{"title":"增材制造中光束形状对数字光处理打印精度的影响","authors":"Andrew Reid, James Windmill","doi":"10.1089/3dp.2022.0193","DOIUrl":null,"url":null,"abstract":"<p><p>Photopolymerization-based additive manufacturing requires selectively exposing a feedstock resin to ultraviolet (UV) light, which in digital light processing is achieved either using a digital micromirror device or a digital mask. The minimum tolerances and resolution for a multilayer process are separate for resolution through the Z-axis, looking through the thickness of a printed part, and resolution in the XY-axes, in the plane of the printed layer. The former depends wholly on the rate of attenuation of the incident UV light through the material relative to the mechanical motion of the build layer, while the latter is determined by a two-dimensional pattern of irradiance on the resin formed by the digital micromirror device or the digital mask. The size or the spacing of elements or pixels of this digital mask is frequently given by manufacturers as the \"resolution\" of the device, however, in practice the achievable resolution is first determined by the beam distribution from each pixel. The beam distribution is, as standard, modeled as a two-parameter Gaussian distribution but the key parameters of peak intensity and standard deviation of the beam are hidden to the user and difficult to measure directly. The ability of models based on the Gaussian distribution to correctly predict the polymerization of printed features in the microscale is also typically poor. In this study, we demonstrate an alternative model of beam distribution based on a heavy-tailed Lorentzian model, which is able to more accurately predict small build areas for both positive and negative features. We show a simple calibration method to derive the key space parameters of the beam distribution from measurements of a single-layer printed model. We propose that the standard Gaussian model is insufficient to accurately predict a print outcome as it neglects higher-order terms, such as beam skew and kurtosis, and in particular failing to account for the relatively heavy tails of the beam distribution. Our results demonstrate how the amendments to the beam distribution can avoid errors in microchannel formation, and better estimates of the true XY-axes resolution of the printer. The results can be used as the basis for voxel-based models of print solidification that allow software prediction of the photopolymerization process.</p>","PeriodicalId":54341,"journal":{"name":"3D Printing and Additive Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11057548/pdf/","citationCount":"0","resultStr":"{\"title\":\"Impact of Beam Shape on Print Accuracy in Digital Light Processing Additive Manufacture.\",\"authors\":\"Andrew Reid, James Windmill\",\"doi\":\"10.1089/3dp.2022.0193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Photopolymerization-based additive manufacturing requires selectively exposing a feedstock resin to ultraviolet (UV) light, which in digital light processing is achieved either using a digital micromirror device or a digital mask. The minimum tolerances and resolution for a multilayer process are separate for resolution through the Z-axis, looking through the thickness of a printed part, and resolution in the XY-axes, in the plane of the printed layer. The former depends wholly on the rate of attenuation of the incident UV light through the material relative to the mechanical motion of the build layer, while the latter is determined by a two-dimensional pattern of irradiance on the resin formed by the digital micromirror device or the digital mask. The size or the spacing of elements or pixels of this digital mask is frequently given by manufacturers as the \\\"resolution\\\" of the device, however, in practice the achievable resolution is first determined by the beam distribution from each pixel. The beam distribution is, as standard, modeled as a two-parameter Gaussian distribution but the key parameters of peak intensity and standard deviation of the beam are hidden to the user and difficult to measure directly. The ability of models based on the Gaussian distribution to correctly predict the polymerization of printed features in the microscale is also typically poor. In this study, we demonstrate an alternative model of beam distribution based on a heavy-tailed Lorentzian model, which is able to more accurately predict small build areas for both positive and negative features. We show a simple calibration method to derive the key space parameters of the beam distribution from measurements of a single-layer printed model. We propose that the standard Gaussian model is insufficient to accurately predict a print outcome as it neglects higher-order terms, such as beam skew and kurtosis, and in particular failing to account for the relatively heavy tails of the beam distribution. Our results demonstrate how the amendments to the beam distribution can avoid errors in microchannel formation, and better estimates of the true XY-axes resolution of the printer. The results can be used as the basis for voxel-based models of print solidification that allow software prediction of the photopolymerization process.</p>\",\"PeriodicalId\":54341,\"journal\":{\"name\":\"3D Printing and Additive Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11057548/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"3D Printing and Additive Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1089/3dp.2022.0193\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/4/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"3D Printing and Additive Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1089/3dp.2022.0193","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/4/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
引用次数: 0
摘要
基于光聚合的增材制造需要选择性地将原料树脂暴露在紫外线(UV)下,在数字光处理过程中,可以使用数字微镜装置或数字掩膜来实现。多层工艺的最小公差和分辨率分别为通过 Z 轴(观察打印部件的厚度)和 XY 轴(打印层的平面)的分辨率。前者完全取决于入射紫外光通过材料的衰减率与构建层的机械运动的关系,而后者则由数字微镜装置或数字掩膜在树脂上形成的二维辐照图案决定。制造商通常将数字光罩的元件或像素的大小或间距称为设备的 "分辨率",但实际上,可实现的分辨率首先取决于来自每个像素的光束分布。按照标准,光束分布被建模为双参数高斯分布,但光束的峰值强度和标准偏差等关键参数对用户来说是隐藏的,难以直接测量。基于高斯分布的模型正确预测微尺度印刷特征聚合的能力通常也较差。在本研究中,我们展示了基于重尾洛伦兹模型的另一种光束分布模型,它能够更准确地预测正负特征的小构建区域。我们展示了一种简单的校准方法,可从单层印刷模型的测量结果中得出光束分布的关键空间参数。我们提出,标准高斯模型不足以准确预测印刷结果,因为它忽略了高阶项,如光束偏斜和峰度,尤其是没有考虑到光束分布相对较重的尾部。我们的研究结果表明,对光束分布的修正可以避免微通道形成中的误差,并更好地估算出打印机真正的 XY 轴分辨率。这些结果可作为基于体素的印刷凝固模型的基础,从而实现光聚合过程的软件预测。
Impact of Beam Shape on Print Accuracy in Digital Light Processing Additive Manufacture.
Photopolymerization-based additive manufacturing requires selectively exposing a feedstock resin to ultraviolet (UV) light, which in digital light processing is achieved either using a digital micromirror device or a digital mask. The minimum tolerances and resolution for a multilayer process are separate for resolution through the Z-axis, looking through the thickness of a printed part, and resolution in the XY-axes, in the plane of the printed layer. The former depends wholly on the rate of attenuation of the incident UV light through the material relative to the mechanical motion of the build layer, while the latter is determined by a two-dimensional pattern of irradiance on the resin formed by the digital micromirror device or the digital mask. The size or the spacing of elements or pixels of this digital mask is frequently given by manufacturers as the "resolution" of the device, however, in practice the achievable resolution is first determined by the beam distribution from each pixel. The beam distribution is, as standard, modeled as a two-parameter Gaussian distribution but the key parameters of peak intensity and standard deviation of the beam are hidden to the user and difficult to measure directly. The ability of models based on the Gaussian distribution to correctly predict the polymerization of printed features in the microscale is also typically poor. In this study, we demonstrate an alternative model of beam distribution based on a heavy-tailed Lorentzian model, which is able to more accurately predict small build areas for both positive and negative features. We show a simple calibration method to derive the key space parameters of the beam distribution from measurements of a single-layer printed model. We propose that the standard Gaussian model is insufficient to accurately predict a print outcome as it neglects higher-order terms, such as beam skew and kurtosis, and in particular failing to account for the relatively heavy tails of the beam distribution. Our results demonstrate how the amendments to the beam distribution can avoid errors in microchannel formation, and better estimates of the true XY-axes resolution of the printer. The results can be used as the basis for voxel-based models of print solidification that allow software prediction of the photopolymerization process.
期刊介绍:
3D Printing and Additive Manufacturing is a peer-reviewed journal that provides a forum for world-class research in additive manufacturing and related technologies. The Journal explores emerging challenges and opportunities ranging from new developments of processes and materials, to new simulation and design tools, and informative applications and case studies. Novel applications in new areas, such as medicine, education, bio-printing, food printing, art and architecture, are also encouraged.
The Journal addresses the important questions surrounding this powerful and growing field, including issues in policy and law, intellectual property, data standards, safety and liability, environmental impact, social, economic, and humanitarian implications, and emerging business models at the industrial and consumer scales.