PDF(Pubmed)
Abstract：
Drop-on-demand printing using colloidal or pigmented inks is prone to the clogging of printing nozzles, which can lead to positional deviations and inconsistently printed patterns (e.g., data matrix codes, DMCs). However, if such deviations are detected early, they can be useful for determining the state of the print head and planning maintenance operations prior to reaching a printing state where the printed DMCs are unreadable. To realize this predictive maintenance approach, it is necessary to accurately quantify the positional deviation of individually printed dots from the actual target position. Here, we present a comparison of different methods based on affinity transformations and clustering algorithms for calculating the target position from the printed positions and, subsequently, the deviation of both for complete DMCs. Hence, our method focuses on the evaluation of the print quality, not on the decoding of DMCs. We compare our results to a state-of-the-art decoding algorithm, adopted to return the target grid positions, and find that we can determine the occurring deviations with significantly higher accuracy, especially when the printed DMCs are of low quality. The results enable the development of decision systems for predictive maintenance and subsequently the optimization of printing systems.
摘要：
使用胶状或着色油墨的按需滴落印刷容易堵塞印刷喷嘴,这可能导致位置偏差和印刷图案不一致(例如，数据矩阵代码，DMC）。然而,如果早期发现这种偏差，它们可用于确定打印头的状态并在达到打印DMC不可读的打印状态之前规划维护操作。为了实现这种预测性维护方法，需要精确地量化单独打印的点与实际目标位置的位置偏差。这里,我们提出了基于亲和力变换和聚类算法从打印位置计算目标位置的不同方法的比较，随后,对于完整的DMC，两者的偏差。因此，我们的方法侧重于打印质量的评估，不是关于DMC的解码。我们将我们的结果与最先进的解码算法进行比较，用于返回目标网格位置，发现我们可以以更高的精度确定发生的偏差，特别是当印刷的DMC是低质量的时候。结果可以开发用于预测性维护的决策系统，并随后优化打印系统。
