Data-driven Material Models for Certification by Analysis

Objective: The objective of this study is to minimize reliance on current empirically-based life-prediction methods by building a more physics-based, high fidelity model using advanced microscopy and high-performance computing. Such an advancement would help enable efficient certification of novel materials, such as those from additive manufacturing, with a reduced requirement for costly mechanical testing programs.\n\nInnovativeness: A robust certification method ensures that a fabricated component can perform with high reliability. Currently, certification relies heavily on gathering performance statistics via an exhaustive mechanical testing program. This study aims to eliminate this burden via the fusion of cutting edge experimental techniques and high fidelity computational modeling. The approach centers on the novel combination of advanced microscopy-based tools. The successful coupling of HREBSD, \xb5DIC and material modeling has yet to be shown by any research organization. However, by leveraging our recent work on a novel patterning technique and efficient, in-house computational tools, NASA has the unique opportunity to pioneer this field. Success of this method will allow for the extraction of orders of magnitudes more results from a single experimental test. This technique will allow for the rapid development and validation of accurate material models utilizing high performance computing and only a small population of mechanical tests, thereby increasing the speed and reducing the cost of material certification. The calibrated model will not just be an improved design tool, it will also have enough fidelity for structural health prognosis (DigitalTwin).\n \nImpact: In the near term, this data-driven model development would provide a valuable tool for the certification of COPV liners, which have been identified as a critical issue for NASA space flight systems. Because these structures are so thin, NASGRO and other fatigue-life prediction methods fail to capture the real behavior. With the proposed framework, the accurate and reliable certification can be achieved from around 10 fabricated specimens, instead of the thousands required for conventional certification. This new approach offers a practical path toward the new designs, materials and manufacturing processes needed for Safe Human Travel Beyond LEO. In the long term, the framework discussed here has the potential to revolutionize the certification of materials, particularly novel materials associated with additive manufacturing in a number of other areas. New, additive materials and manufacturing processes, as well as better predictions of the life of the material will allow designers to dramatically reduce the weight of components without compromising reliability. This increased reliability and weight reduction will help enable a number of strategic technologies, including the manufacturability of on demand mobility vehicles (Safe and reliable On-Demand Mobility (ODM) vehicles). The development of a high fidelity fatigue model is essential to the development of the ageless airframe concept, which relies heavily on computational air frame health prognosis (Civil Aviation Transport Concepts). The ability to simulate the safety of a component with an arbitrary and/or complex loading will also alleviate the concerns related to sudden decompression in super-sonic commercial aircraft (Viable Supersonic Transport). In summary, reliability and weight reduction provide massive benefits to all aspects of air and space exploration, and the ability to both rapidly certify and accurate predict the life of novel, additive components will expedite these goals.

Description: Analytical models have the potential to enable high rate manufacturability of additive manufacturing components, but because additive materials are so novel, there is not enough data to create models with sufficient fidelity. Here a hybrid experimental/computational method is presented for rapid certification of novel material systems. By coupling state-of-the-art characterization techniques with advanced computational methods certification by analysis with only a handful of material tests becomes a possibility. The use of these new materials, as well as the ability to better model their fatigue life will result in significant weight reduction and increased reliability across a broad spectrum of air and space applications.\n\n Problem: Because of the difficulty in characterizing material behavior and performance, certification of new aerospace materials can take decades. Conventional models rely on a large number of tests and/or years of historical data. Limitations in the models used to certify components also means that many designs are overly conservative, or in the case of thin structures (which are more and more prevalent as efforts are made to reduce weight), dangerously non-conservative. These material and modeling limitations result in a significant cost in the weight of aircraft and spacecraft components. Additive manufacturing of metals opens up a number of potential solutions in the form of radical new geometries, tailored material behavior/microstructure and new alloys. There is little hope of adequately certifying this burst of innovation using conventional means. Efforts are underway (including integrated computational materials engineering [ICME]) to expedite certification of a material through analytical life-prediction modeling, but calibration of material models often relies on simplistic, macroscopic measurements. In contrast, characterization techniques have progressed dramatically in the last 20 years, with techniques like high resolution electron backscatter diffraction (HREBSD) to unlock the material state at each point in a material and micro-digital image correlation (\xb5DIC) to resolve the local deformation state with high resolution and precision. Despite the wealth of information now available, a disconnect remains between data and models for practical reasons (conventional surface treatments for HREBSD and \xb5DIC are mutually exclusive). While these methods are broadly applicable, this proposed work will focus on thin, additive COPV (composite overwrapped pressure vessel) liners, which are mission critical components (typically fuel tanks) for both air- and spacecraft with difficult to predict failure mechanisms. The objective of this study is to minimize reliance on current empirically based life-prediction methods by building a more physics-based, high fidelity model using advanced microscopy and high-performance computing. Such an advancement would help enable efficient certification of novel materials, such as those from additive manufacturing, with a reduced requirement for costly mechanical testing programs.

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