In-situ Interferometric Curing Monitoring for Digital Light Processing based Vat Photopolymerization Additive Manufacturing
In-situ Interferometric Curing Monitoring for Digital Light Processing based Vat Photopolymerization Additive Manufacturing
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DOI：
10.1016/j.addma.2024.104001
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发表时间：
2024-02
影响因子：
11
通讯作者：
Yue Zhang;Haolin Zhang;Xiayun Zhao
中科院分区：
文献类型：
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作者：
Yue Zhang;Haolin Zhang;Xiayun Zhao
基于数字光处理（DLP）的瓮光聚合（VPP）是一种增材制造（AM）技术，其投射顺序光学掩模以逐层选择性地固化横截面图案。DLP-VPP广泛应用于从消费品到软机器人等各种产品的快速成型和制造。固化度（DoC）是基于光聚合物的AM工艺的一个主要性能指标，这是由于其与诸如密度和弹性模量的关键材料性质高度相关。然而，缺乏原位监测方法来理解和控制光聚合过程和部件性能。最先进的工作使用原位傅里叶变换红外光谱（FT-IR）和原子力显微镜，这会干扰工艺和材料，每次只能测量一个点的DoC。这项工作的目的是开发一种具有成本效益的，非中断的，非侵入性的，和全场原位干涉固化监测（ICM）的方法，揭示时空分辨固化动力学和材料的演变过程中DLP-VPP。为此，导出了基于物理的传感器模型，并开发了机器学习辅助的传感器数据处理和分析方法，以解决DLP-VPP特定ICM中的独特测量挑战。使用所开发的ICM模型和方法，对所获取的干涉图数据进行清理、分类和计算，以估计每个体素的折射率，折射率是光密度和物理密度的指标。然后，通过将原位ICM测量的折射率与非原位FT-IR测量的DoC相关联来创建DoC预测模型。我们的实验结果表明，所开发的ICM系统和方法能够测量几何形状（例如，横向尺寸和形状）以及捕获固化速度、折射率和DoC的变化，这是由于DLP-VPP中使用的不同曝光掩模和强度。它有可能提供实时多模态测量，并实现闭环反馈控制，以提高DLP-VPP过程的再现性和打印质量。
Digital light processing (DLP) based vat photopolymerization (VPP) is an additive manufacturing (AM) technology that projects sequential optical masks to selectively cure cross-sectional patterns layer by layer. DLP-VPP is widely used in rapid prototyping and fabrication of diverse products ranging from consumer goods to soft robotics. Degree of curing (DoC) is one primary performance metric for photopolymer-based AM processes due to its high correlation with key material properties such as density and elastic modulus. Yet there is a lack of in-situ monitoring approaches to understand and control the photopolymerization process and part properties. State-of-the-art works use in-situ Fourier-transform Infrared Spectroscopy (FT-IR) and atomic force microscopy, which would interfere with the process and material and can only measure DoC at one single point each time. This work aims to develop a cost-effective, non-interruptive, non-invasive, and full-field in-situ interferometric curing monitoring (ICM) method for revealing the spatiotemporally resolved curing dynamics and material evolution during DLP-VPP. To this end, a physics-based sensor model is derived, and machine learning-aided sensor data processing and analytics methods are developed to address the unique measurement challenges in DLP-VPP-specific ICM. Using the developed ICM model and methods, the acquired interferogram data is cleaned, classified, and calculated for estimating each voxel’s refractive index, which is an indicator of optical density as well as physical density. Then, a DoC prediction model is created by correlating the in-situ ICM-measured refractive index to ex-situ FT-IR-measured DoC. Our experiment results demonstrate that the developed ICM system and methods are capable of measuring the geometry (e.g., lateral dimensions and shapes) of printed part as well as capturing the changes in curing speed, refractive index, and DoC due to the different exposure masks and intensities being used in DLP-VPP. It has the potential to provide real-time multi-modality measurement and enable closed-loop feedback control for enhancing the DLP-VPP process reproducibility and print quality.