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Andrew R Kitahara

Publications and source records attributed to Andrew R Kitahara.

6 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Flash Thermal Diffusivity Measurements and Inspection of Additively Manufactured Ti-6Al-4V Specimens with Varying Process Parameters

Flash thermal diffusivity measurements were obtained on additively manufactured Ti-6Al-4V disk shaped specimens with various process parameters. For additively manufactured metal parts, processing parameters such as laser power and scanning speed are critical to ensure the desired microstructure. For this study, the laser powder bed fusion process parameters were changed at various angular sections on a 21 mm diameter and 3.0 mm thick disk. The measurement of thermal diffusivity was performed by fitting a 1-dimensional thermal model to the data pixel by pixel to produce an inspection image. The image revealed the detection of defects such as lack of fusion porosity and areas of aggregated porosity. The thermal diffusivity imagery was compared to immersion scan ultrasonic and X-ray computed tomography (CT) measurements for validation. Based on these results, additional samples were investigated using a single side thermal inspection technique to detect lack of fusion porosity and near surface voids.

thermal nondestructive evaluation↗
NASA NTRSDate not supplied

Towards Qualification and Certification of Laser Powder Bed Fusion Ti-6Al-4V with In-Situ Process Monitoring and Automated Defect Detection

Qualification and certification of laser powder bed fusion (LPBF) parts are two challenges that must be answered to ensure suitability for critical applications. In-situ monitoring using high frame rate thermal and conventional optical imaging sensors is applied to the LPBF build process. Currently, the large volume of data from such sensors becomes untenable for manual inspection in production environments. This presentation serves to address this in-situ monitoring deficiency in two ways. First, a framework for managing data streams from LPBF process monitoring sensors is described. Second, two candidate image analysis techniques are presented: one is a set of heuristics that are easily interpretable, and the other is an uninterpretable convolutional neural network. These strategies are compared in terms of performance, computational expense, and speed. These methodologies represent platforms for connecting processing conditions to process modeling efforts aligned with the qualification and certification mission for LPBF Ti-6Al-4V components.

Qualification↗
NASA NTRSDate not supplied

In-Situ Process Monitoring, Synchronization, and Mapping Laser Powder Bed Fusion Builds of Ti6Al4V

The use of in-situ process monitoring is of interest to lower the cost of inspection for the qualification of laser powder bed fusion (LPBF) parts. Precise monitoring of the LPBF-AM build process constitutes a multi-scale and multi-discipline task. There are several significant challenges to the in-situ approach: the synchronization of sensor signals to process steps, the physical interpretation and classification of sensor signals, managing very large datasets, and comparing the inputs with the observed monitoring signals. At NASA Langley Research Center, a configurable architecture additive testbed has been developed to monitor the build process with synchronized sensors. The philosophy and method adopted for the synchronization of the cameras with laser power & position Ti-6Al-4V LPBF are described. The synchronized in-situ monitoring signals are compared with ex-situ nondestructive inspection and optical microscopy observations. Such comparisons permit a better understanding of how the sequential process actions of LPBF-AM can affect build quality.

Laser Powder Bed Fusion↗
NASA NTRSDate not supplied

Additive Manufacturing Model-Based Process Metrics: Reduced Order Modeling of the Laser Powder Bed Fusion Process

The multi-scale and complex process of printing additively manufactured (AM) parts can have unexpected, but predictable, build conditions that result in material microstructure variability. In this work, we describe a fully parallel reduced order modeling approach that has been developed to evaluate the evolution of AM processes, termed the AM moment measure method. This method couples the known sequence of the AM process with a physically informed nearest neighbors’ calculation to map the conditions of a part-scale build. The result is a map of the build that is derived directly from build files or in-situ process monitoring sensors. The methodology and terminology of the approach will be described, and computed build maps will be calculated and compared for various laser powder bed fusion (LPBF) builds of Ti-6Al-4V. Such comparative results develop understanding of how the sequential process actions can affect the LPBF-AM build quality and microstructure variability.

Laser Powder Bed Fusion↗
NASA NTRSDate not supplied

Database Design Strategies for Coordinated Simulation and Testing in Additive Manufacturing

The qualification and certification (Q&C) process presents a significant challenge for widespread adoption of additive manufacturing (AM) materials and processes for aerospace applications. A relational database framework will be presented as a tool for data curation of coordinated experimental and computational materials modeling research activities. A comparison of relational and hierarchical data structures in this domain will be emphasized through the evolution of a database design strategy. This framework’s mission is to support the advancement of computational materials-informed Q&C by providing the necessary data infrastructure to trace reliability and reproducibility measures through unified AM materials simulation and experimental testing. FAIR (findable, accessible, interoperable, and reusable) data will be highlighted as a necessary precursor for automation of specific actions, which ultimately reduces the time and expense burden for Q&C. The discussion will be mostly limited to back-end design elements, though a few front-end user experience examples will also be shared.

Qualification↗