Results for “Deep Learning” — Page 30

NASA NTRS2014

The Ergonomics of Human Space Flight: NASA Vehicles and Spacesuits

Space...the final frontier...these are the voyages of the starship...wait, wait, wait...that's not right...let's try that again. NASA is currently focusing on developing multiple strategies to prepare humans for a future trip to Mars. This includes (1) learning and characterizing the human system while in the weightlessness of low earth orbit on the International Space Station and (2) seeding the creation of commercial inspired vehicles by providing guidance and funding to US companies. At the same time, NASA is slowly leading the efforts of reestablishing human deep space travel through the development of the Multi-Purpose Crew Vehicle (MPCV) known as Orion and the Space Launch System (SLS) with the interim aim of visiting and exploring an asteroid. Without Earth's gravity, current and future human space travel exposes humans to micro- and partial gravity conditions, which are known to force the body to adapt both physically and physiologically. Without the protection of Earth's atmosphere, space is hazardous to most living organisms. To protect themselves from these difficult conditions, Astronauts utilize pressurized spacesuits for both intravehicular travel and extravehicular activities (EVAs). Ensuring a safe living and working environment for space missions requires the creativity of scientists and engineers to assess and mitigate potential risks through engineering designs. The discipline of human factors and ergonomics at NASA is critical in making sure these designs are not just functionally designed for people to use, but are optimally designed to work within the capacities specific to the Astronaut Corps. This lecture will review both current and future NASA vehicles and spacesuits while providing an ergonomic perspective using case studies that were and are being carried out by the Anthropometry and Biomechanics Facility (ABF) at NASA's Johnson Space Center.

Reid, Christopher R.↗

NASA NTRS2016

Development of a High Performance, Low Profile Translation Table with Wire Feedthrough for a Deep Space CubeSat

NEAScout, a 6U cubesat and secondary payload on NASA's EM-1, will use an 85 sq m solar sail to travel to a near-earth asteroid at about 1 Astronomical Unit (about 1.5 x 10(exp 8) km) for observation and reconnaissance1. A combination of reaction wheels, reaction control system, and a slow rotisserie roll about the solar sail's normal axis were expected to handle attitude control and adjust for imperfections in the deployed sail during the 2.5-year mission. As the design for NEAScout matured, one of the critical design parameters, the offset in the center of mass and center of pressure (CP/CM offset), proved to be sub-optimal. After significant mission and control analysis, the CP/CM offset was accommodated by the addition of a new subsystem to NEAScout. This system, called the Active Mass Translator (AMT), would reside near the geometric center of NEAScout and adjust the CM by moving one portion of the flight system relative to the other. The AMT was given limited design space - 17 mm of the vehicle's assembly height-and was required to generate +/-8 cm by +/-2 cm translation to sub-millimeter accuracy. Furthermore, the design must accommodate a large wire bundle of small gage, single strand wire and coax cables fed through the center of the mechanism. The bend radius, bend resistance, and the exposure to deep space environment complicates the AMT design and operation and necessitated a unique design to mitigate risks of wire bundle damage, binding, and cold-welding during operation. This paper will outline the design constraints for the AMT, discuss the methods and reasoning for design, and identify the lessons learned through the designing, breadboarding and testing for the low-profile translation stages with wire feedthrough capability.

Few, Alex↗

NASA NTRS2016

Development of a High Performance, Low-Profile Translation Table with Wire Feedthrough

NEAScout, a 6U cubesat, will use an 85 sq m solar sail to travel to a near-earth asteroid for observation. Over the course of the 3-year mission, a combination of reaction wheels, cold gas reaction control system, and a slow rotisserie roll about the solar sail's normal axis were expected to handle attitude control and adjust for imperfections in the deployed sail. As the design for NEAScout matured, one of the critical design parameters, the offset in the center of mass and center of pressure (CP/CM offset), proved to be sub-optimal. After significant mission and control analysis, the CP/CM offset was addressed and a new subsystem was introduced to NEAScout. This system, called the Active Mass Translator (AMT), would reside near the geometric center of NEAScout and adjust the CM by moving one portion of the flight system relative to the other. The AMT was given limited design space-about 17 mm of the vehicle's assembly height-and was required to generate +/-10 cm by +/-5 cm translation to sub-millimeter accuracy. Furthermore, the design must accommodate a large wire bundle of small gage, single strand wire and coax cables fed through the center of the mechanism. The bend radius, bend resistance, and the exposure to deep space environment complicates the AMT design and operation and necessitated a unique design to mitigate risks of wire bundle damage, binding, and cold-welding during operation. This paper will outline the design constraints for the AMT, discuss the methods and reasoning for design, and identify the lessons learned through the design downselect process and breadboarding for designing low-profile translation stages with feedthrough capabilities.

Few, Alex↗

NASA NTRS2016

Development of a High-Performance, Low-Profile Translation Table with Wire Feedthrough for a Deep Space CubeSat

NEAScout, a 6U cubesat and secondary payload on NASA's EM-1, will use an 85 sq m solar sail to travel to a near-earth asteroid at about 1 Astronomical Unit (about 1.5 x 10(exp 8) km) for observation and reconnaissance1. A combination of reaction wheels, reaction control system, and a slow rotisserie roll about the solar sail's normal axis were expected to handle attitude control and adjust for imperfections in the deployed sail during the 2.5-year mission. As the design for NEAScout matured, one of the critical design parameters, the offset in the center of mass and center of pressure (CP/CM offset), proved to be sub-optimal. After significant mission and control analysis, the CP/CM offset was accommodated by the addition of a new subsystem to NEAScout. This system, called the Active Mass Translator (AMT), would reside near the geometric center of NEAScout and adjust the CM by moving one portion of the flight system relative to the other. The AMT was given limited design space - 17 mm of the vehicle's assembly height-and was required to generate +/-8 cm by +/-2 cm translation to sub-millimeter accuracy. Furthermore, the design must accommodate a large wire bundle of small gage, single strand wire and coax cables fed through the center of the mechanism. The bend radius, bend resistance, and the exposure to deep space environment complicates the AMT design and operation and necessitated a unique design to mitigate risks of wire bundle damage, binding, and cold-welding during operation. This paper will outline the design constraints for the AMT, discuss the methods and reasoning for design, and identify the lessons learned through the designing, breadboarding and testing for the low-profile translation stages with wire feedthrough capability.

Few, Alex↗

NASA NTRSDate not supplied

Microservice Architecture for Cognitive Networks

This develops the concept of a cognitive network and describes a microservice based architecture which could be used to implement such a system. Delay tolerant networking (DTN) influences the design of the architecture as well as the networking scenarios that the system attempts to address. A cognitive storage and fragmentation service is developed based on existing artificial intelligence techniques such as Advantage Actor Critic (A2C) and Deep Q-Networks. The system is simulated using OpenAI Gym in a custom developed DTN environment.

cognitive networks↗

NASA NTRS1985

Mark 4A project training evaluation

A participant evaluation of a Deep Space Network (DSN) is described. The Mark IVA project is an implementation to upgrade the tracking and data acquisition systems of the dSN. Approximately six hundred DSN operations and engineering maintenance personnel were surveyed. The survey obtained a convenience sample including trained people within the population in order to learn what training had taken place and to what effect. The survey questionnaire used modifications of standard rating scales to evaluate over one hundred items in four training dimensions. The scope of the evaluation included Mark IVA vendor training, a systems familiarization training seminar, engineering training classes, a on-the-job training. Measures of central tendency were made from participant rating responses. Chi square tests of statistical significance were performed on the data. The evaluation results indicated that the effects of different Mark INA training methods could be measured according to certain ratings of technical training effectiveness, and that the Mark IVA technical training has exhibited positive effects on the abilities of DSN personnel to operate and maintain new Mark IVA equipment systems.

Stephenson, S. N.↗

NASA NTRS2010

The Heliophysics Data Environment, Virtual Observatories, NSSDC, and SPASE

Heliophysics (the study of the Sun and its effects on the Solar System, especially the Earth) has an interesting data environment in that the data are often to be found in relatively small data sets widely scattered in archives around the world. Within the last decade there have been more concentrated efforts to organize the data access methods and create a Heliophysics Data and Model Consortium (HDMC). To provide data search and access capability a number of Virtual Observatories (VO's) have been established both via funding from the U.S. National Aeronautics and Space Administration (NASA) and through other funding agencies in the U.S. and worldwide. At least 15 systems can be labeled as Heliophysics Virtual Observatories, 9 of them funded by NASA. Other parts of this data environment include Resident Archives, and the final, or "deep" archive at the National Space Science Data Center (NSSDC). The problem is that different data search and access approaches are used by all of these elements of the HDMC and a search for data relevant to a particular research question can involve consulting with multiple VO's - needing to learn a different approach for finding and acquiring data for each. The Space Physics Archive Search and Extract (SPASE) project is intended to provide a common data model for Heliophysics data and therefore a common set of metadata for searches of the VO's and other data environment elements. The SPASE Data Model has been developed through the common efforts of the HDMC representatives over a number of years. We currently have released Version 2.1. of the Data Model. The advantages and disadvantages of the Data Model will be discussed along with the plans for the future. Recent changes requested by new members of the SPASE community indicate some of the directions for further development.

Thieman, James↗

NASA NTRS2009

Compact Deep-Space Optical Communications Transceiver

Deep space optical communication transceivers must be very efficient receivers and transmitters of optical communication signals. For deep space missions, communication systems require high performance well beyond the scope of mere power efficiency, demanding maximum performance in relation to the precious and limited mass, volume, and power allocated. This paper describes the opto-mechanical design of a compact, efficient, functional brassboard deep space transceiver that is capable of achieving megabyte-per-second rates at Mars ranges. The special features embodied to enhance the system operability and functionality, and to reduce the mass and volume of the system are detailed. System tests and performance characteristics are described in detail. Finally, lessons learned in the implementation of the brassboard design and suggestions for improvements appropriate for a flight prototype are covered.

DSOCT↗

NASA NTRS2019

Trends in Human Spaceflight: Failure Tolerance, High Reliability and Correlated Failure History

In a half century of human spaceflight, NASA has continuously refined agency safety and reliability requirements in response to mission demands, critical failures, and technology development. Early spacecraft, including Mercury, Gemini and Apollo vehicles, were highly reliant on dissimilar redundancy and demonstrated test margins. Later programs, such as the reusable Space Transportation System (STS) and International Space Station (ISS), introduced probabilistic studies and isolated two-failure tolerance to improve robustness at the expense of added complexity. More recently, the Orion Multi-Program Crew Vehicle (MPCV) program adopted universal single-failure tolerance with two categorical exceptions; Zero-Failure Tolerant (0FT) and Design for Minimum Risk (DFMR) hardware. Failure tolerance variances are defined and managed in accordance with agency human-rating requirements, and require concurrence from program Technical Authorities (TA) as well as the MPCV Safety and Mission Assurance Safety and Engineering Review Panel (MSERP). To understand and reaffirm standards applied to Apollo, Space Shuttle and Orion vehicles, Orion and Deep Space Gateway Safety and Mission Assurance (S&MA) representatives conducted accelerated research to compare unique safety and reliability criteria against ground and flight anomalies, based on information contained in post-mission reports and the Problem Reporting and Corrective Action (PRACA) database. In some cases, high-profile failures and narrow escapes have reinforced decisions to maintain or adapt safety requirements. In others, empirical trends have highlighted the need for vigilance and innovative safety guidelines. Given the inability to achieve absolute compliance with evolving safety and reliability requirements, the team conducted a targeted review of DFMR and 0FT propulsion elements within the framework of changing system design, inspection, materials and process developments to formulate conclusions on technological maturity, failure density, and net changes in safety risk. Based on the aggregate performance of high-reliability and failure-tolerant systems, the authors have attempted to establish best practices and guidelines to inform future program decisions. On a somewhat cautionary note, this study is not intended to direct a universal set of requirements for future missions based on prior lessons learned. Spacecraft safety is a multi-variable problem, and attempts to mitigate past failures will not guarantee future success. However, this assessment offers a retrospective review of policy changes, implementation and effectiveness. In the future, NASA, European Space Agency (ESA) and industry partners may benefit from a more robust correlation between requirements and performance, as space-faring nations work toward more challenging, complex and long-duration commercial and deep-space ventures.

Green, Carrie↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRSDate not supplied

From Simulation to Reality With Random Noise

The challenging environment of autonomous vehicle (AV) navigation necessitates certain functions be performed by deep neural networks. Optimizing these models involves collecting vast quantities of domain-specific training data and ensuring that the dataset is representative of expected conditions. High-fidelity simulation plays a vital role in making this process feasible, allowing a wide range of scenarios to be explored at low cost. However, learning from simulation introduces subtle biases into models, which can degrade real-world performance in unpredictable ways. This effect can be mitigated with learning schemes specialized to bridge distributional shifts (transfer learning). Given the complex nature of these methods, the underlying models, and their environments, meaningfully evaluating performance is notstraight forward. Many unrelated factors can effect an improvement in generalization accuracy, but a full ablation analysis is often difficult. To tease out signal from noise, it is necessary to understand how transfer learning performance is affected by noise itself. The goals of this paper are (i) to establish a domain randomization baseline for a simple classification transfer learning task and (ii) to validate the RRAV testbed as a platform for further research in sim-to-real learning. We generate imagery from a simulation of NASA Ames Research Center and train a small convolutional neural network (ConvNet) to classify position relative to a centerline. Further models are trained with different types of noise progressively added to the data. The models are deployed aboard the on-site test vehicle to test real-world performance. In our experiments, we find that such naive domain randomization raises sim-to-real accuracy from 64% to 79%, while training directly on real data yields an 89% accuracy ceiling. These results suggest that the isolated mechanism of domain randomization can significantly improve generalization.

simulation↗

NASA NTRS1989

SSME fault monitoring and diagnosis expert system

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Ali, Moonis↗

NASA NTRSDate not supplied

Interplanetary Terminal Procedure Design Applied to Lunar Landing

The impending NASA Artemis mission to the Moon will enable deep space exploration. The planned cadence of crewed Moon missions under Artemis necessitates scalable, safe lunar landings. This paper describes the development of Interplanetary Terminal Procedure Design (iTERP) which leverages current terminal procedure design criteria augmented with lunar physics to support the exploration of the Moon, Mars and beyond. The iTERPS model combines lessons learned from decades of commercial airspace operations with novelties of human space exploration on other worlds. This paper will highlight some of the key functional areas in procedure design safety, training, and operations reimagined for human space flight.

terminal procedure design↗

NASA NTRSDate not supplied

Interplanetary Terminal Procedure Design Applied to Lunar Landing

The impending NASA Artemis mission to the Moon will enable deep space exploration. The planned cadence of crewed Moon missions under Artemis necessitates scalable, safe lunar landings. This paper describes the development of Interplanetary Terminal Procedure Design (iTERP) which leverages current terminal procedure design criteria augmented with lunar physics to support the exploration of the Moon, Mars and beyond. The iTERPS model combines lessons learned from decades of commercial airspace operations with novelties of human space exploration on other worlds. This paper will highlight some of the key functional areas in procedure design safety, training, and operations reimagined for human space flight.

lunar approach procedures, Artemis Moon base, inte↗

NASA NTRS2016

NEEMO 20: Science Training, Operations, and Tool Development

The 20th mission of the National Aeronautics and Space Administration (NASA) Extreme Environment Mission Operations (NEEMO) was a highly integrated evaluation of operational protocols and tools designed to enable future exploration beyond low-Earth orbit. NEEMO 20 was conducted from the Aquarius habitat off the coast of Key Largo, FL in July 2015. The habitat and its surroundings provide a convincing analog for space exploration. A crew of six (comprised of astronauts, engineers, and habitat technicians) lived and worked in and around the unique underwater laboratory over a mission duration of 14-days. Incorporated into NEEMO 20 was a diverse Science Team (ST) comprised of geoscientists from the Astromaterials Research and Exploration Science (ARES/XI) Division from the Johnson Space Center (JSC), as well as marine scientists from the Department of Biological Sciences at Florida International University (FIU). This team trained the crew on the science to be conducted, defined sampling techniques and operational procedures, and planned and coordinated the science focused Extra Vehicular Activities (EVAs). The primary science objectives of NEEMO 20 was to study planetary sampling techniques and tools in partial gravity environments under realistic mission communication time delays and operational pressures. To facilitate these objectives two types of science sites were employed 1) geoscience sites with available rocks and regolith for testing sampling procedures and tools and, 2) marine science sites dedicated to specific research focused on assessing the photosynthetic capability of corals and their genetic connectivity between deep and shallow reefs. These marine sites and associated research objectives included deployment of handheld instrumentation, context descriptions, imaging, and sampling; thus acted as a suitable proxy for planetary surface exploration activities. This abstract briefly summarizes the scientific training, scientific operations, and tool development conducted during NEEMO 20 with an emphasis on the primary lessons learned.

Graff, T.↗

NASA NTRS2012

Lessons Learned in the Decommissioning of the Stardust Spacecraft

The Stardust spacecraft completed its prime mission in 2006, returning samples from the coma of comet Wild 2 to earth in the sample return capsule. Still healthy, and in a heliocentric orbit, the Stardust spacecraft was repurposed for a new mission - Stardust NExT. This new mission would take the veteran spacecraft to a 2011 encounter with comet Tempel 1, providing a new look at the comet visited in 2005 by the Deep Impact mission. This extended mission for Stardust would push it to the limits of its fuel reserves, prompting several studies aimed at determining the actual remaining fuel on board. The results were used to plan mission events within the constraints of this dwindling resource. The team tracked fuel consumption and adjusted the mission plans to stay within the fuel budget. This effort intensified toward the end of the mission, when a final assessment showed even less remaining fuel than previously predicted, triggering a delay in the start of comet imaging during the approach phase. The flyby of comet Tempel 1 produced spectacular up close views of this comet, imaging previously seen areas as well as new territory, and providing clear views of the location of the 2005 impact. The spacecraft was decommissioned about a month after the flyby, revealing that the fuel tank was now empty after having flown successfully for 12 years, returned comet dust samples to earth, and flown by an asteroid and two comets.

repurposed spacecrafts↗

NASA NTRSDate not supplied

Space Suit Portable Life Support System Fastener and Insert Summary, Issues, and Testing Results

Fasteners are included in every assembly on the Exploration External Mobility Unity Portable Life Support System (xEMU PLSS). However, fasteners and inserts have been the cause of many issues seen on the PLSS during development; furthermore, inserts with thermoplastics are not very well understood. The materials used in the PLSS are sometimes softer and lighter weight metals or thermoplastics which require the installation of inserts to improve the interface strength. In addition, space flight requirements call out the need for an additional running/locking torque, so they do not detach in case of failure of the final torque. The xEMU PLSS has experienced various issues with fasteners and inserts, from the bolt binding in an insert or the running torque failing prematurely. A study has been completed to address the running torque life problem. This study compares the insert and locking feature combinations to determine the combination which maximizes life. The study found that using a locking patch with an insert fails after the first installation. Therefore, it is no longer recommended to use a locking patch fastener with an insert for future installments. For the bolt binding issue, inserts have been installed too deep and have caused the bolt to bind near the final threads of the inserts where the hole is not tapped deep enough. Thermoplastics, on the other end, can either use standard helical inserts or heat set inserts. Another study was performed using tangless inserts and heat inserts installed in thermoplastics to quantify the expected pull-out strength for various insert installation parameters (insert type, temperature, and pressure). This paper will review the issues and lessons learned with fasteners on the xEMU PLSS, provide results of a locking feature study, and provide results and recommended installation settings from a heat insert study.

Ryan Ogilvie↗