Browse Topic: Artificial intelligence (AI)
Space membrane structures, prized for their lightness, affordability, and ability to fold and unfold, hold immense promise for missions like deep space exploration, high-definition Earth observation, and advanced satellite communications. However, these structures are typically large-size, ultra-lightweight, highly flexible, and have very low damping. This leads to complex dynamic behavior featuring significant geometric nonlinearity, strong coupling between rigid and flexible motions, and uncertain parameters. Consequently, creating accurate yet simple mathematical models for designing controllers is exceptionally difficult, and traditional control methods that rely on such models often fall short. To tackle this problem, this study focuses on a membrane antenna spacecraft equipped with cable actuators, aiming to suppress its vibrations during attitude maneuvers. Using the Deep Deterministic Policy Gradient (DDPG) algorithm, an intelligent vibration controller based on the Actor-Critic architecture is designed. This approach is data-driven, meaning it learns effective control strategies directly through interaction with the system’s environment, bypassing the need for a precise pre-defined mathematical model. Simulation results show that the controller demonstrated superior vibration suppression and strong adaptability across several key scenarios.
The rapid evolution of satellite communication systems enables direct network access for terrestrial users via portable devices, but also introduces critical security challenges in access control. In particular, identity hijacking attacks allow malicious users to exploit legitimate access channels, leading to inefficient occupation of scarce satellite resources. Existing approaches mainly depend on trust evaluation or heuristic-based decision strategies, which are inadequate for adapting to dynamic environments. To address this issue, this paper proposes a trust-aware deep reinforcement learning (DRL) framework for satellite access control, which jointly considers user behavior evaluation and decision optimization. Specifically, a Bayesian trust evaluation model is first developed to quantify the credibility of user behavior by incorporating historical interaction records as prior information. Unlike conventional static trust schemes, an adaptive mechanism is introduced to enhance sensitivity to behavioral dynamics. Building upon the trust assessment, the satellite access process is formulated as a Markov Decision Process (MDP), where system states integrate channel conditions, user trust levels, and service demands. A Deep Q-Network (DQN) is then employed to learn optimal admission and scheduling policies that maximize long-term system utility under resource constraints. Simulation results demonstrate that the proposed method significantly outperforms conventional greedy and threshold-based strategies. In particular, it effectively mitigates the impact of malicious behaviors and improves system utility by over 80%, while achieving more efficient resource utilization in dynamic environments.
Model-Based Systems Engineering (MBSE) has become a mandated practice for Department of Defense acquisition programs, yet measured benefits remain elusive. The 2024 Defense Science Board found that less than one percent of published literature actually quantified MBSE outcomes, and flagship ground vehicle programs such as the XM30 Infantry Fighting Vehicle have experienced schedule delays attributed directly to insufficient proficiency with model-based approaches. This paper presents the Digital Safety Twin concept: an AI-powered safety intelligence architecture that addresses three of the most labor-intensive and error-prone MBSE workflows. First, the architecture uses hybrid natural language processing and large language model (NLP/LLM) pipelines to auto-formalize unstructured natural language documents into formally structured, traceable requirements. Second, it auto-generates and continuously maintains traceability relationships across requirements, design elements, hazard analyses, and verification artifacts. Third, it provides continuous safety case completeness and confidence assessment through automated Goal Structuring Notation (GSN) synthesis connected to live evidence sources. The approach is grounded in Systems-Theoretic Process Analysis (STPA), the OMG Risk Analysis and Assessment Modeling Language (RAAML), MIL-STD-882E system safety practice, and the UL 4600 safety case framework. We present the methodology, its alignment to the DoD Digital Engineering Strategy, and its applicability to ground vehicle autonomy programs including next-generation infantry fighting vehicles and robotic combat vehicles. We also discuss the limitations, risks, and cultural barriers that must be addressed for AI-augmented safety engineering to achieve acceptance in mission-critical defense applications.
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
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.
Unmanned Aerial Systems (UAS) pose a growing threat on the modern battlefield, demanding rapid detection and characterization capabilities for the warfighter. Existing single-model solutions are inadequate for Counter-UAS (C-UAS), as they struggle across varying ranges and cannot provide detailed contextual information beyond bounding boxes. We present ZEUS (Zero-shot Explainable Universal Segmentation), a multi-model detection and recognition system that integrates several machine learning approaches. ZEUS employs a high-performance UAS detector trained on synthetic, internally collected, and open-source datasets, with real-time capability demonstrated on edge hardware across both electro-optical and infrared modalities. For classification, ZEUS uses a zero-shot approach: detected UAS are segmented and compared against a library of 3D reference models rendered at various poses, enabling identification of new UAS types without retraining. This methodology additionally provides UAS pose and range estimates critical for threat assessment and engagement decisions.
The modern battlefield is increasingly transparent, generating large volumes of open-source data on the use, damage, and loss of military vehicles. This paper presents a structured methodology to exploit such data for deriving operational requirements for future vehicles. It uses a mixed-method framework combining qualitative reporting with quantitatively verified loss data. Daily battlefield reports are analyzed with large language models to extract operational context, employment patterns, and tactical conditions. These insights are cross-referenced with loss data to assess how operational factors affect vehicle survivability, with the findings being used to prioritize requirements that improve vehicle performance. The approach is demonstrated through a case study of Leopard tanks in the Russia-Ukraine war, using Institute for the Study of War reports and Oryxspioenkop loss data. Results show how open-source intelligence can systematically inform survivability, mobility, and combat effectiveness in modern vehicle design.
Biomanufacturing uses microorganisms to produce chemicals or materials of interest, much like a brewery uses fermentation by yeast to produce the alcohol in beer. Biomanufacturing relies upon synthetic biology to reprogram yeast or other microorganisms to produce something of greater value, such as fuel, food, or pharmaceuticals. Industrial biomanufacturing has made significant advances and the products it can deliver include reactive coatings and textiles, sensors, optical materials that can bend light, and new therapeutics such as antimicrobials and vaccines. The convergence of synthetic biology, robotics, and artificial intelligence is opening the way to produce materials never before possible in the commercial market. These same technologies create the opportunity for the miniaturization of this technology to fit into ever more compact spaces, bringing forward deployment of these mini-factories closer and closer to the point of need.
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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.
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Drum brake systems are becoming increasingly important in electric vehicles (EV) and purpose-built vehicles due to cost competitiveness and EURO-7 particulate emission regulations. Despite this trend, drum brake friction behavior remains incompletely characterized due to its dependence on multiple coupled variables: temperature history, braking conditions, and component interactions. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF model. Model performance was evaluated by comparing predicted friction torque with measured torque, and prediction error was quantified by using Mean Absolute Error (MAE) to check whether predicted model is reliable. The proposed prediction model demonstrates a high level of agreement with experimental measurements, confirming that the MERF approach can effectively capture the non-linear and transient characteristics of drum brake friction torque from time-series sensor signals. These results indicate that friction torque estimation is feasible using only sensor signals already available from conventional test instrumentation, without additional dedicated sensors. This capability is expected to support broader applications, including brake performance prediction for vehicles equipped with drum brakes and enhanced simulation of drum brake thermal performance across operating conditions.
It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to validate and even calibrate chassis controls features. In current state of the art HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces, which in turn can cause hysteretic behavior (a difference in the output for a given input, depending on whether the brake is applying or releasing against the hysteretic friction). This hysteresis is neglected in most state of the art HIL simulations. Past studies by General Motors have shown that the importance of brake corner hysteresis in vehicle level, customer facing performance of chassis controls features can range from inconsequential to significant. With the crescendo-ing demand for high quality non-vehicle based methods for assessing chassis controls function, the effect of hysteresis is no longer academic. The present study starts with HIL based simulations, establishing the effect of brake corner hysteresis on one of the most visible chassis controls behaviors. An inertia dynamometer-based test was developed to exercises the subject brake corners through apply and release cycles, thus enabling any hysteretic behavior to be observed and characterized. Machine Learning models were trained with these data to represent brake corner hysteretic behavior and then deployed into an HIL simulation rig. The impact of these models – representing brake corner hysteretic behavior – was characterized for straight line stopping distance on low, medium, and high coefficient road surfaces.
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