ERI: Nondestructive in-situ Evaluation of Bio-printed Constructs Using Video-based Vibrometry

ERI:使用基于视频的振动测量法对生物打印结构进行无损原位评估

基本信息

项目摘要

Bio-additive manufacturing (bio-AM) has shown great promise for the rapid manufacturing of highly customized biomedical devices and implants. However, one key barrier of bio-AM is that the print quality is substantially below the required industrial standards, partially because of the lack of effective in-situ monitoring and control methods for defect removal. This Engineering Research Initiation (ERI) grant will support basic research that aims to establish non-contact vibration-based monitoring to identify structural defects of bio-constructs as they are being fabricated. The research outcome is expected to create broad socioeconomic impacts on public health and biomedical fields by realizing the potential of bio-AM and strengthening US competitiveness in bioeconomy. The integrated research and educational program, aligned with the national priorities of biomanufacturing and biotechnology, will stimulate student interest in biomanufacturing, expose undergraduate and graduate students to the research frontiers of bio-AM, and develop the next-generation workforce ready for bioeconomy. Understanding the formation mechanism of defects is critical for manufacturing high-quality biomaterials. Most existing sensing methods for biomaterials require that sensors be attached on the surface of additively manufactured parts. Due to the soft and light-weight nature of bio-constructs, contact-type sensors may not be suitable. The research objective is to understand how the non-contact, video-based vibration measurements are correlated with various types of defects. Three research tasks are planned: (1) identifying novel feature-based composite damage metrics that incorporate vibration features reflecting both dimensional and embedded defects; (2) establishing a reliable numerical model to understand how the modal properties of bio-constructs are affected by process conditions such as infill patterns and common defects; and (3) identifying the dynamic properties of bioprinting materials such as hydrogels via video-based vibrometry. The data generated from the project will provide a benchmark for the research community to advance research in biomanufacturing and biotechnology. If successful, this research will tremendously improve the structural integrity of bio-AM organs and increase the availability, ultimately saving lives. The proposed composite damage metrics based on vibration features will provide a new tool for automated in-situ assessment of defects and can accelerate sensing and analytics research in other fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
生物增材制造(bio-AM)在快速制造高度定制的生物医学设备和植入物方面显示出巨大的前景。然而,生物增材制造的一个关键障碍是打印质量大大低于所需的工业标准,部分原因是缺乏有效的原位监测和控制方法来去除缺陷。这项工程研究启动(ERI)资助将支持基础研究,旨在建立基于非接触式振动的监测,以识别生物构建物在制造过程中的结构缺陷。预计该研究成果将对公共卫生和生物医学领域产生广泛的社会经济影响,发挥bio-AM的潜力,加强美国在生物经济领域的竞争力。综合研究和教育计划与生物制造和生物技术的国家优先事项保持一致,将激发学生对生物制造的兴趣,使本科生和研究生接触到生物增材制造的研究前沿,并为生物经济培养下一代劳动力。了解缺陷的形成机制是制造高质量生物材料的关键。大多数现有的生物材料传感方法要求传感器附着在增材制造零件的表面。由于生物结构的柔软和重量轻的性质,接触式传感器可能不适合。研究目的是了解非接触式、基于视频的振动测量如何与各种类型的缺陷相关联。计划开展三个研究任务:(1)识别新的基于特征的复合损伤指标,该指标包含反映尺寸和嵌入缺陷的振动特征;(2)建立可靠的数值模型,以了解生物结构体的模态特性如何受到填充模式和常见缺陷等工艺条件的影响;(3)通过视频振动测量技术识别水凝胶等生物打印材料的动态特性。该项目产生的数据将为研究界提供一个基准,以推进生物制造和生物技术的研究。如果成功,这项研究将极大地改善生物am器官的结构完整性,增加可用性,最终挽救生命。提出的基于振动特征的复合损伤度量将为缺陷的自动原位评估提供新的工具,并可以加速其他领域的传感和分析研究。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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Jinki Kim其他文献

Robust 2D human upper-body pose estimation with fully convolutional network
使用全卷积网络进行稳健的 2D 人体上半身姿势估计
  • DOI:
    10.12989/arr.2018.2.2.129
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Seunghee Lee;Jungmo Koo;Jinki Kim;H. Myung
  • 通讯作者:
    H. Myung
A re-examination of the asymmetric power ARCH model
重新审视不对称权力 ARCH 模型
  • DOI:
    10.1016/j.jempfin.2005.05.002
  • 发表时间:
    2006
  • 期刊:
  • 影响因子:
    2.6
  • 作者:
    M. Karanasos;Jinki Kim
  • 通讯作者:
    Jinki Kim
Deep Learning Model to Improve the Stability of Damage Identification via Output-only Signal
深度学习模型通过仅输出信号提高损伤识别的稳定性
Characterization of hydrogel structures using video-based vibration analysis
使用基于视频的振动分析表征水凝胶结构
Virtual metrology of critical dimensions in etch processes based on automated dynamics–inspired analysis of complete tool signals

Jinki Kim的其他文献

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