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This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. This one-day program is designed to provide introductory information for those organizations who are considering transitioning from the Aeronautic, Space and Defense industry to the Food & Drug Administration (FDA), Medical Device Manufacturing market. Reviewing essential information necessary to understand and successfully begin the journey to FDA Medical Device approval, this course will examine many of the controls between the AS9100 Standard and FDA Regulations and identify the similarities.

This course is verified by Probitas Authentication as meeting the AS9104/3A requirements for continuing Professional Development. AS13100 and RM13000 define the Problem-Solving standard for suppliers within the aero-engine sector, with the Eight Disciplines (8D) problem solving method the basis for this standard. This two-day course provides participants with a comprehensive and standardized set of tools to become an 8D practitioner. Successful application of 8D achieves robust corrective and preventive actions to reduce the risk of repeat occurrences and minimize the cost of poor quality.

Abstract The use of nanomaterials and nanostructures have been revolutionizing the advancements of science and technology in various engineering and medical fields. As an example, Carbon Nanotubes (CNTs) have been extensively used for the improvement of mechanical, thermal, electrical, magnetic, and deteriorative properties of traditional composite materials for applications in high-performance structures. The exceptional materials properties of CNTs (i.e., mechanical, magnetic, thermal, and electrical) have introduced them as promising candidates for reinforcement of traditional composites. Most structural configurations of CNTs provide superior material properties; however, their geometrical shapes can deliver different features and characteristics. As one of the unique geometrical configurations, helical CNTs have a great potential for improvement of mechanical, thermal, and electrical properties of polymeric resin composites.

Abstract Letter from the Guest Editors

Abstract The objective of this article is to evaluate the effects of different blast protective modules to military vehicle structures and occupants. The dynamic responses of the V-shape integral basic armor, the add-on honeycomb sandwich structure module, and the anti-shock seat-dummy system were simulated and analyzed. The improvements of occupant survivability by different protective modules were compared using occupant injury criteria. The integral armored cab can maintain the integrity of the cab body structure. The add-on honeycomb sandwich armor reduces the peak structural deformation and velocity of the cab floor by 34.9% and 47.4%, respectively, compared with the cab with integral armors only. The integral armored cab with the anti-shock seat or the honeycomb sandwich structures reduces the occupant shock responses below the injury criteria. For different blast threat intensities, the selection of appropriate protective modules can meet protection requirements.

Abstract Soft actuators with pneumatic network have innovative potential applications in medical and rehabilitation areas. The performance of this kind of actuators is determined by the design of chambers and the properties of the active extensible layer and the passive inextensible layer. In this article, actuator with isosceles trapezoidal chambers is proposed. Orthogonal experiment design and finite element method are used to optimize the structure of actuators. Results indicate that adding constrain-limiting paper in the passive layer can significantly reduce the bending radius. Position of the paper in the passive layer also affects the bending radius. Actuators with trapezoidal chambers can have a smaller bending radius compared with that with rectangle chambers. The bending radius decreases as the ratio of short base to long base of trapezoid decreases. Increasing the number density of chambers can further reduce the bending radius.

Abstract Autonomy has the potential to make the most radical impact by significantly reducing the number of soldiers in harm’s way and changing the military paradigm. Benefits of autonomy to improve the Army’s mission capabilities and the rapid evolution of military systems exerts pressure to develop these systems quickly. Since the associated technological development is highly fast paced and stochastic, approaches that develop systems for stochastic future scenarios are required. In this article we present a vision for the autonomous high-mobility military systems for that future. We discuss the ramifications of autonomy in five areas: (1) fleet organization, (2) physical attributes of high-mobility military systems, (3) individual behaviors of autonomous assets, (4) interactions between humans and autonomous systems, and (5) operation and teaming strategies. We present the future vision, implications, requirements, and technological challenges for each of the five areas.

Abstract The advancement in vision sensors and embedded technology created the opportunity in autonomous vehicles to look ahead in the future to avoid potential obstacles and steep regions to reach the target location as soon as possible and yet maintain vehicle safety from rollover. The present work focuses on developing a nonlinear model predictive controller (NMPC) for a high-speed off-road autonomous vehicle, which avoids undesirable conditions including stationary obstacles, moving obstacles, and steep regions while maintaining the vehicle safety from rollover. The NMPC controller is developed using CasADi tools in the MATLAB environment. The CasADi tool provides a platform to formulate the NMPC problem using symbolic expressions, which is an easy and efficient way of solving the optimization problem. In the present work, the vehicle lateral dynamics are modeled using the Pacejka nonlinear tire model.

People at work are frequently distracted (“hijacked”) by a mix of in and out of workplace events, frequently including poor communications. As a result, they sometimes find themselves in situations in which they can be seriously injured or killed. An investigation into the “how” these situations were initiated led to list of six primary causes. Based on this list, an experiential program was developed to help people remain focused on their work, not get hijacked, and improve their communications.

The objective of this study was to optimize the occupant restraint systems for a light tactical vehicle in frontal crashes. A combination of sled testing and computational modeling were performed to find the optimal seatbelt and airbag designs for protecting occupants represented by three size of ATDs and two military gear configurations. This study started with 20 sled frontal crash tests to setup the baseline performance of existing seatbelts, which have been presented previously; followed by parametric computational simulations to find the best combinations of seatbelt and airbag designs for different sizes of ATDs and military gear configurations involving both driver and passengers. Then 12 sled tests were conducted with the simulation-recommended restraint designs. The test results were further used to validate the models. Another series of computational simulations and 4 sled tests were performed to fine-tune the optimal restraint design solutions.

Significant growth of Unmanned Aerial Vehicles (UAV) has unlocked many services and applications opportunities in the healthcare sector. Aerial transportation of medical cargo delivery can be an effective and alternative way to ground-based transport systems in times of emergency. To improve the security and the trust of such aerial transportation systems, Blockchain can be used as a potential technology to manage, operate and monitor the entire process. In this paper, we present a blockchain network solution based on Ethereum for the transportation of medical cargo such as blood, medicines, vaccines, etc. The smart contract solution developed in solidity language was tested using the Truffle program. Ganache blockchain test network was employed to host the blockchain network and test the operation of the proposed blockchain model. The suitability of the model is validated in real-time using a UAV and all the flight data are captured and uploaded into the blockchain.

Project Oculus is an in-flight deployable mechanical arm/pod system that will accommodate 500 pounds of sensor payload, developed for a C-130 military aircraft. The system is designed for use in counter narco-terrorism and surveillance applications by the Department of Defense and the National Guard [1]. A prototype of the system has been built and is in the testing/analysis phase. The purpose of this study was to analyze the actual stresses and strains in the critical areas found using previous Finite Element (FE) simulations and to ensure that acceptable safety requirements have been met. The system components tested will be redesigned, tested, and reconstructed in the case of unacceptable safety factors or if more reliable methods can be implemented. The system was built to be deployed and retracted in flight, to avoid causing any problems in take off and landing.

After manufacture, every military vehicle experiences a unique history of dynamic loads, depending on loads carried, missions completed, etc. Damage accumulates in vehicle structures and components accordingly, leading eventually to failures that can be difficult to anticipate, and to unpredictable consequences for mission objectives. The advent of simulation-based fatigue life prediction tools opens a path to Digital Twin based solutions for tracking damage, and for gaining control over vehicle reliability. An incremental damage updating feature has now been implemented in the Endurica CL fatigue solver with the aim of supporting such applications for elastomer components. The incremental updating feature is demonstrated via the example of a simple transmission mount component. The damage state of the mount is computed as it progresses towards failure under a series of typical loading histories.

To improve the biofidelity of the currently available Hybrid III 10-year-old (HIII-10C) Anthropomorphic Test Device (ATD), the National Highway Traffic Safety Administration (NHTSA) has developed the Large Omnidirectional Child (LODC) ATD. The LODC head is a redesigned HIII-10C head with mass properties and modified skin material required to match pediatric biomechanical impact response targets from the literature. A dynamic, nonlinear finite element (FE) model of the LODC head has been developed using the mesh generating tool Hypermesh based on the three-dimensional CAD model. The material data, contact definitions, and initial conditions are defined in LS-PrePost and converted to LS-Dyna solver input format. The aluminum head skull is stiff relative to head flesh material and was thus modeled as a rigid material. For the actual LODC, the head flesh is form fit onto the skull and held in place through contact friction.

Facial recognition software (FRS) is a form of biometric security that detects a face, analyzes it, converts it to data, and then matches it with images in a database. This technology is currently being used in vehicles for safety and convenience features, such as detecting driver fatigue, ensuring ride share drivers are wearing a face covering, or unlocking the vehicle. Public transportation hubs can also use FRS to identify missing persons, intercept domestic terrorism, deter theft, and achieve other security initiatives. However, biometric data is sensitive and there are numerous remaining questions about how to implement and regulate FRS in a way that maximizes its safety and security potential while simultaneously ensuring individual’s right to privacy, data security, and technology-based equality.

This SAE Aerospace Information Report (AIR) is prepared for stakeholders seeking information about the evolution, integration, and approval of SHM technologies for military aircraft systems. The report provides this information in the form of (a) two military organizations’ perspectives on requirements, and (b) general SHM challenges and industry perspectives. The report only provides information to generate awarness of prespectives for military aircraft and, hence, assists those who are involved in developing SHM systems understanding the broad range of regulations, requirements, and standards published by military organizations that are available in the public domain from the military organizations.

Abstract Fighter pilots must study models of aircraft dynamics before learning complex maneuvers and tactics. Similarly, autonomous fighter aircraft applications may benefit from a model-based learning approach. Instead of using a preexisting physics model of a given aircraft, a machine learning system can learn a predictive model of the aircraft physics from training data. Furthermore, it can model interactions between multiple friendly aircraft, enemy aircraft, and the environment. Such a system can also learn to represent state variables that are not directly observable, as well as dynamics that are not hard coded. Existing model-based methods use a deep neural network that takes observable state information and agent actions as input and provides predictions of future observations as output. The proposed method builds upon this approach by adding a residual feedforward skip connection from some of the inputs to all of the outputs of the deep neural network.

Mobile heavy article tilter and positioner (HATP) is special purpose vehicle designed to level, articulate and positioning of very heavy load within the accuracy of arc minutes and in a stipulated time in fully auto mode. HATP system uses sophisticated electronic controller system to carry out required task in auto mode. This electronic controller system comprises of various types of electronic hardware, software, sensors and actuators. As this system is dealing with heavy load, any failure in any of subsystem of HATP can result into catastrophe. Therefore active and passive safety measure at various levels must be incorporated into system which firstly prevents the failure and reduce the effect of failure. The safety system for HATP system has been divided in three major levels: 1. Access level safety 2. Operational safety 3. Preventive safety. All three levels of safety is incorporated at appropriate subsystem based on Risk Priority Number (RPN) and failure mode effect analysis.

The advancement of both sensory and unmanned technology, combined with increased utilization of autonomous platforms in complex teaming scenarios, has created a need for practical design space exploration tools to aid in the synthesis of effective System-of-Systems (SoS). The presented work describes a modular, flexible, and extensible framework, referred to herein as the Technologies and Teaming Evaluation (TATE) framework, for straightforward identification of high-quality SoS, which may include both manned and autonomous elements, through quantitative evaluation of system-level and SoS-level attributes against a set of user-defined reference tasks.