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Browse AllAMS3970/6 Material Specification (MS) defines the requirements of carbon fiber plain weave fabric, 193 g/m2, reinforced epoxy structural prepreg for repair, curing under vacuum at 120 °C (250 °F), and a companion non-structural glass fiber fabric reinforced epoxy prepreg, 105 g/m2, used in repair of carbon fiber reinforced epoxy structures and qualified according to AMS3970/1 and AMS3970/2 for aerospace applications. The prepreg system may include an epoxy film adhesive to be applied in a co-curing process with the prepreg for joint and sandwich bonding. The need for a film adhesive shall be established during screening tests. If included, the requirements to be met by the adhesive are also defined in this document.
This paper presents an efficiently structured and integrated Reliability and Maintainability (R&M) process for implementation in a Digital Engineering (DE) environment during the product design phase, in support of the Army Transformation Initiative. This process links key R&M tasks to influence their execution and clarifies the relationship between a system's design risk and its R&M performance. The significant contributions of this process are: 1) establishing intra- and inter-task linkages for R&M activities within a comprehensive, systemic design process; 2) defining the direct impact on component and system-level R&M as a function of strategic design risk mitigation activities through a central Design Failure Modes and Effects Analysis (DFMEA); and 3) executing R&M tasks within a fully integrated, closed-loop R&M modeling and risk assessment approach, supported by a DE and AI-enabled environment.
Ground combat vehicles traditionally remain in service for decades, yet their rigid architectures make them costly to upgrade and slow to adapt to evolving threats. While the Department of War's 2025 Modular Open Systems Approach (MOSA) mandate aims to address this challenge, implementation barriers persist inconsistent vendor interpretations, physical and logical interoperability gaps, and IP complexities hinder progress. This paper proposes a reformed MOSA framework for ground vehicle Portfolio Acquisition Executives that redefines the government's role from system architect to ecosystem governor. The framework comprises four pillars: tiered standards balancing mandatory physical integration with vendor innovation, digital validation pipelines accelerating compliance verification, dynamic IP rights preventing vendor lock-in, and strategic portfolio management aligning investments with ground vehicle capability priorities. Special emphasis addresses integrating AI capabilities. This reformed approach enables rapid fielding of advanced ground vehicle capabilities at commercial innovation speed.
The U.S. Army’s Modular Open Systems Approach (MOSA) is driving data-centric vehicle architectures that demand higher bandwidth, faster decision loops, and greater cross-platform interoperability. Despite these needs, stakeholders hesitate to adopt fiber optics because of perceived fragility, field-retrofit FOD risk, and soldier-handling concerns. This paper characterizes common fiber failure modes in ground-vehicle environments and demonstrates how system-level ruggedization, connector design, and qualified components mitigate those risks. Drawing on demonstrated experience with hardened optics, connector-integrated transceivers, sealed media converters, and rugged cabling, we summarize practical architecture and field maintenance procedures that enable reliable fiber deployment in vehicles. Results show that, with appropriate component selection, installation practices, and built-in diagnostics, fiber optics can provide a robust, maintainable backbone for future ground vehicle networks.
Shrike Nano provides forward observers and small unmanned aerial system (sUAS) operators with an integrated solution to enhance target prosecution using sUAS video feeds and indirect fire systems. Operable within the Android Tactical Assault Kit (ATAK) ecosystem, Shrike Nano functions as a software plugin that interacts seamlessly with existing tools, including UAS Tool, Robot Picker, and Network Monitor. By utilizing either aided threat recognition (AiTR) or manual targeting workflows, along with passive single-camera geolocation, operators can nominate targets and correct shot placement via digital messaging to enterprise fires terminals such as the Advanced Field Artillery Tactical Data System (AFATDS). The system offers key advantages, including operator standoff capabilities, accurate geolocation, and streamlined fires messaging workflows, all while leveraging low-observable platforms. Shrike Nano seeks to bridge gaps in traditional targeting processes by providing a cohesive and efficient sensor-to-shooter workflow that reduces cognitive load and enables faster, more reliable fire missions at the tactical edge.
Ground vehicle autonomy increasingly depends on human-on-the-loop (HOTL) supervision, yet supervisors are often overloaded by visual interfaces that can obscure emerging risks. This paper presents an AI-driven predictive sonification architecture that converts short-horizon forecasts of platoon behavior into structured auditory cues for supervisory monitoring. A forecasting engine predicts future vehicle interaction states and evaluates predicted and active violations to generate a composite risk indicator. When risk exceeds defined thresholds, a sonification module conveys risk magnitude and trajectory through changes in pitch, loudness, modulation, and spatial panning. The paper describes the system architecture, sonification design, operational use cases, and a planned human-subject evaluation. The proposed framework is intended to improve early awareness of emerging instability and support more timely supervisory intervention.
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Modern mission-critical ground vehicle systems must adapt to rapidly evolving threats, deploying changes in months or days while maintaining reliable and safe operation. Historic manual development and testing methods cannot keep pace without compromising safety assurances. Continuous Integration and Continuous Deployment (CI/CD) pipelines offer proven approaches to accelerating development, but implementing them for mission-critical systems requires careful attention to verification rigor. This paper presents a practical framework for implementing CI/CD pipelines across any level of rigor, from rapid prototyping to DO-178C and ISO 26262 certified systems. Drawing on experience from aviation, medical device, and ground vehicle development, the framework provides guidance for each pipeline stage based on the system’s desired level of rigor. This framework includes an examination of the value of Software-in-the-Loop vs Hardware-in-the-Loop testing to optimize development timelines while maintaining software quality.
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming Computer-Aided Engineering (CAE) workflows by enabling faster design iterations and reducing computational costs. This paper presents the application of Ansys SimAI and Ansys GeomAI in modelling an automotive side impact scenario using high-fidelity data from LS-DYNA simulations. Two AI models are trained on datasets with systematically varied parameters: one encompassing pole impact position and door beam configurations, and another focusing on rocker panel reinforcements. Both models exhibit strong predictive performance, reliably capturing deformation patterns and force-time histories for previously unseen configurations. The datasets are subsequently merged to train a comprehensive surrogate model capable of simultaneously representing variations in pole position, door beam geometry, and rocker reinforcement design, demonstrating robust generalization across a multidimensional design space. To address the emerging bottleneck of geometry creation, GeomAI’s geometry exploration functionality is employed to generate new rocker reinforcement geometries from existing ones, which are then rapidly validated using the pre-trained surrogate model. The results confirm that LS-DYNA simulations can be leveraged effectively to build AI models that dramatically reduce design exploration time. With SimAI and GeomAI in the loop, CAE workflows can evolve from simulation-driven design toward AI-augmented autonomous engineering, where geometry generation, simulation, validation, and optimization converge into an intelligent closed loop.