Non-Destructive Evaluation of Additively Manufactured Parts via Impedance-Based Measurements

使用添加剂制造技术来实现最终用途产品的愿望显著增加，因为它们能够产生复杂的几何形状，这些几何形状可以定制以改善系统性能。然而，由于目前可用的无损评估技术的局限性，添加剂制造的广泛工业应用受到了限制，这阻碍了过程性能的验证。为了满足这一需求，研究小组将探索一种非破坏性评估技术，使用压电材料作为配置的传感器和执行器，同时激励打印部件并测量其动态响应。由于压电材料的机电耦合特性，测量的电阻抗取决于印刷部件的特性(惯性、刚度和阻尼)。因此，由印刷缺陷引起的变化反映在测量的电阻抗上，因此可以容易地检测到。该奖项支持基础研究，以了解零件和缺陷参数如何影响所研究的无损评估过程的检测分辨率和范围。该团队已经制定了一项精心制定的计划，将这项研究的核心概念整合到弗吉尼亚理工大学加强工程多样性中心(CEED)提供的夏令营活动中。将研究融入到CEED代表人数不足的学生的实践实验室体验中，将有助于人力资源的开发，并寻求扩大对与先进制造相关的STEM职业的兴趣。这项研究的主要目标是(I)探索基于阻抗的工艺的灵敏度如何受部件材料和缺陷类型、大小和位置的影响，以及(Ii)对所产生的阻抗响应进行分析，以便能够识别缺陷类型。具有不同尺寸、类型(尺寸误差、位置误差和内部孔隙率)和位置的设计缺陷的零件将通过多种添加剂制造工艺(聚合物挤出、聚合物粉末床熔合和金属粉末床熔合)制造。通过将复合损伤指标表述为多变量优化问题，将测量的基于阻抗的响应与从无缺陷控制样本获得的基线响应进行比较，这将使团队能够了解基于阻抗的技术灵敏度的变化来源。该项目的成果包括：(I)有效验证附加制造部件和识别埋入缺陷的非破坏性评估过程；(Ii)了解基于阻抗的测量如何受激励频率、部件材料和缺陷拓扑的影响；(Iii)了解阻抗响应如何对应于常见的添加制造缺陷类型；以及(Iv)用于基于阻抗的缺陷识别的新型复合损伤度量和框架。在这方面，该项目的成果将对添加剂制造和结构健康监测社区产生重大影响。

There has been a significant increase in the desire to use Additive Manufacturing technologies for the realization of end-use products due to their ability to produce complex geometries that can be tailored to improve system performance. However, broad industrial adoption of Additive Manufacturing has been constrained due to the limitations of currently available non-destructive evaluation technologies, which stymie validation of process performance. To address this need, the research team will explore a non-destructive evaluation technique that uses piezoelectric materials as collocated sensors and actuators to simultaneously excite a printed part and measure its dynamic response. Due to the coupled electromechanical characteristics of piezoelectric materials, the measured electrical impedance depends on the characteristics of the printed part (inertia, stiffness and damping). Therefore, changes induced by printing defects are reflected on the measured electrical impedance, and hence, can readily be detected. This award supports fundamental research to gain an understanding of how part and defect parameters affect the researched non-destructive evaluation process' detection resolution and range. The team has created a well-formulated plan to integrate the central concepts of this research into the summer camp activities offered by Virginia Tech's Center for the Enhancement of Engineering Diversity (CEED). The integration of the research into hands-on laboratory experiences for CEED's underrepresented students will contribute to the development of human resources and seeks to broaden interest in STEM careers related to advanced manufacturing. The primary objectives of this research are (i) to explore how the sensitivity of the impedance-based process is affected by part material and defect type, size, and location, and (ii) to conduct an analysis of the resulting impedance responses to enable identification of defect type. Parts featuring designed defects of various sizes, types (dimensional inaccuracies, positional inaccuracies, and internal porosity), and locations will be fabricated from multiple additive manufacturing processes (polymer extrusion, polymer powder bed fusion, and metal powder bed fusion). The measured impedance-based responses will be compared against baseline responses obtained from defect-free control samples via formulation of the composite damage metric as a multivariable optimization problem, which will enable the team to understand sources of variation in the impedance-based technique's sensitivity. The outcomes of this project include (i) a non-destructive evaluation process for efficiently validating additively manufactured parts and identifying embedded defects; (ii) an understanding of how impedance-based measurements are affected by excitation frequency, part material, and defect topology; (iii) an understanding of how impedance response corresponds to common additive manufacturing defect types; and (iv) a novel composite damage metric and framework for impedance-based defect identification. In this regard, this outcomes of this project will have significant impact on both Additive Manufacturing and Structural Health Monitoring communities.