Browse Topic: Artificial intelligence (AI)

Items (2,432)

Automating Compliance Workflows in Aerospace Systems: A Scalable Framework for Specification Review and Requirement Mapping

2026-26-0714To be published on 06/01/2026

Compliance verification in aerospace systems often relies on labor-intensive workflows that demand extensive manual effort to produce structured review documentation and requirement matrices. These processes can span dozens of hours per review, are vulnerable to inconsistencies due to non-standardized annotations, and depend heavily on individual interpretation of fragmented technical sources. With a growing backlog of review tasks and a steady influx of new requests, the need for scalable automation has become increasingly urgent. This study presents a modular automation framework designed to streamline compliance assessments through intelligent document parsing, requirement extraction, and matrix generation. The system integrates optical character recognition, computer vision, and natural language processing techniques to process both scanned and digital documents. By digitizing data across multiple hardware configurations and automating extraction from diverse technical records, the

Mirani, Harsh, Baviskar, Yash G.

Automated Flight Maneuver Recognition for Intelligent Flight Simulator

2026-26-0713To be published on 06/01/2026

The rapid growth in the number of aircrafts and pilots emphasizes the need for an AI-enabled training framework that can offer precise, automated examination of flight maneuvers, thereby optimizing the pilot’s training efficiency and minimizing iterations of the conduct of flight maneuvers thereby reducing the training time of the pilot for a flight. This paper introduces a novel hybrid methodology to intelligently detect manoeuvres performed during a flight using signal processing and machine learning techniques. A general framework is developed that can be used for all kinds of flight phases and aircraft types. The framework is implemented in a two-step approach. Firstly, Continuous Wavelet Transform (CWT) of the signals of interest for different manoeuvres are generated, to find the time at which a manoeuvre happens during a sortie. CWT looks at the energy levels in the transformed signal and uses smart thresholding technique to pick out only the parts with high energy, to extract

sahu, Akash, c, Poornima, C, Aravindh, Kaliyari, Dushyant, Tk, Khadeeja Nusrath

Enabling Circularity in the Aerospace Maintenance Ecosystem through Generative-AI Driven Back-to-Birth Traceability of Life-Limited Engine Parts

2026-26-0719To be published on 06/01/2026

Circular economy principles are increasingly central to aerospace sustainability strategies, aiming to extend asset life, improve asset valuations, and enhance benefits to stakeholders in the part ownership and maintenance lifecycle. In aircraft engines, achieving circularity hinges on the safe reuse, repair, and recirculation of high-value components. Life-Limited Parts (LLPs) are among the most critical in this context, but their reuse is strictly contingent on complete Back-to-Birth (BtB) traceability. Any gap in BtB records—often due to fragmented data across multiple airline operators, shop visits, document formats, and time expanse—renders otherwise serviceable LLPs unusable, leading to premature scrappage and lost circular value. This paper presents a Generative AI (GenAI)-driven methodology to reconstruct and validate complete LLP BtB histories from heterogeneous, unstructured, and legacy maintenance datasets. By combining aerospace domain-trained language models with embedded

Bhate, Ujwal, Jain, Dilip Kumar, Kulkarni, Ninad, Kalaiyarasan, Aravindh, Jha, Ashish, Shenoy, Karthik

Aircraft cabin Diagnosis - Inspection of the Aircraft cabins and seats using AI

2026-26-0789To be published on 06/01/2026

Operational challenges related to aircraft interiors particularly damaged seats, compromised seat cushions, and liquid or food spillages have a direct impact on airline operations and passenger satisfaction. Allowing passengers to use defective seating not only degrades the travel experience but also risks damaging the airline’s reputation. Moreover, addressing these issues often results in increased aircraft ground time, contributing to significant financial implications for airline operators. To mitigate these challenges, a predictive maintenance approach leveraging artificial intelligence (AI) is proposed. This system utilizes radar scans and high-definition camera images captured when the aircraft is unoccupied to detect interior issues. The collected data is then compared against a baseline “master scan” taken under ideal, clean, and properly maintained conditions. Advanced AI algorithms analyze these comparisons to accurately identify problem areas, down to specific seat

Nagoal, Chandrasekhar Reddy, Prathipati, Krishna Chaitanya, Kandukuri, Ravindra

A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems.

2026-26-0784To be published on 06/01/2026

In today’s global aviation industry, passenger experience is strongly influenced by effective communication. In-flight announcements, often limited to English and a single local language, can create confusion and stress for international travelers who may not be fluent in either. This communication gap not only impacts passenger comfort but also poses potential risks in conveying time-sensitive or safety-critical information. Recent advances in Generative Artificial Intelligence (GenAI), particularly in speech recognition, neural machine translation, and naturalistic text-to-speech, provide a pathway to overcome these challenges. This paper explores the concept of real-time multilingual in-flight announcements, delivered in each passenger’s preferred language through connected headphones or personal devices. The proposed system architecture integrates speech-to-text conversion, language translation, and speech synthesis with aircraft infotainment platforms. Potential applications range

Mishra, Ashwini, Kature, Kartik, Patil, Ashish

Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency

2026-26-0791To be published on 06/01/2026

Passenger comfort within vehicle and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modeling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapt environmental conditions based on passenger feedback and physiological needs. Simulations and early-stage prototypes improve energy savings and improve

Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta

Data-Driven Air Traffic Management: Forecasting Demand, Modeling Delay Propagation, and Demand Capacity Balancing

2026-26-0772To be published on 06/01/2026

Air traffic management (ATM) systems must accurately estimate flight demand, analyze network delays, and optimize capacity. This research uses a global airline dataset with passenger information, airport characteristics (including geographic markers and continental classifications), flight scheduling data (timing and destination details), crew information, operational status records, and OpenSky Network real-time states data to create an intelligent ATM decision-support framework. Advanced machine learning (ML) methods like gradient boosting and sequential modeling are used to build network models of flight routes between origins and destinations, develop temporal demand patterns for individual airports, analyze delay factors, and predict congestion periods. The research uses mixed-effects statistical models using crew data to measure operational variability and demographic and nationality-based analysis to identify demand trends for improving staffing and security processing during

Senthilkumar, N., S, Gopalakrishnan, Gopinath PhD, S

Integrating Human Factors and Predictive Modeling for Enhanced Aviation Safety: A Data-Driven Approach to Risk Mitigation and System Design

2026-26-0795To be published on 06/01/2026

The evolution of the aviation industry from manual control to highly automated and autonomous systems has brought human factors to the forefront of safety, design, and operational efficiency. This paper presents an integrated analysis combining machine learning techniques with statistical modeling to assess the impact of human-system interaction on aviation incident outcomes. The first phase of this research involved a longitudinal dataset covering aircraft design evolution, training programs, and incident records. Using classification algorithms, including XGBoost, the study achieved an accuracy of 81.82% in predicting incident likelihood. Performance metrics such as F1-scores (0.85 for “Incident” and 0.78 for “No Incident”) highlighted the model’s robustness in identifying risk patterns associated with human factors and system complexity. In the second phase, 50 plus detailed aviation incident reports were analyzed using logistic regression, t-tests, and Cook’s Distance to identify

Valiyaparambil, Praveen

Agentic Digital thread for self-healing non-conformities in aerospace product manufacturing lifecycle

2026-26-0763To be published on 06/01/2026

Aircraft manufacturing involves a complex ecosystem where quality, traceability and compliance are paramount. Managing non-conformities both before and after manufacturing requires a digitally connected data framework that ensures seamless data flow, decision-making and corrective actions across different functions like sourcing, materials, engineering and production. The advent of agentic AI brings more autonomous solutions through drastically reduced human prompts. This paper presents a comprehensive Agentic Digital thread framework for managing pre and post manufacturing non-conformities, enabling real-time visibility and control from design to delivery. The Agentic digital thread transforms from being passive connectivity layer into living intelligence ecosystem that continuously learns, adapts, and improves in real time Pre-manufacturing nonconformity management focuses on early detection of issues through Agentic AI in sourcing, materials and design through integration of PLM

Veluri, Sastry, Gopala Krishnan, Kannan

Electric Health-Monitoring Wiring (E-Wiring) for Aircraft

2026-26-0785To be published on 06/01/2026

Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. The proposed Electric Health-Monitoring Wiring (E-Wiring) system integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet

Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha

Drilling Studies on Sustainable Lightweight Hybrid Polymer Matrix High Performance Nanocomposites

2026-26-0732To be published on 06/01/2026

Worldwide, engineers are exploring the possibility of using polymer composites in their quest for lightweight materials. In an effort to create polymer composites that are less harmful to the environment, the use of biodegradable polymers has been investigated in recent years. Injection molding was used to create a biodegradable polymer composite containing 20 wt.% vetiver fibers (VFs) and 2 wt.% nano-silica (nSiO2) obtained from pearl millet. Parts for unmanned aerial vehicles, interior cabin panels, seating structures, and secondary structural components were made of this composite. Desirability analysis was used to examine and optimize machining (drilling) studies that were designed according to Taguchi's design (L9 orthogonal array). Surface roughness (Ra) and delamination factor (DF) were taken as outputs, while spindle speed (SS) ranging from 1000 to 3000 rpm, feed rate (FR) from 15 to 45 mm/min, and drill diameter (DD) from 6 to 10 mm were used as inputs. The developed

Senthilkumar, N.

AI Based Energy Sustained Series Hybrid Aircraft

2026-26-0776To be published on 06/01/2026

This paper investigates the energy consumption characteristics of series hybrid aircraft with a focus on comparing conventional energy management approaches against an AI-powered optimization framework. The study comprehensively models the energy demands of a series hybrid aircraft across all major flight phases, including Idle & Ground Operations, Taxi, Takeoff, Climb, Cruise, Descent, Approach, Landing, and Rollout & Taxi. For each phase, detailed mathematical formulations are developed to capture power requirements and energy flow, incorporating real-time operational parameters to enhance the accuracy of the energy consumption estimations measured in kilowatt-hours (kWh). The AI-based optimization leverages advanced control strategies, specifically Model Predictive Control (MPC) and Reinforcement Learning (RL) algorithms, to dynamically manage the aircraft's energy systems. MPC is employed to predict and optimize future energy usage by solving constrained optimization problems over

Kanchagar, Amogha

Enabling Digital Transformation in Aerospace: Automated Model Based Engineering

2026-26-0722To be published on 06/01/2026

Model-based development (MBD) and testing are critical for airborne software compliance with DO-178C and its supplement DO-331, which specifically addresses model-based approaches for software levels A through D. Traditional manual methods for MBD and testing are time-consuming and prone to variability, impacting consistency, accuracy, and certification timelines as avionics systems grow in complexity. This paper presents an automation framework designed to align with DO-331 objectives by leveraging fine-tuned large language models (LLMs) to automate the generation of high-level textual requirements and low-level model-based requirements. From these, comprehensive test cases are automatically derived, covering normal, edge, mutant, and dynamic scenarios to ensure thorough validation of model behavior. Utilizing Boeing AI, the framework extracts requirements and key parameters from documentation, enabling automated specification analysis and test script generation that simulate real

Lalchandani, Tushar, Purushothaman, Kalaivani, Jeppu, Yogananda, Vijaya Kumar, Shree Harsha, Natarajan, Akilandeswari

Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction

2026-26-0718To be published on 06/01/2026

Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds and constrain the failure envelope, and (2) a data-driven Recurrent Neural Network (RNN) (specifically, an LSTM) for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, synthetic run-to-failure dataset under varying operational loads

Mohamed, Abbas

From LLM to Deep Learning: Efficient Simulation of Last-Mile Energy Behavior in Campus Communities

2026-01-0461To be published on 04/07/2026

Communities are critical nodes in the urban energy network, integrating energy, transportation, and building systems to enhance overall energy efficiency. Accurate management of these systems depends on characterizing individual energy-demand behaviors. However, traditional last-mile behavioral models often fail to capture the complex and non-linear nature of human decision-making, creating a need for advanced simulation techniques. Although agent-based simulations powered by Large Language Models (LLMs) have demonstrated considerable efficacy across diverse domains, their substantial computational demands—specifically in terms of token consumption—pose pronounced constraints in large-scale simulations involving tens of thousands of agents, considerably limiting their practical applicability. To address this challenge, a deep learning-based approach is proposed for single-step prediction of last-mile behavior. A hybrid architecture, consisting of an MLP encoder and a Transformer

Yang, Zhifeng, Chen, Yongjian, Ou, Shiqi(Shawn)

Evaluating the Impact of Drag Coefficient Changes on Driving Energy Using a Probability Distribution Model of Yaw Angle Based on Empirical Data from North American Highways

2026-01-0615To be published on 04/07/2026

This study estimates the impact on driving energy of differences in aerodynamic characteristics for yaw angle from natural wind during North American Highway mode driving. A previous study[1] clarified the potential to estimate the fuel consumption impact of natural wind by integrating the CD yaw characteristics and yaw angle occurrence frequency. The natural wind was measured on a vehicle while driving a representative North American Highway test course[2]. The driving energy is predicted from the obtained yaw frequency and CD yaw sweep data in a wind tunnel. Measurements were conducted every weekday for 8 hours in 2023, covering 70% of the traffic volume. The validity of the measurement period was evaluated by the deviation from the annual average of wind direction and speed. Since yaw frequency varies depending on the road environment, it is necessary to weight the road environment type frequency when calculating the driving energy. The frequency was calculated using machine

Onishi, Yasuyuki, Nucera, Fortunato, Nichols, Larry, Metka, Matt

Predictive Maintenance of EV Drive Unit Oil Pumps via Machine Learning on Fleet Telemetry

2026-01-0161To be published on 04/07/2026

The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. However, direct physical sensors for real-time pump health monitoring are absent in current architectures, making early fault detection particularly challenging. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. The core of the approach is an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage Error (MAPE) between predicted and actual currents, providing a continuous and interpretable measure of

Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne

Improving EV Energy Forecast Accuracy via ML Residual Correction: The Role of Wind Speed in Data-Driven Predictions

2026-01-0150To be published on 04/07/2026

The increasing demand for electrified transportation is leading to accelerated development of highly efficient hybrid and battery electric vehicles. A major concern for customers adapting to battery electric vehicles (BEV) is range anxiety due to low charging speeds, charging infrastructure not matching expectations and unreliable range estimations shown to the customers by their vehicles. Estimating the range more accurately has been difficult due to the sensitivity of vehicle’s energy consumption to real-world environmental and driving conditions. This paper aims to find out the effect of true wind in the road load experienced by BEVs in the real-world driving scenarios and how using a highly accurate wind speed measurement improves the energy consumption estimation better. On-road tests were conducted on public roads and in controlled test-track environments to collect reliable wind speed measurements using a wind probe. Additional coastdown tests were also conducted to find more

RAGHUPATHY, Vishnu Prasaad, Kim, Shinhoon, Evans, Nic, Niimi, Keisuke, Mochihara, Takahiro

Assessing Plug-in Electric Vehicle Adoption: Methodologies, Policy Effects, and Diverging Market Pathways in China, the U.S., and Europe

2026-01-0455To be published on 04/07/2026

Accurate projection of Plug-in Electric Vehicle (PEV) market sales share is vital for evidence-based policymaking, yet existing studies employ diverse and often fragmented methodologies, creating a need for a systematic review to clarify their analytical foundations and comparative strengths. This study classifies mainstream approaches to market projections into theory-driven and data-driven categories and reviews the merits, limitations, and future directions of five representative models. Analysis reveals that leading approaches increasingly employ cross-scale model coupling, theory-data fusion, and modular design to harness complementary strengths, improving model robustness and predictive accuracy. Furthermore, the study compares PEV policies and market outlooks in China, the United States, and Europe—the world's three largest automotive markets. The findings indicate a strong linkage between projection convergence and policy stability. China demonstrates the highest policy

Luo, Wei, Ou, Shiqi(Shawn), Zhou, Pan, Wang, Tianpeng, Qian, Xiaodong

Generative Agents for High-Fidelity Simulation of Community-Scale Mobility and Energy Behavior

2026-01-0465To be published on 04/07/2026

The transition to sustainable mobility and energy systems is a complex socio-technical challenge, where the success of new technologies and policies hinges on their interaction with human behavior. Traditional models often fail to capture the nuanced, heterogeneous, and adaptive nature of occupant decision-making, particularly in terms of mobility choices and energy use, creating a critical uncertainty gap for system design and policy evaluation. This paper presents a new paradigm to address this gap: a scalable, high-fidelity simulation framework based on Generative Agents. The framework's core innovations are twofold: 1) It leverages Large Language Models (LLMs) to autonomously generate agents with intrinsic personality traits autonomously, enabling a realistic simulation of diverse, human-like responses to environmental, social, and transportation-related cues. 2) It introduces a hierarchical "Prototype-Individual" architecture that makes large-scale simulation of entire communities

Chen, Yongjian, Yang, Zhifeng, Ou, Shiqi(Shawn)

Machine Learning based Meta-Analysis of IIHS Moderate Overlap Frontal Crash Test for Rear Seat Occupant Safety Performance

2026-01-0579To be published on 04/07/2026

A machine learning (ML)-based meta-analysis was conducted to evaluate rear seat occupant safety performance in the Insurance Institute for Highway Safety (IIHS) Moderate Overlap Frontal (MOF) 2.0 crash test. ML models were trained on historical IIHS crash test data to predict rear passenger injury metrics using vehicle architecture, restraint system characteristics, crash pulse parameters, and vehicle kinematics as input features. The models demonstrated high predictive accuracy and were subsequently used in a Sobol sensitivity analysis to identify critical design parameters influencing injury outcomes. The analysis revealed that rear passenger injury metrics were most sensitive to restraint system parameters. Specifically, crash pulse magnitude was the dominant factor for head injury metrics, pretensioner activation time for neck tension force, and lap belt force for the Neck Injury Criterion (Nij). For chest-related metrics—sternum deflection, dynamic belt position, and maximum belt

Lalwala, Mitesh, Kim, Wonhee, Furton, Lisa, Song, Jay

A Large Language Model-Based Database for Analyzing the Electric Vehicle Battery Supply Chain

2026-01-0471To be published on 04/07/2026

Global geopolitical volatility poses a critical threat to the resilience of the electric vehicle battery supply chain. Static, manually updated databases fail to capture the sector's rapid dynamics, creating significant information gaps for strategic planning. This study proposes an Artificial Intelligence-driven methodology to construct a comprehensive and dynamic knowledge base. An automated pipeline is implemented, beginning with specialized web crawlers that harvest real-time textual data from curated news and industry sources. This unstructured data is preprocessed through deduplication and cleansing to ensure quality. The core innovation involves applying fine-tuned Large Language Models (LLMs) to perform deep semantic parsing and extract structured information. These models precisely identify key entities—corporations, facilities, production capacities—and delineate the complex multi-tier relationships from raw material extraction to final distribution. The output is an

Zhu, Juntong, Luo, Wei, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi(Shawn), He, Xin

CrashML Analyzer: A Data-Driven Approach for Investigation and Fault Assignment in Autonomous Driving Collisions

2026-01-0160To be published on 04/07/2026

This study investigates factors contributing to autonomous vehicle (AV) accidents and proposes an automated fault determination framework. A total of 563 accident reports from the State of California Department of Motor Vehicles spanning from 2019 to 2024 were analyzed by converting unstructured standardized reports into structured data using custom extraction tools. Analysis of these reports reveal that AVs were not at fault in 69.4% of cases, and were fully at fault for 22.6% of the cases. The proposed method uses these reports to provide an early indicator of fault likelihood and potentially replaces tedious manual review. Machine Learning (ML) and Natural Language Processing techniques were used to replicate the reported faults, achieving 96% average accuracy across three models: Gradient Boosting, Linear Regression, and Random Forest. Through feature engineering techniques in semantic feature extraction from narrative accident descriptions, quantifiable variables were obtained and

Rwejuna, Florida Perfect, Majid, Nishatul, Goutham, Mithun, Loukili, Alae

Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images

2026-01-0107To be published on 04/07/2026

Being a vital component of transportation, parking and parking-related research are gaining increasing attention from the intelligent transportation and automotive industry research communities. A digital parking map with precise parking spot geospatial information is crucial for research such as automatic valet parking, parking spot recommendations, and parking route optimization. Currently, these maps are generated through costly and resource-intensive human annotation. To address the challenges of insufficient parking maps and the high costs of manual map creation, researchers have proposed the following two strategies for generating parking maps. The SLAM-based map generation for indoor parking lots and the remote sensing image-based map generation for outdoor parking lot. Our work mainly focuses on the outdoor high-definition parking map generation from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. To

Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin

Transformer-Based Deep Learning Framework for Efficient Prediction of Automotive Wind Noise

2026-01-0222To be published on 04/07/2026

Aerodynamic wind noise is a critical challenge in modern automotive development, particularly with the rise of vehicle electrification and intelligent mobility, where cabin acoustic comfort is a key quality metric. While reliable, traditional methods like wind tunnel experiment and CFD simulations are both costly and time-consuming. To address this, we propose a novel transformer-based framework for rapid and accurate wind noise prediction. Several model improvements, including the physical attention, geometry wave number embedding, hybrid FPS-random down sampling method and frequency separation output heads are properly employed to reduce the GPU mermory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale transient flow field dataset of 1,000 diverse vehicles generated using Delayed Detached Eddy Simulation (DES). From a vehicle's point-cloud coordinates, the model directly predicts the surface pressure spectrum on the driver-side window and the

TANG, Weishao, Liu, Mengxin, QIN, Ling, Duan, Menghua, wang, chengjun

Computational Modeling and Optimization of Shape Memory Polymer-Based Energy Absorbers

2026-01-0573To be published on 04/07/2026

Shape memory polymers (SMPs) provide tunable thermomechanical properties and enable the design of recoverable crash structures for automotive applications. This paper introduces a computational framework for the design and optimization of SMP-based crash absorbers with periodic auxetic microstructures. A finite element (FE) model is developed and validated against experimental data reported in the literature. The model is used to simulate crushing and recovery behavior under different temperature conditions. A parametric study is performed by varying key microstructural features, including wall thickness, cell size, and cell shape. Structural performance is evaluated in terms of specific energy absorption (SEA), peak force, and recoverability under axial crush loading. To efficiently explore the high-dimensional design space, surrogate models based on machine learning are constructed, and multi-objective optimization is carried out to identify Pareto-optimal designs that balance

Zhu, Yingbo, Zhu, Feng, Deb, Anindya

Machine Learning-Based Gear Defect Detection in Electric Vehicle Drive Units Using End-of-Line Vibration Analysis

2026-01-0637To be published on 04/07/2026

The final assembly of electric vehicle (EV) drive units includes an essential End-of-Line (EOL) test to ensure both component integrity and Noise, Vibration, and Harshness (NVH) quality. This screening process, which uses dynamometers to measure vibration signals, is critical for identifying defects before a drive unit is installed in a vehicle. A significant source of failure during this test is gear defects, which can arise from manufacturing or handling issues. Traditional EOL testing methods rely on time-domain analysis and the impulsiveness of vibration signatures to detect these defects, a technique with inherent limitations in accuracy. This paper introduces and evaluates a novel approach using Machine Learning (ML) to analyze vibration signals for improved gear defect detection. We discuss the methodologies of both the traditional time-domain and the proposed ML-based techniques. Finally, we provide a comprehensive comparison of their respective efficiency and accuracy

Arvanitis, Anastasios, Michaloliakos, Anargyros

ProGuard: A Real-time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices

2026-01-0100To be published on 04/07/2026

This paper proposes ProGuard, a novel approach to preemptive pinch detection systems for buses. ProGuard utilizes state-of-the-art AI object detection algorithms to identify potential pinching events in bus entryways before pinching occurs. Modern conventional anti-pinch systems, such as pressure sensors or hall effect sensors, often rely on mechanical contact before triggering. While these systems are established safety mechanisms, they are reactive and therefore require some level of pinching before triggering. This reactive approach presents numerous safety concerns for passengers, especially when considering children on school buses. Existing preemptive detection methods, such as infrared or ultrasonic sensors, solve the problems presented by these reactive detection systems. However, these systems either lack the range or environmental resilience needed for reliable operation in buses. The critical nature of anti-pinch systems requires a robust and reliable solution that can adapt

Bradley, Hudson, Zadeh, Mehrdad, TAN, Teik-Khoon

Open Source LLM Performance in Automating Embedded C Software Quality Improvements for Automotive

2026-01-0103To be published on 04/07/2026

The study presented in this paper explored the potential of five open-source Large Language Models (LLMs) with parameter counts between 30 billion and 50 billion to automate enhancements in code quality and developer productivity. The evaluated models - CodeLlama, Command-R, Deepseek R1, Nemotron, and QwQ - were assessed on their ability to refactor a large and complex automotive mechatronic C language function. This assessment focused on adherence to provided code quality standards and successful compilation of the refactored function within its original code module. The evaluation also compared the impact of parameter count, hyperparameter tuning, model architecture, and fine-tuning. This comparison revealed that larger models generally outperformed smaller models against the provided quality standards. Additionally, hyperparameter tuning yielded a moderate improvement in performance. The study further highlighted that model architecture and fine-tuning had less predictable effects

Struck, Daniel, Kumaraswamy, Samanth

Extreme Scale Automotive Aerodynamic Simulations

2026-01-0597To be published on 04/07/2026

Achieving an optimal balance between simulation accuracy and computational efficiency remains a central challenge in automotive aerodynamics. While the adoption of AI and machine learning (ML) methods in vehicle development is expected to grow significantly, the demand for highly scalable, computationally efficient, and accurate computational fluid dynamics (CFD) methods persists. The emergence of GPU technology presents new opportunities to deliver cost-effective, high-fidelity, scale-resolving simulations to industrial users. A comprehensive evaluation of Simcenter STAR-CCM+’s parallel scalability and accuracy across extensive CPU and GPU resources was executed on the Frontier supercomputer at Oak Ridge National Laboratory. Steady-state and transient aerodynamic scalability simulations were executed using the DrivAer notchback vehicle configuration. Simulation accuracy was evaluated through transient simulations employing the SST-DDES turbulence model on an initial mesh of 200

Larsson, Torbjörn, Grover, Ronald O., Landi, Simone, Altmann, Peter, McManus, Liam, Dowding, Steven

HaloBus: Edge Computing‑Enabled Real‑Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI

2026-01-0555To be published on 04/07/2026

This paper proposes HaloBus, an innovative, edge‑computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems are forced upon the individual households with their own methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and every parent deserves to feel like their child is safe. That is why HaloBus was invented. The system employs YOLOv5u—an Ultralytics‑enhanced, anchor‑free, split‑head architecture that offers a superior accuracy–speed tradeoff. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident

Getz, Grayson, Zadeh, Mehrdad, TAN, Teik-Khoon

Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture

2026-01-0639To be published on 04/07/2026

The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded SmartCorners project addresses this challenge by developing integrated smart corner systems (SCS) that combine in-wheel motor propulsion, brake-by-wire, active suspension, and steer-by-wire functionality in a single module. These SCS are embedded within a highly adaptable skateboard chassis architecture, enabling scalable deployment across diverse vehicle platforms. The central approach of this paper is the utilization of artificial intelligence and machine learning to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and a user-centric vehicle architecture. The SmartCorners project targets to demonstrate significant advancements, including improvement in real-world driving range, reduction of component and system costs, and development time drop of the EV systems enabled by digital

Ratz, Florian, Armengaud, Eric, Formento, Cecilia, Moscone, Giulia, Sorrentino, Gennaro, Bisciaio, Giorgio, Sorniotti, Aldo, Amati, Nicola, Braun, Daniel, Deibler, Bernd, Boxberger, Valerius, Sottile, Salvatore, Ivanov, Valentin, Fuse, Hiroyuki, Kompara, Tomaž

Virtual Sensor Development for Real-Time Estimation of Transient NOx Emissions for Internal Combustion Engine

2026-01-0370To be published on 04/07/2026

Accurately measuring NOx emissions under transient engine conditions is becoming increasingly important with upcoming Euro 7 and EPA 2027 regulations. Traditional physical sensors often struggle with cost and response time, especially with aging of sensors in dynamic operation. This paper introduces a machine-learning–based virtual NOx sensor that can provide real-time emission estimates while reducing reliance on hardware sensors. The approach uses multiple machine-learning methods (Random Forest, Bootstrap Aggregating, Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting) and selected best one to establish correlations between engine operating parameters, measured steady-state data, and transient duty cycle NOx emissions. Validation across different duty cycles has shown strong alignment with physical sensor readings, with R² values above 0.9995 for known data sets and above 0.9534 for unseen conditions. The model needs to be trained with larger training samples to further

Kumar, Chandan, Dahodwala, Mufaddel, Thawrani, Kiran

Peak SCR Efficiency for Ultra-Low NOx Using Electrically Heated Mixer for EU-VII and China-VII

2026-01-0363To be published on 04/07/2026

Achieving ultra-low NOx emissions remains a major challenge in diesel emission control worldwide, especially as increasingly stringent regulations are introduced globally. Selective Catalytic Reduction (SCR), the leading NOx reduction technology in diesel systems, performs best when both heat and ammonia are available. At the same time, any proposed solution must also be cost-effective and economically viable. In this work, we present a low-cost Electrically Heated Mixer (EHM). EHM not only significantly reduces urea deposit formation, thereby lowering warranty costs and mitigating failure modes, but also enhances SCR efficiency, especially in challenging low-load conditions having low exhaust temperature. EHM is also ideal for rapid heat-up and urea injection during engine cold-start conditions ( <100 °C) enabling swift NH₃ formation and storage in the SCR catalyst. Additionally, it enables early urea injection (~ 130 - 150 °C), improving SCR efficiency in low temperatures below 200

Masoudi, Mansour, Poliakov, Nick

Multi-Physics Topology Optimization and Machine Learning Surrogates for Lightweight Cast Automotive Parts

2026-01-0510To be published on 04/07/2026

This paper proposes an integrated optimization framework for lightweight cast vehicle structures by combining Multi-Physics Topology Optimization (MPTO) with machine learning (ML)-driven surrogate modeling. Conventional structural optimization approaches often rely on simplified load cases—such as 3 g inertial loading, frame torsion, or bending—rather than capturing the true complexity of performance-critical conditions like crashworthiness and fatigue durability. As a result, designs optimized for a single criterion frequently degrade in other performance areas, leading to costly redesigns or over-engineered solutions. The proposed methodology introduces MPTO through four structured steps that progressively build design fidelity. Step 1 establishes a baseline primitive topology optimization workflow using a standardized package checklist, ensuring that the design domain, boundary conditions, constraints, and key manufacturing rules are consistently defined as the foundation for

Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh

Leveraging Large Language Models for CAD Automation and Multi-Objective Optimization of Automotive Heat Exchangers

2026-01-0506To be published on 04/07/2026

The design of automotive heat exchangers is a critical challenge, requiring the careful balance of thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design processes rely heavily on manual CAD modeling and iterative simulations, which are both time-intensive and computationally demanding. Recent advances in artificial intelligence, particularly in Large Language Models (LLMs) and Transformer-based architectures, open new possibilities for automating these tasks. This work introduces a novel framework that integrates LLMs with CAD kernels to generate parametric heat exchanger geometries directly from natural language descriptions. The models are automatically exported into standard formats for computational fluid dynamics simulations and seamlessly incorporated within a multi-objective optimization pipeline. By coupling natural language-driven CAD automation with advanced optimization techniques, the framework enables rapid exploration

Chaudhari, Prathamesh, Tovar, Andres

Artificial Intelligence in Automotive Stamping –the latest industrial applications and future directions

2026-01-0179To be published on 04/07/2026

A review of the applications of Artificial Intelligence (AI) in automotive stamping is presented. The focus is on recent AI implementations within the automotive industry. Through this review, the authors aim to capture the current momentum of AI in automotive stamping. The article begins with an overview of the importance and challenges of stamping in the automotive sector, followed by a discussion of key AI technologies applied in this domain. Several industrial applications are then introduced, categorized by their specific use cases. Finally, strategic challenges and future directions are discussed.

Sheng, ZiQiang, Huang, Lu, Asimba, Brian, McCarty, Eric, whaley, Jason, cabral, Kleber, Osegueda, Mario, Huang, Xiaosong, Erol, Baris

A Frequency-Aware Reinforcement Learning Framework for Suspension Control with Integrated Temporal Features and Expert Guidance

2026-01-0209To be published on 04/07/2026

Active suspension systems play a crucial role in improving vehicle ride comfort and handling stability. However, most existing studies focus on the low-frequency range below 20 Hz, leaving the suppression of high-frequency vibrations within 50–500 Hz largely unexplored, even though these vibrations strongly affect in-cabin noise and ride quality. To address this gap, this study introduces a quarter-car suspension model incorporating both bushing dynamics and a rigid-ring tire within a reinforcement learning (RL) framework. A major challenge for RL-based suspension control is its degradation in high-frequency performance. To overcome this issue, we design an innovative training framework that integrates multiple synergistic strategies. First, frequency-domain rewards are incorporated as auxiliary signals to explicitly guide policy optimization in the high-frequency band. Second, long short-term memory (LSTM) networks are embedded in both the Actor and Critic to capture the sequential

zhu, Zhehui, Zhang, Lijun, Meng, Dejian, Hu, Xingyu

In-situ Decoupling and Stiffness Optimization of Resiliently Coupled Assemblies Using FRF-Based Substructuring

2026-01-0227To be published on 04/07/2026

Road shake remains one of the most critical NVH challenges in light- and heavy-duty body-on-frame trucks, directly shaping ride comfort and perceived vehicle quality. At the heart of this issue are the body mounts, which govern the transfer of vibration between frame and body. Optimizing these mounts for improved road shake performance is therefore a key objective in NVH engineering. Traditionally, such optimization has relied on finite element analysis (FEA). While highly accurate, FEA—especially when performed with commercial solvers such as NASTRAN—demands enormous computational resources, with complete simulations often requiring hundreds of hours. Each design iteration multiplies this cost, restricting the pace and efficiency of NVH development. Recent methods such as response surface modeling (RSM) and machine learning (ML) have aimed to reduce the burden, yet they remain dependent on large libraries of pre-computed FEA data, inheriting much of the same expense and inflexibility

Haider, Syed, Abbas, Ahmad, Jahangir, Yawar, Maddali, Ramakanth

Study on the Regulation of the Three-Phase Microenvironment in Low-Platinum MEA

2026-01-0443To be published on 04/07/2026

With the growth of energy demand, fuel cells as efficient and clean energy devices, have attracted increasing attention. However, the high cost of membrane electrode assembly (MEA) restricts their large-scale application. Therefore, reducing the platinum usage and improving performance have become key research point. In this work, MEA was prepared and excellent performance of 1.52 W·cm-2 was achieved at a low platinum loading. The influence of different ionomer/carbon (I/C) ratio on the performance of fuel cells was systematically investigated. It was found that the performance of the MEA was the highest when the I/C ratio is 0.6. Quantifying hydrophilic and hydrophobic characteristics of catalyst layers with varying ionomer contents revealed that the proton conduction efficiency is optimal when the I/C ratio is 0.6. This balance established efficient proton conduction pathways, from the results of proton conduction impedance testing. SEM analysis demonstrated that pore structure

Li, Xin, Cai, Xin, Lin, Rui

CAE Acceleration With Machine Learning For Results Prediction

2026-01-0488To be published on 04/07/2026

Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AIML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AIML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in

Dangare, Anand Manohar, Kulkarni, Mandar

AI LabelMate: A Context-Aware Annotation Agent for Reducing Semantic Fragmentation

2026-01-0261To be published on 04/07/2026

Robust perception systems for autonomous vehicles depend critically on high-quality labeled data, especially for off-road and unstructured environments. However, perception model performance is often degraded by 'data chaos' from limitations in automated segmentation. Foundation models like SAM2, while powerful, usually generate masks based on low-level visual cues like color and texture gradients. In complex off-road scenes, this leads to semantic fragmentation. A single object, like a moss-covered log, can be split into not only dozens of segments for its bark and moss, but also hundreds of smaller, meaningless patches based on minor color variations. This paper introduces a context-aware annotation agent to resolve this issue. Our workflow integrates a vision-language model (Florence-2) for scene understanding with a segmentation model (SAM2) for mask generation. Instead of segmenting indiscriminately, our agent leverages Florence-2 to comprehend the image holistically, localizing

Patil, Ashish, Mikulski, Dariusz, Mwakalonge, Judith, Jia, Yunyi

Neural Surrogates and Reinforcement Learning for Heavy-Duty Diesel Torque Control: A Data-Driven Alternative to PID

2026-01-0199To be published on 04/07/2026

Accurately reproducing the torque trace of regulatory drive cycles is central to heavy-duty diesel emissions certification and repeatable development testing. Conventional controllers—PI/PID with gain scheduling and feedforward mapping—can meet requirements but demand extensive hand-tuning and struggle with strong nonlinearities and time-varying delay. Building on prior work that quantified the limits and benefits of gain-scheduled and feedforward strategies under the FTP transient cycle, this work investigates a fully data-driven control framework. First, a recurrent neural network (LSTM) torque surrogate is trained on dynamometer data to capture engine dynamics from readily available signals (e.g., engine speed, commanded pedal/torque, recent torque history) and actuator constraints. Second, this learned “world model” is used to train a reinforcement learning (RL) policy that outputs pedal commands to minimize torque-tracking error while regularizing control effort and respecting

Cook, James, Puzinauskas, Paulius, Bittle, Joshua, Hall, Spencer

Development of a System for Regression Model Enhancement Utilizing Shape Generation AI

2026-01-0499To be published on 04/07/2026

This study proposes a method to enhance regression models by shape generation AI. The approach focuses on automatically identifying regions within the design space where the model’s prediction accuracy is low. Once these regions are identified, new and diverse sample shapes are automatically generated by the shape generation AI and incorporated into the training dataset. The regression model is then retrained to improve its performance. By iteratively repeating this cycle of exploration, shape (FE mesh) generation, and model updating, the model’s reliability and accuracy across the entire design space are progressively enhanced. This method addresses data sparsity issues common in complex design tasks and enables better generalization to underrepresented regions. The effectiveness of the proposed system was demonstrated through a case study involving hood outer panels in automotive design. The results showed that adding AI-generated shapes improved prediction accuracy, particularly in

Taniguchi, Mashio

Adaptive Economic Model Predictive Control for Engine Emissions Management with Compensation for Performance Drift

2026-01-0287To be published on 04/07/2026

This paper addresses the changes in engine emissions due to in-use component changes through the synergistic application of predictive control, machine learning, and onboard adaptation. In particular, we consider an adaptive economic Model Predictive Control (eMPC) strategy to mitigate the effects of performance drift on Nitrogen Oxides (NOx) and Soot emissions from compression ignition (diesel) engines. A performance drift block, which applies a multiplier and offset to nominal emissions, is integrated with a high-fidelity Neural Network (NN) plant model to simulate these characteristic changes. To counteract variability, two online adaptation methods are integrated within the eMPC framework: One is based on Recursive Least Squares (RLS) and another on a continuously updated online NN. The proposed control architecture is validated through simulations over standard transient cycles. Results demonstrate that while the rate-based eMPC possesses inherent robustness to performance drift

ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization

2026-01-0165To be published on 04/07/2026

Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback

Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin

A Framework for Predicting 3D Printed Part Defects Using MultiModal Deep Learning on Infrared Images

2026-01-0140To be published on 04/07/2026

Fused filament fabrication (FFF) has gained popularity in recent years because it can produce prototypes and functional components with complex geometry. Because of inherent process variability, the components often exhibit defects such as warping, layer delamination, voids, and poor surface finish, as well as issues related to variable material strength and anisotropy. In-situ monitoring (ISM) of the FFF process is a promising technique to predict part performance, which in turn can support accept or reject decisions for printed parts. This paper proposes a framework for incorporating ISM-generated information, with a particular focus on infrared (IR) image analysis for this purpose. IR camera images, in conjunction with numerical features such as infill pattern and extruder nozzle temperature, serve as an input to a multimodal deep learning (MDL) model that predicts the mechanical performance of printed parts. In the framework, convolutional neural nets process image inputs, while a

Mollan, Calahan, Kulkarni, Saurabh, Malik, Ali Ahmad, Patterson, Albert E., Pandey, Vijitashwa

Machine Learning for Predicting Rotor and Brake Fluid Temperatures in Hill Descent Events

2026-01-0159To be published on 04/07/2026

High thermal loads on brake systems during extended descents followed by vehicle soak pose significant safety and durability risks. Excessive rotor or fluid temperatures can cause loss of braking efficacy, fluid degradation or evaporation, thermal fade, and accelerated component wear. This study uses time history data of brake disc and fluid temperatures collected during controlled hill descent events and subsequent soak (park) periods, and simultaneously logs environmental conditions (ambient temperature, humidity, wind speed and direction) and brake specifications (rotor, caliper geometry, pad material, fluid type, and vehicle mass). Seventy six test runs from a dedicated validation program are used, each providing time history records that form the basis of our analysis. From these records we extract phase specific samples (descent and soak phase) and engineer compact descriptors, such as start and peak temperatures, environmental factors, rolling statistics and contextual metadata

poojari, Uday Kumar, Westphalen, Jan, Venugopal, Narayana

Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections

2026-01-0045To be published on 04/07/2026

Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show

Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin

Clustering-Based Segmentation of Off-Road Vehicle Activity into Productive and Non-Productive States from GPS Data

2026-01-0169To be published on 04/07/2026

Accurate identification of Productive and Non-Productive States or Off-Road vehicle duty cycles—comprising working, idle, and transport states—is critical for performance analysis, fuel optimization, and emissions modeling in agriculture machinery and fleet operations. This study explores the application of unsupervised machine learning techniques to classify duty cycles using GPS-derived parameters such as speed, location variance, and temporal patterns. Unlike supervised approaches, the proposed method does not rely on labeled datasets, making it scalable and adaptable across diverse operational contexts. Clustering algorithms, including K-means and DBSCAN, are employed to segment GPS data into distinct behavioral states. Feature engineering focuses on extracting motion signatures and spatial-temporal features that correlate with operational modes. Validation against manually annotated datasets demonstrates high accuracy in distinguishing idle, working, and transport phases. The

Maharana, Devi prasad, Gangsar, Purushottam, Dharmadhikari, Nitin, Pandey, Anand Kumar

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