Nondestructive Evaluation of Additively Manufactured Polymers and Composites

Abstract

Technical POC: Thai Tran The emergence of additive manufacturing (AM) of continuous fiber-reinforced polymer matrix composites (CFPMCs) invigorates research to rapidly translate the technology into the application domain. The composite nature of this class of materials gives rise to desirable mechanical properties, including high strength, stiffness, and toughness at a reduced weight penalty. The properties can be further tuned to achieve specific performance criteria through rigorous analyses that consider the fiber orientation, fiber volume fraction, and stacking sequence, to name a few critical parameters. AM can then be used to realize net-shape parts with complex geometry. However, a twofold major shortcoming of AM of CFPMCs is the remnant of traditional composite manufacturing techniques. First, the structural integrity of parts can only be determined post-printing using nondestructive evaluations with limited penetration depth, resulting in less than optimal flaw detection despite the exhaustive effort and the elaborate infrastructure. Second, the properties are also determined post-mortem on sacrificial parts or coupons, resulting in unacceptable manufacturing yield. This proposal aims to overcome these shortcomings by using nonionizing, penetrative, terahertz wave (THz) technology for in-situ imaging and spectroscopy of 3D printed composite parts. The approach to accomplish this goal is to integrate terahertz sources and sensors within the print volume of a state-of-the-art CFPMCs printer, allowing for real-time inspection via the developed THz tomography based on in-situ imaging. The pixel terahertz data is also encoded with molecular information related to the materials mechanical properties. The expected outcomes are detailed tomographic maps of the part while printing and in-situ characterization of mechanical properties, which can be further developed in conjunction with machine learning to abandon print if the required performance metrics are not met. Additionally, this collaborative research will establish a pipeline of highly trained engineers from underrepresented minorities to the naval warfare centers. Publically Releasable

Document Details

Document Type
DoD Grant Award
Publication Date
Mar 12, 2025
Source ID
N001742310009

Entities

People

  • George Youssef

Organizations

  • San Diego State University
  • United States Navy

Tags

Fields of Study

  • Materials science

Readers

  • Reinforced Composite Materials
  • Research Science/Academic Research
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks