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NASA NTRSDate not supplied

TPSAS-NF1676L-11021-DND

The Orion Pad Abort 1 Flight Test occurred on May 6th, 2010 at White Sands Missile Range in New Mexico. The test was the first for the Orion vehicle and was primarily a test of the Orion Launch Abort System’s ability to execute an abort from the launch pad. The test was successful and in most cases results were well within predicted outcomes. For this test, the thermal control subsystem was an inherent part of the flight operations team. The thermal team provided pre-launch predictions of component temperatures, set component temperature limits, and monitored those limits during the flight operations process as part of the primary flight operations team. The thermal team was also responsible for day-of-flight predictions of the main abort motor’s propellant mean bulk temperature, a key launch constraint for the test. After the test, thermal data was collected, reviewed, and compared against pre-launch predictions showing very good agreement in most areas. Based on the pre-test and flight operations experience, a detailed list of lessons learned has been developed and will be reviewed prior to potential future Orion or other such flight tests.

Joseph F Gasbarre↗
NASA NTRS2014

Fifteen Years of Chandra Operation: Scientific Highlights and Lessons Learned

NASA's Chandra X-Ray Observatory, designed for three years of operation with a goal of five years is now entering its 15-th year of operation. Thanks to its superb angular resolution, the Observatory continues to yield new and exciting results, many of which were totally unanticipated prior to launch. We will review some scientific highlights and present "lessons learned" from the experience of operating this great observatory.

Weisskopf, Martin C.↗
NASA NTRS2015

Multipurpose Pressure Vessel Scanner and Photon Doppler Velocimetry

Critical flight hardware typically undergoes a series of nondestructive evaluation methods to screen for defects before it is integrated into the flight system. Conventionally, pressure vessels have been inspected for flaws using a technique known as fluorescent dye penetrant, which is biased to inspector interpretation. An alternate method known as eddy current is automated and can detect small cracks better than dye penetrant. A new multipurpose pressure vessel scanner has been developed to perform internal and external eddy current scanning, laser profilometry, and thickness mapping on pressure vessels. Before this system can be implemented throughout industry, a probability of detection (POD) study needs to be performed to validate the system’s eddy current crack/flaw capabilities. The POD sample set will consist of 6 flight-like metal pressure vessel liners with defects of known size. Preparation for the POD includes sample set fabrication, system operation, procedure development, and eddy current settings optimization. For this, collaborating with subject matter experts was required. This technical paper details the preparation activities leading up to the POD study currently scheduled for winter 2015/2016. Once validated, this system will be a proven innovation for increasing the safety and reliability of necessary flight hardware.Additionally, testing of frangible joint requires Photon Doppler Velocimetry (PDV) and Digital Image Correlation instrumentation. There is often noise associated with PDV data, which necessitates a frequency modulation (FM) signal-to-noise pre-test. Generally, FM radio works by varying the carrier frequency and mixing it with a fixed frequency source, creating a beat frequency which is represented by audio frequency that can be heard between about 20 to 20,000 Hz. Similarly, PDV reflects a shifted frequency (a phenomenon known as the Doppler Effect) from a moving source and mixes it with a fixed source frequency, which results in a beat frequency. However, for PDV, discerning the signal from the noise is difficult without a moving source to induce the modulation. A rotating wheel is currently being used as the moving source but its configuration is impractical and has cumbersome placement inside the current frangible joint test cell. As a way to combat this problem and verify a satisfactory signal-to-noise ratio, a reflective moving crystal piezo will be used to modulate a beat frequency, and an absorptive target will be used to block the signal in order to determine any back reflection coming from the probe and discern the true signal-to noise ratio. The piezo will be mounted and inserted onto the test table on an extendable telescopic antenna grounded by a magnetic base in the test zone. This piezo configuration will be more compatible within the test zone and allow for easy removal of the disk following acceptable signal verification and prior to frangible joint tests.Additionally, topics of what was learned and smaller tasks given at White Sands Test Facility (WSTF) will be discussed. All statements in this paper are newly gained knowledge of what I have learned, observed, and have done while at WSTF.

Ellis, Tayera↗
NASA NTRS1992

Dimensions of complexity in learning from interactive instruction

Robot systems deployed in space must exhibit flexibility. In particular, an intelligent robotic agent should not have to be reprogrammed for each of the various tasks it may face during the course of its lifetime. However, pre-programming knowledge for all of the possible tasks that may be needed is extremely difficult. Therefore, a powerful notion is that of an instructible agent, one which is able to receive task-level instructions and advice from a human advisor. An agent must do more than simply memorize the instructions it is given (this would amount to programming). Rather, after mapping instructions into task constructs that it can reason with, it must determine each instruction's proper scope of applicability. In this paper, we will examine the characteristics of instruction, and the characteristics of agents, that affect learning from instruction. We find that in addition to a myriad of linguistic concerns, both the situatedness of the instructions (their placement within the ongoing execution of tasks) and the prior domain knowledge of the agent have an impact on what can be learned.

Huffman, Scott B.↗
NASA NTRS1992

Implementations of learning control systems using neural networks

The systematic storage in neural networks of prior information to be used in the design of various control subsystems is investigated. Assuming that the prior information is available in a certain form (namely, input/output data points and specifications between the data points), a particular neural network and a corresponding parameter design method are introduced. The proposed neural network addresses the issue of effectively using prior information in the areas of dynamical system (plant and controller) modeling, fault detection and identification, information extraction, and control law scheduling.

Sartori, Michael A.↗
NASA NTRS2013

Lessons Learned During Instrument Testing for the Thermal Infrared Sensor (TIRS)

The Themal InfraRed Sensor (TIRS) instrument, set to launch on the Landsat Data Continuity Mission in 2013, features a passively cooled telescope and IR detectors which are actively cooled by a two stage cryocooler. In order to proceed to the instrument level test campaign, at least one full functional test was required, necessitating a thermal vacuum test to sufficiently cool the detectors and demonstrate performance. This was fairly unique in that this test occurred before the Pre Environmental Review, but yielded significant knowledge gains before the planned instrument level test. During the pre-PER test, numerous discrepancies were found between the model and the actual hardware, which were revealed by poor correlation between model predictions and test data. With the inclusion of pseudo-balance points, the test also provided an opportunity to perform a pre-correlation to test data prior to the instrument level test campaign. Various lessons were learned during this test related to modeling and design of both the flight hardware and the Ground Support Equipment and test setup. The lessons learned in the pre-PER test resulted in a better test setup for the nstrument level test and the completion of the final instrument model correlation in a shorter period of time. Upon completion of the correlation, the flight predictions were generated including the full suite of off-nominal cases, including some new cases defined by the spacecraft. For some of these ·new cases, some components now revealed limit exceedances, in particular for a portion of the hardware that could not be tested due to its size and chamber limitations.. Further lessons were learned during the completion of flight predictions. With a correlated detalled instrument model, significant efforts were made to generate a reduced model suitable for observatory level analyses. This proved a major effort both to generate an appropriate network as well as to convert to the final model to the required format and yielded additional lessons learned. In spite of all the challenges encountered by TIRS, the instrument was successfully delivered to the spacecraft and will soon be tested at observatory level in preparation for a successful mission launch.

Peabody, Hume L.↗
NASA NTRSDate not supplied

Developmental and Cryogenic Thermal Vacuum (TVAC) Testing Lessons Learned

Developmental thermal vacuum (TVAC) testing is a critical step in maturing hardware designs and validating performance prior to flight qualification. Unlike qualification or acceptance testing, developmental testing provides flexibility to explore design margins, uncover integration challenges, and refine test approaches before formal verification activities begin. This presentation highlights the value of developmental testing while sharing common pitfalls encountered during developmental TVAC campaigns. Lessons learned from hands-on testing experience, including extensive developmental testing at cryogenic temperatures, will be shared in this presentation. Topics include test planning and preparation, instrumentation strategies, contamination control considerations, troubleshooting unexpected anomalies, and approaches for staying on schedule while meeting test objectives. The audience will gain practical insights and best practices that can improve test efficiency, reduce risk, and enhance the overall success of future developmental TVAC efforts.

Mackenzie Byrnes↗
NASA NTRS2012

Hybrid Collaborative Learning for Classification and Clustering in Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events as well as faster responses, such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if learners at individual nodes can communicate with their neighbors. In previous work, methods were developed by which classification algorithms deployed at sensor nodes can communicate information about event labels to each other, building on prior work with co-training, self-training, and active learning. The idea of collaborative learning was extended to function for clustering algorithms as well, similar to ideas from penta-training and consensus clustering. However, collaboration between these learner types had not been explored. A new protocol was developed by which classifiers and clusterers can share key information about their observations and conclusions as they learn. This is an active collaboration in which learners of either type can query their neighbors for information that they then use to re-train or re-learn the concept they are studying. The protocol also supports broadcasts from the classifiers and clusterers to the rest of the network to announce new discoveries. Classifiers observe an event and assign it a label (type). Clusterers instead group observations into clusters without assigning them a label, and they collaborate in terms of pairwise constraints between two events [same-cluster (mustlink) or different-cluster (cannot-link)]. Fundamentally, these two learner types speak different languages. To bridge this gap, the new communication protocol provides four types of exchanges: hybrid queries for information, hybrid "broadcasts" of learned information, each specified for classifiers-to-clusterers, and clusterers-to-classifiers. The new capability has the potential to greatly expand the in situ analysis abilities of sensor networks. Classifiers seeking to categorize incoming data into different types of events can operate in tandem with clusterers that are sensitive to the occurrence of new kinds of events not known to the classifiers. In contrast to current approaches that treat these operations as independent components, a hybrid collaborative learning system can enable them to learn from each other.

Wagstaff, Kiri L.↗
NASA NTRS2025

Navigating Team Dynamics: Automated Detection of Micro-Behaviors Between Team Members Through Longitudinal Interaction Data

The success in future long term space exploration missions will depend on the cooperation, coordination, and mutual understanding among the crew members. Micro-behaviors are momentary, subtle linguistic and paralinguistic indicators of thinking and feeling toward another member of the team (Cortina et al., 2001; Smith & Griffiths, 2022) that can significantly impact team dynamics and influence the overall team performance (Paromita & Chaspari, 2024). Due to their interactive nature, micro-behaviors have a sender (i.e., the team member expressing the micro-behavior) and a target (the team member impacted by the micro-behavior). Detection of these behaviors can assist in avoiding possible conflict among crew members and promoting the overall team success. Our prior research focused on an initial proof of concept of machine learning (ML) models and natural language processing (NLP) techniques that were used for automatically detect micro-behaviors among crew members of the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA) Campaigns 4 and 5 missions (Paromita et al., 2023). Results underscored the importance of incorporating contextual information in the ML models in the form of sentiment analysis, type of task, and dyadic interaction among team members. Here, we expand the scope of our prior work in two ways. First, we assess ML/NLP methods on new behavioral annotations coded using an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). Second, we expand the design of the ML model to preserve information about the role of each team member within the occurrence of the micro-behavior (in contrast to the previous model that only considered the sender and the target without determining the team member role). This allows to consider all team members' contributions in the conversation and model long-term dependencies in the dialogue. Our experiments for this study are conducted on data from 5 teams of the NASA HERA C4 (NASA grant NNX16AQ48G (PI: Bell)). Conversations were extracted from the 1.5 hour Team Interaction Battery (TIB) task that occurred 5 times in-mission per crew. This resulted in a total of 13,058 conversational turns (i.e., 17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls). Our findings with the revised behavioral coding and ML/NLP models indicate a 43.66% macro F1-score (i.e., 38.29% precision (P), 50.8% recall (R)) for a dialog state-tracking model that includes information from the sender only, and a 40.9% F1-score (i.e., 38.7% P, 43.36% R) for the same model that includes information from both the sender and the target of the micro-behavior. These are significantly higher compared to simple random forest models that classify behaviors strictly based on speech content and do not consider iterative team dynamics, achieving a 36.07% F1-score (i.e., 39.04% R, 33.53% P). Our findings demonstrate potential ways to leverage large conversational datasets to better capture complex team dynamics. We will discuss future directions including proposed models that can incorporate additional mission days and tasks beyond the TIB for objectively quantifying team behavior at high temporal resolution in space exploration missions.

Projna Paromita↗
NASA NTRS2021

Secondary Science Teachers’ Implementation of a Curricular Intervention When Teaching With Global Climate Models

In the past decade, emphasis on promoting “climate literacy” in K-16 science classrooms has increased. Teachers play a critical role in cultivating these opportunities, especially in secondary science classrooms. However, most prior climate education research has focused on students and student learning; little is known about how teachers implement climate-focused curricular interventions. Here, we report findings from a concurrent mixed methods, multiple-case study of four secondary science teachers’ implementation of a new, NGSS-aligned, model-centric climate curriculum module grounded in the use of a data-driven, computer-based climate modeling tool—Easy Global Climate Model (EzGCM). We employ multiple data sources, including video-recorded classroom observations, interviews, and instructional artifacts, and both qualitative and quantitative analyses, to investigate how teachers implemented the curriculum. Findings show that, overall, teachers implemented the curriculum in ways that were less model-centric than designed, placing greater emphasis on EzGCM itself rather than using the model to investigate Earth’s changing climate. Additionally, we present detailed single-case studies of each participant teacher that highlight differences in teachers’ implementation of the curriculum module and their reasoning for making observed instructional decisions. This research sheds light on the design of secondary science learning environments by illustrating the varied ways teachers implement a climate-focused curriculum to support students’ developing climate literacy. This has important implications for the design of climate-focused curriculum and supports for teachers.

Secondary science teaching↗
NASA NTRS1994

Learning time series for intelligent monitoring

We address the problem of classifying time series according to their morphological features in the time domain. In a supervised machine-learning framework, we induce a classification procedure from a set of preclassified examples. For each class, we infer a model that captures its morphological features using Bayesian model induction and the minimum message length approach to assign priors. In the performance task, we classify a time series in one of the learned classes when there is enough evidence to support that decision. Time series with sufficiently novel features, belonging to classes not present in the training set, are recognized as such. We report results from experiments in a monitoring domain of interest to NASA.

Manganaris, Stefanos↗
NASA NTRS2020

A Reinforcement Learning Framework for Space Missions in Unknown Environments

A land-and-traverse mission to icy worlds such as Europa and Enceladus is challenging due to lack of prior knowledge regarding the terrain conditions. Previous work [1] showed that rovers with high degrees of freedom (DoF) can achieve robust traversal by leveraging redundant modes for mobility to counter terrain uncertainty (e.g. walking, driving, or inch-worming). This paper presents a generic and scalable reinforcement learning scheme for enabling on-board decision making on rovers to automatically switch between modes of traversal based on online performance feedback. The objective is to maximize energy efficiency, minimize operator input and successfully negotiate unstructured terrain conditions without relying on exhaustive prior knowledge. The proposed methodology is well grounded in the literature on reinforcement learning and has been adapted to address conformance to validation and verification requirements and JPL flight operations history of using per-sol prescribed sequences for a space mission.

Tavallali, Peyman↗
NASA NTRSDate not supplied

Preserving and Curating the Moon: Adventures in Lunar Core Processing

The lunar crust is the most easily accessible part of the Moon to both remote sensing and sample analyses and provides an archive of information about planetary formation, crustal evolution, and contains a wealth of information about the origin of the Earth-Moon system [e.g., 1-5]. The Apollo mission returned 382 kg of rocks, soil and core samples. Studies of these lunar samples are crucial for our understanding of the Moon’s formation and geological evolution, and for the past 50 years these returned samples have provided the foundation for lunar science [5]. The returned samples are stored and cared for in the lunar curation facility at NASA’s Johnson Space Center. This facility is comprised of a large suite of clean rooms, sample vaults for pristine and return samples, thin section labs, core and saw rooms, storage and working areas, and ancillary labs all designed to minimize contamination from the environment and other samples. Some of the returned samples were intentionally set aside and left unopened. Recently, the Apollo Next Generation Sample Analysis (ANGSA) initiative was designed to examine these pristine samples so the next generation of lunar scientists can further our insight into the Moon’s history. Here, we present the meticulous process that involves preparing for, and ultimately opening, one of the unopened core samples: Apollo 17 drive tube 73002,0,which was collected on the Moon from a landslide deposit near Lara Crater by astronauts Gene Cernan and Jack Schmitt. In order to open, examine, and curate 73002,0withminimalpotential contamination, great care had to be taken prior to opening its container. Beginning18 months before extrusion of the sample, all core processing equipment was pulled out of storage, identified, sorted, cleaned, and purged with nitrogen gas. However, limited institutional memory has made this step challenging as most of the former core processors from the Apollo area have retired or passed away. Twelvemonths prior to extrusion, table-top rehearsals were initiated to identify equipment and learn how it fits together and operates. Five months before extruding the real core, preparations further evolved to include the extrusion and dissection of a lunar core simulant. In addition, a mock-up glovebox was designed and built to allow for a more realistic practice environment. One month prior to extrusion, the actual core cabinet was prepared for use, which included fitting it with lights, a webcam, and power. The tool and equipment cleaning procedure was also modified to include increased cleanliness and sterility requirements. While still sealed, the core was CT scanned at the University of Texas at Austin to maximize its scientific return. Days before the extrusion, witness plates and foil were deployed inside the core cabinet to monitor potential particle and organic contamination within the cabinet. On Nov. 5th, 2019, core sample 73002,0 was successfully opened and extruded(Fig.1). Dissection of 73002,0 began immediately afterwards and is still under way. Processing this sample will help us prepare for future sampling missions and core extrusions and will enable new scientific discoveries about the Moon.

C H Krysher↗
NASA NTRS2005

Real-Time Principal-Component Analysis

A recently written computer program implements dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN), which was described in Method of Real-Time Principal-Component Analysis (NPO-40034) NASA Tech Briefs, Vol. 29, No. 1 (January 2005), page 59. To recapitulate: DOGEDYN is a method of sequential principal-component analysis (PCA) suitable for such applications as data compression and extraction of features from sets of data. In DOGEDYN, input data are represented as a sequence of vectors acquired at sampling times. The learning algorithm in DOGEDYN involves sequential extraction of principal vectors by means of a gradient descent in which only the dominant element is used at each iteration. Each iteration includes updating of elements of a weight matrix by amounts proportional to a dynamic initial learning rate chosen to increase the rate of convergence by compensating for the energy lost through the previous extraction of principal components. In comparison with a prior method of gradient-descent-based sequential PCA, DOGEDYN involves less computation and offers a greater rate of learning convergence. The sequential DOGEDYN computations require less memory than would parallel computations for the same purpose. The DOGEDYN software can be executed on a personal computer.

Duong, Vu↗
NASA NTRS2003

Residual Gas Effects on Detached Solidification in Microgravity

Many microgravity directional solidification experiments yielded ingots with portions that grew without contacting the ampoule wall, leading to greatly improved crystallographic perfection. Our long-term goal is to make such detached solidification reproducible, which requires a full understanding of the mechanisms underlying it. Our Moving Meniscus Model of steady-state detachment predicts that it depends strongly on the surface tension of the melt and the advancing contact angle with the ampoule wall. Detached solidification is more likely when the contact angle for the melt on the ampoule wall is high, i.e. non-wetting. It has been claimed that impurities increase the contact angle. The objective of the current project is to determine the influence of residual gases on the surface tension and contact angle of molten semiconductors on typical ampoule materials. We are focusing on determining the influence of oxygen on the contact angle of molten InSb on clean silica ('quartz'), including the advancing and retreating contact angles in addition to the usual equilibrium contact angle. We have created a gas flow system that allows us to control the oxygen partial pressure over a sessile drop of InSb on a horizontal quartz surface. The cell is slowly tilted while videotaping to reveal the contact angles on the two sides of the drop just prior to it rolling down the surface. Thus far, we have learned the following: (1) Molten InSb readily forms an oxide layer in the presence of the trace amounts of oxygen found in high purity argon; (2) This oxide contains a substantial amount of Ga, which presumably is a trace contaminant that is not detectable in the starting material; (3) The addition of 10% hydrogen to the argon gas is sufficient to reduce the oxide and produce a clean drop; (4) An infrared filter must precede the video camera in order to produce a sharp image of the drop for later image analysis; (5) Tilting the surface on which the drop rests causes the two sides of the drop to display different contact angles, reflecting contact line sticking; (6) Vibration strongly accelerates the approach of the drop to its final shape on a horizontal surface by helping to overcome sticking of the contact line; (7) Oscillation of the drop surface due to vibration appears to increase as the surface is inclined from horizontal. Presumably, the angle at which the drop rolls down the surface is also reduced by vibration. This observation is particularly significant, as the meniscus must move along the ampoule wall during detached solidification.

Regel, Liya L.↗
NASA NTRS2013

Development of the ISS NORS

This paper will discuss the design and development of the Nitrogen Oxygen Recharge System (NORS) for the International Space Station (ISS). The emphasis of the paper is the collaborative effort between The Boeing Company and Cobham in the crafting of the architecture of the system to meet NASA's requirements. Specific examples to be discussed include the inclusion of the pneumatic isolation valve for compliance with safety requirement, the inclusion of a single stage regulator with flow limiter versus a two-stage regulator, and the use of integrated assemblies as opposed to separate valves/regulators. The paper will also discuss NORS experience with integrated assemblies (common body) verses prior ISS hardware and Cobham trade study efforts, to include lessons learned and potential applications to future programs.

Brandon, Richard↗
NASA NTRS2012

MAVEN Relay Operations Concept

The Mars Atmosphere and Volatile EvolutioN (MAVEN) mission will launch in late 2013 and following a 10 month cruise to Mars, will study the upper atmosphere of the planet. In addition to the science instruments, the MAVEN spacecraft is equipped with an Electra UHF transceiver to support relay communication with landed assets. This paper describes how UHF relay service is provisioned by MAVEN. The discussion includes a description of the Electra payload, the process by which relay activities are coordinated and accounted for, the process of a typical relay session, including uplink and downlink, as well as special commands to calibrate and verify relay performance. The operational processes for providing these services are inherited largely from prior Mars missions and take advantage of existing infrastructure and lessons learned from those missions. Preliminary data volume return capabilities using adaptive data rates and low-density parity check channel coding are presented.

Electra↗
NASA NTRS2000

NASA's Design and Development of a Field Goniometer Instrument Using Solid Works

With NASA suffering severe funding cutbacks, engineers at NASA are required to produce state-of-the-art hardware with limited personnel and financial resources. In light of these constraints, the new NASA mandate is to build better, faster and cheaper. In April of 1998, Stennis Space Center's Commercial Remote Sensing Program contracted to the Systems Engineering Division at NASA Ames Research Center to develop a device known as a Field Goniometer. A Field Goniometer is a device that measures bi-directional reflectance of a target, such as vegetation, relative to the sun and an imaging system in an aircraft or spacecraft. The device is able to provide a spectral fingerprint of the surface it is measuring in wavelengths from 350nm-2500nm using a hyperspectral imager. To accomplish this project, several obstacles had to be overcome. First, the design had to be completed in less than four months. Second, due to the complexity of the design, the use of solid modeling was highly desirable but most of the group's solid modelers were assigned to other jobs. Third, the amount of funding available from the customer was one half to one third the funding typically expended for a job of this nature. Our choices for this project were to design with standard 2-D CAD systems currently used in-house or train additional engineers on our existing solids package or purchase a new solid model package. The use of a 2D CAD system was very undesirable due to the complexity of the design. Using our existing solids modeler would have required a learning curve for our engineers that would be incompatible with our schedule. Prior to this project, a member of our design group researched the solid modeling industry and decided to purchase SolidWorks. After examining the product for ease of use, modeling capability, training time required and cost, we decided our highest probability of success would be to design with Solidworks. During the design phase, our fabrication group was able to provide input at the very early stages, which added significant benefit to the final product. Fabrication cost and schedule savings have been realized by having complex part geometries translated directly from the SolidWorks design models to Surfcam and other computer-aided manufacturing (CAM) software. This direct model translation capability optimized the fabrication processes. The end result was that we were able to successfully complete the project on time and on budget. Other advantages of using SolidWorks, as cited by the design team, include a rapid negotiation of the initial learning curve, the ability to develop solid model hardware prototypes (used to communicate the design intent to both the customer and the fabricator), and the ability to work as a team collaborating on a large, complex model. These types of tools and efforts represent our response to NASA's challenge to produce higher quality products within shorter design and fabrication times.

Turner, Mark↗