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

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

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.