Chen Danwu, Urban Philipp
Opt Express. 2021 Jan 18;29(2):615-631. doi: 10.1364/OE.410796.
Multi-material 3D printers are able to create material arrangements possessing various optical properties. To reproduce these properties, an optical printer model that accurately predicts optical properties from the printer's control values (tonals) is crucial. We present two deep learning-based models and training strategies for optically characterizing 3D printers that achieve both high accuracy with a moderate number of required training samples. The first one is a Pure Deep Learning (PDL) model that is essentially a black-box without any physical ground and the second one is a Deep-Learning-Linearized Cellular Neugebauer (DLLCN) model that uses deep-learning to multidimensionally linearize the tonal-value-space of a cellular Neugebauer model. We test the models on two six-material polyjetting 3D printers to predict both reflectances and translucency. Results show that both models can achieve accuracies sufficient for most applications with much fewer training prints compared to a regular cellular Neugebauer model.
多材料3D打印机能够创建具有各种光学特性的材料排列。为了再现这些特性，一个能根据打印机控制值（色调）准确预测光学特性的光学打印机模型至关重要。我们提出了两种基于深度学习的模型和训练策略，用于对3D打印机进行光学特性表征，它们在所需训练样本数量适中的情况下都能实现高精度。第一个是纯深度学习（PDL）模型，它本质上是一个没有任何物理基础的黑箱，第二个是深度学习线性化细胞纽格鲍尔（DLLCN）模型，它使用深度学习对细胞纽格鲍尔模型的色调值空间进行多维线性化。我们在两台六材料喷墨式3D打印机上测试了这些模型，以预测反射率和半透明度。结果表明，与常规的细胞纽格鲍尔模型相比，这两种模型都能以少得多的训练打印次数实现足以满足大多数应用的精度。
