the standard is less than the prescribed requirement. On the basis of the above research, this study proposes a new ink preset model that takes the influence of ink transverse flow into consideration. The experimental results show that the model can effectively improve the ink preset accuracy, and its application value is appreciable.
机译:现有的墨水预设技术忽略了墨水转移特性(例如墨水回流和横向流动)的重要影响。本研究构建了一个矩阵,该矩阵可以通过BP神经网络定量表示在相应图形覆盖下的墨水分布特征。事实上,即使印刷条件令人钦佩,在一定的图形覆盖率条件下,基板上的墨水量也无法达到所需的统一要求。本研究采用矩阵奇异值分解法(通过截断奇异值获得墨水预设值),使承印物和标准品上墨水量的均方根误差小于规定要求。在上述研究的基础上,本研究提出了一种新的墨水预设模型,该模型考虑了墨水横向流动的影响。实验结果表明,该模型可以有效地提高油墨预置精度,其应用价值是可观的。

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