Opt Express. 2023 Apr 10;31(8):13486-13502. doi: 10.1364/OE.487526.
A high prediction accuracy of optical printer models is a prerequisite for accurately reproducing visual attributes (color, gloss, translucency) in multimaterial 3D printing. Recently, deep-learning-based models have been proposed, requiring only a moderate number of printed and measured training samples to reach a very high prediction accuracy. In this paper, we present a multi-printer deep learning (MPDL) framework that further improves data efficiency utilizing supporting data from other printers. Experiments on eight multi-material 3D printers demonstrate that the proposed framework can significantly reduce the number of training samples thus the overall printing and measurement efforts. This makes it economically feasible to frequently characterize 3D printers to achieve a high optical reproduction accuracy consistent across different printers and over time, which is crucial for color- and translucency-critical applications.
光学打印机模型的高精度预测是准确再现多材料 3D 打印中视觉属性（颜色、光泽、半透明度）的前提。最近，已经提出了基于深度学习的模型，它们只需要中等数量的打印和测量训练样本即可达到非常高的预测精度。在本文中，我们提出了一种多打印机深度学习（MPDL）框架，该框架利用来自其他打印机的支持数据进一步提高了数据效率。在八台多材料 3D 打印机上的实验表明，所提出的框架可以显著减少训练样本的数量，从而减少整体打印和测量工作量。这使得频繁地对 3D 打印机进行特性描述变得经济可行，从而实现跨不同打印机和随时间推移的高光学再现精度，这对于颜色和半透明度关键应用至关重要。
