Browse Topic: Artificial intelligence (AI)

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Production-Ready Automated ECU Calibration using Residual Reinforcement Learning2026-01-0780To be published on 07/01/2026
Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack
Kampmeier, Andreas, Badalian, Kevin, Koch, Lucas, Lee, Sung-Yong, Andert, Jakob
Multi-Agent LLM Architecture for Systems Engineering Automation2026-01-0777To be published on 07/01/2026
The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles (SDVs), where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making. This paper presents a modular multi-agent LLM pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and
Padubrin, Marcel, Kulzer, André Casal, Guerocak, Erol
Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations2026-01-0744To be published on 07/01/2026
The aim of this work is to develop a modular digital twin of an electric powertrain based on novel machine learning (ML)-based model structures and a systematic, component-oriented architecture. This results in a modular, real-time-capable digital twin that precisely maps the efficiency characteristics of the entire drivetrain and can be used in development and testing processes. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measurement data from a test bench, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates physically consistent predictions of key variables such as torque, speed, power factors, and
Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus, Carsten, Kley, Markus
Multi-Agent LLM workflow for Regulatory-Driven Requirement Generation in Automotive Software Development2026-01-0787To be published on 07/01/2026
The increasing regulatory complexity in automotive development places significant pressure on engineering teams to derive complete and correct requirements. This paper presents a multi-agent-based large language model (LLM) workflow designed to support requirement extraction from technical specifications and regulatory documents in compliance with automotive requirement guidelines. The approach structures the requirement derivation process across collaborating agents that interpret specification and regulatory text, generate candidate requirements for the early engineering activities, and cross-validate their outputs to improve consistency and traceability. To evaluate the applicability of the workflow in an industrial context, we applied it to the draft Euro 7 emissions regulation. The agents produced requirements for relevant functional domains, which were subsequently reviewed by domain experts at FEV. The evaluation focused on correctness, completeness, and coverage. Results
Abdalla, Abdelrahman, Schäfers, Lukas, Schmidt, Fabian, Schaub, Joschka, Lee, Sung-Yong, Andert, Jakob
Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts2026-01-0770To be published on 07/01/2026
Recent advancements in Vision-Language Models (VLMs) have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models (LLMs) are primarily trained on textual data and generic code repositories, which limits their ability to interpret graphical engineering artifacts such as Simulink block diagrams or system architecture models. In safety-critical domains like the automotive industry, these graphical models are central to development workflows and must remain closely aligned with textual requirements and implementation code to ensure traceability, compliance, and functional correctness. This paper proposes a VLM-centered multimodal training framework for code generation that integrates textual requirements, graphical model-based artifacts, and annotated source code into a unified learning process. By leveraging models which combine vision encoders with language backbones, the approach enables the
Padubrin, Marcel, Kulzer, Andre Casal, Guerocak, Erol
Harmonizing Generative AI and ISO 26262 for Safe and Intelligent ADAS Systems2026-01-0785To be published on 07/01/2026
The convergence of Generative AI (GenAI) and Functional Safety (FuSa) under ISO 26262 is reshaping Advanced Driver Assistance Systems (ADAS) by merging adaptive intelligence with stringent safety assurance. GenAI significantly improves perception, object classification, pedestrian intent prediction, and risk assessment, empowering vehicles to navigate highly dynamic, real-world environments. Furthermore, GenAI-driven synthetic data generation accelerates ADAS validation by enabling exhaustive testing of rare and safety-critical scenarios not covered by conventional datasets. However, the integration of GenAI into safety-critical automotive systems introduces major challenges. Its probabilistic nature, limited transparency, and vulnerability to adversarial inputs directly conflict with ISO 26262 principles that demand determinism, traceability, and fault isolation. This paper proposes a co-engineering framework that combines GenAI’s learning-based adaptability with deterministic safety
Jain, Yesha
An Application-Oriented Process Model for Selecting Uncertainty Quantification Methods in Machine Learning2026-01-0773To be published on 07/01/2026
This paper presents an application-oriented process model for selecting uncertainty quantification methods in machine learning. The work is motivated by the increasing relevance of uncertainty quantification in product engineering applications, including the automotive domain, where reliable decision making under uncertainty is essential. In this context, machine learning is widely used in data mining to derive knowledge about systems and user behavior from data and to make this knowledge accessible to product developers. As these applications increasingly influence engineering decisions, the assessment and communication of uncertainty become critical requirements. Each data mining task is unique and no single algorithm is optimal for all tasks. This observation motivates the need for a structured process model to support the selection of appropriate uncertainty quantification methods in machine learning. The proposed process model builds on established problem-solving methods and
Holderied, Niklas, Hörtling, Stefan, Bause, Katharina, Düser, Tobias
Kolmogorov-Arnold Networks in Industrial Application of Truck Development2026-01-0760To be published on 07/01/2026
Kolmogorov-Arnold Networks (KANs) are a novel mathematical method to generate data-driven AI surrogate models. Compared to neural networks based on the MLP standard (Multi-Layer Perceptron), these offer further mathematical interpretability and thus allow improved validation of AI for industrial applications. In this paper, we use KANs to generate an AI vehicle model of a truck as a mathematically precise AI surrogate model. To do this, we combine the KAN approach with the approach of Neural Ordinary Differential Equations (Neural ODEs) to generate predictions for the time-series of the truck’s velocity. Furthermore, we compare the results of the AI based on KANs with the traditional approach using MLP in terms of model size, accuracy, and computational time in order to evaluate advantages and disadvantages of the KAN approach. The best AI-KAN vehicle model identified in this way is then embedded in a co-simulation via the Functional Mockup Interface standard, thus opening up a wide
Vaudrevange, Patrick K.S., Halverson, James, Ruehle, Fabian, Fabcic, Tomaz, Dingler, Christian, Piskala Dilipkumar, Santhoshkumar, Ibrahim, Muhammed, Herrnberger, Michael, Kasper, Johanna, Türk, Lars, Keckeisen, Michael
Application of Deterministic and AI-Based Methods to Accelerate the Component-Level Development Process in Electric Vehicles2026-01-0766To be published on 07/01/2026
The global automotive landscape is undergoing a significant paradigm shift driven by the rapid development cycles of emerging competitors, leaving traditional European OEMs with a critical time-to-market gap. To bridge this gap, automotive engineering must pivot from traditional hardware-based processes toward agile, digital data-driven methodologies. This paper presents a comprehensive feasibility study on the implementation of data-centric approaches, using both Artificial Intelligence and deterministic methodologies, with focus on accelerating component development. It provides a detailed assessment of various AI-based and deterministic methodologies at specific stages of the product development process, targeting both: 1. Product design 2. Product development process The aim is to enhance component-level performance, quality, and functionality, while simultaneously optimizing workflows, increasing process and development efficiency, and facilitating knowledge reuse throughout the
Bode, Jana Pascal, Kröll, Sarah, Vohwinkel, Nikolaus, Paetzold-Byhain, Kristin
Generating Machine-Processable Specifications from Natural Language Using Large Language Models in Automotive Commissioning and Testing2026-01-0768To be published on 07/01/2026
The commissioning of electric and electronic components contributes significantly to adding value in vehicle production. Moreover, testing these components and digital vehicle functions secures this value and ensures high-quality products. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of commissioning and testing processes as software functions depend on electric and electronic components that take on perception, execution, and processing tasks. Nevertheless, assembly planning must still meet the requirement of providing thorough, error-free, yet cost-effective processes. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target end results in unstructured natural language. Therefore, extensive manual translation into executable code is needed, being susceptible
Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian
Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data2026-01-0778To be published on 07/01/2026
This study investigates the feasibility of identifying individual e-bike riders based on Controller Area Network (CAN) data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise k-Nearest Neighbors (KNN), Random Forest and Naive Bayes. The findings demonstrate the efficacy of machine
Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner
Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications2026-01-0762To be published on 07/01/2026
Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, provide high accuracy but rely on predefined functional structures and a large number of parameters, which limits interpretability and adaptability to new tire compounds. This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify models that optimally balance prediction accuracy and structural simplicity. In contrast to black-box machine learning approaches, the resulting models consist of explicit mathematical expressions that enable physical
Anselment, Marcel, Borowski, Julian, Rudolph, Stephan
Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches2026-01-0689To be published on 06/20/2026
Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a holistic, front-loaded approach to NVH optimisation, leveraging simulation models, machine learning, and target cascading to deliver superior results. By attacking NVH at the source, and integrating data-driven techniques early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning algorithms are utilised to accelerate root cause analysis and refine simulation accuracy, while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, setting the benchmark for next-generation electric powertrain development. several application and real life exmaple are shown.
Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef
AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan2026-01-0716To be published on 06/20/2026
• In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design phase, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which
Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha
Active Learning Supported Metadata Extraction from NVH Simulations2026-01-0720To be published on 06/20/2026
Simulations can only be searched, reused and leveraged as trainings data for machine learning methods if they possess suitable metadata. Manually obtaining this metadata is time-consuming and requires expert knowledge. Consequently, there is often a lack of metadata, and this prohibits the reutilisation of simulation data. Therefore, automated frame works for metadata extraction are essential to quickly, effortlessly and cost-efficiently obtain this information. At present, there are no existing toolboxes for Finite-Element-Simulation data and current Large Language Models are not usable. Nevertheless, machine learning methods are a promising solution for this task. However, classical supervised machine learning methods cannot be used due to the high effort for labelling. Therefore, classical rule-based extraction algorithms are an alternative for fundamental metadata extraction. For more enhanced tasks, where more context is necessary, they are not usable. Active Learning is the
Luegmair, Marinus, Gröttrup, Sören
A Self-Learning Framework for NVH CAE Analysis2026-01-0718To be published on 06/20/2026
The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort. Optimizing for Noise, Vibration, and Harshness (NVH), particularly road noise, presents a persistent challenge in the automotive development cycle. While modern FEM analysis is essential, the increasing complexity and sheer volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or sacrifices in resulting comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that combines conventional CAE simulation workflows with machine learning in an iterative cyclic process. This creates a self-learning and self-correcting system that autonomously defines, performs, and learns from computational experiments. By building predictive models from simulation data, the framework intelligently guides the design exploration to achieve
Visser, Rene
NoiseSphere: An AI-Based Approach for Dynamic and Large-Scale Noise Mapping2026-01-0719To be published on 06/20/2026
Noise pollution is a major environmental and health challenge, yet its strong spatial and temporal variability makes comprehensive mapping highly complex. Current approaches under the European Noise Directive (END) provide only partial coverage and often lack temporal dynamics. The NoiseSphere project, fundet by the Austrian Research Promotion Agency FFG, develops an AI-based methodology for dynamic, large-scale noise prediction and mapping. A machine learning model is trained on heterogeneous data sources, including semantically enriched Sentinel-2 satellite imagery, OpenStreetMap road data, floating car data (traffic density and speed), and existing noise maps. The model is refined through integration of noise emission data and validated using targeted in-situ measurements. Two case studies, one located in an urban environment (Graz) and the other one in a rural region near Linz demonstrate the model’s applicability across contrasting settings. By combining remote sensing, traffic
Girstmair, Josef
Development of a Convolutional Autoencoder-Based Unsupervised Classification and Visualization Model for Extracting Feature Frequency in C-EPS Fault Diagnosis2026-01-0717To be published on 06/20/2026
In the automotive industry, deep learning models such as convolutional neural networks, autoencoders, and transformers have been actively studied to improve defect detection rates in End-of-Line (EOL) processes. However, applying these approaches to the field of Noise, Vibration, and Harshness (NVH) faces several practical challenges. ① Difficulty in securing sufficient training data and risk of overfitting due to longer evaluation times compared to other data types. ② Label imbalance caused by the relatively small amount of defective data. ③ Reduced labeling accuracy due to human error. ④ Decreased model robustness under domain shifts such as jig variations, evaluation environments, signal-to-noise ratio (SNR), and the occurrence of new abnormal noises. ⑤ Constraints in application due to compatibility issues with existing evaluation equipment. To address these issues, this study proposes an unsupervised learning-based method for classifying operating noise data of Column-type
Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik
Challenges in Predicting Fatigue Damage during Agricultural Operations: Insights from Machine Learning Models02-19-03-00176/10/2026
Agricultural vehicles operating in rough environments experience increased fatigue damage accumulation, which may decrease machine safety and reliability. Autonomous agricultural machines offer an opportunity to incorporate fatigue damage considerations into path planning. This work investigates whether machine learning can predict fatigue damage to a tractor chassis using light detection and ranging (LiDAR)-based terrain features, vehicle speed, and rotational vehicle state data (e.g., triaxial angle, angular velocity, and angular acceleration). Fatigue damage was estimated using the Rupp filter and the Durability Transfer Concept. Following poor predictive performance of the machine learning models, an exploratory analysis of damage histograms, dominant frequency, and acceleration magnitude was performed. Results indicated that most estimated fatigue damage occurred in the 0–2 Hz band, which coincides with the frequency range of terrain-induced acceleration. On-road driving led to
Govers, Megan Emily, Hamilton-Wright, Andrew, Hassan, Marwan, Oliver, Michele L.
A Review of Welding Techniques in Metal Additive Manufacturing: Advancements, Challenges, and Future Directions05-19-04-00296/10/2026
The present review evaluates recent advances in the development of Welding-Based Additive Manufacturing (WBAM) technologies using arc, high-energy density, solid-state, and hybrid welding systems by providing an interdisciplinary assessment of technological aspects, sensing, process optimization, and multi-process strategies. It is concluded that, in spite of considerable progress in process optimization and control, there exist numerous paradoxes associated with relationships among process conditions, structure, and properties, especially those related to heat input effects on material microstructure and performance. An important finding is the fragmentation of predictive modeling approaches, where physics-based and data-driven methods remain inadequately integrated, limiting generalizability and accuracy. Another important conclusion is related to the dominance of the effect of thermal history and multi-physical phenomena on the mechanical performance of the material produced by WBAM
Santhana Babu, A.V., John Rajan, A., Mishra, Aishwary, Chakravarthy, P., Jayabalakrishnan, D.
Measurement Framework for Automated Vehicle Systems Interaction12-09-04-00276/9/2026
1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential
Griffor, Edward R., Arora, Mahima, Kootbally, Zeid, Nguyen, Vinh
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology
Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao
Automated Design Sustainability Evaluation Framework: A CAD-Integrated Tool for Real-Time Costing and LCA in Automotive Engineering2026-37-00446/9/2026
As the automotive industry faces increasingly rigorous environmental regulations and an approaching obligation for Digital Product Passports (DPPs), incorporating sustainability metrics into the early design phase has become a necessity. Traditionally, Life Cycle Assessment (LCA) and manufacturing cost estimation are performed during or after the design phase using specific methods and tools, resulting in costly iterations and delayed decision-making. This paper introduces a preliminary computational tool that combines 3D CAD and spreadsheet software via VBA integration. The framework automates the generation of an “Extended Bill of Materials” by extracting geometric and manufacturing data directly from CAD models. This tool’s classification logic is a key innovation that intelligently processes CAD features to identify component categories, such as sheet metal, machined parts, or plastic injections. This automated recognition allows the framework to implement specific algorithmic
Guadagno, Maurizio, Cecconi, Leonardo, Berzi, Lorenzo, Delogu, Massimo
Rapid Fuel Distribution Prediction in RCCI Marine Engines Using CFD-Informed, Active Learning Framework2026-37-00176/9/2026
Accurate prediction of in-cylinder fuel distribution (FD) is fundamental to reduced-order combustion modeling and emissions prediction yet remains computationally prohibitive with high-fidelity CFD alone. This work develops a CFD-informed machine-learning surrogate for spatial FD in a large-bore diesel engine, based on a Wärtsilä W20 injector and representative engine conditions. A fully coupled injector–spray–engine CFD framework under engine-like RCCI inert conditions determines the needle-lift profile and resolves the combined effects of injector geometry, needle dynamics, and operating conditions on in-cylinder flow, capturing physical phenomena not reproducible by isolated free-spray simulations. A high-fidelity database is generated using Latin Hypercube Sampling, from which FD is extracted at 15 CAD before top dead center within an annular multi-zone (MZ) representation consistent with reduced-order combustion models. A multi-output Random Forest (RF) surrogate, augmented with
Moradi, Jamshid, Salahi, Mahdi, Heidarabadi, Shadab, Andwari, Amin, Konno, Juho, Wik, Christer, Mikulski, Maciej
Automotive Engineering: June 202626AUTP066/4/2026
New York 2026: diversity on full display New powertrain choices keep popping up on new vehicles from OEMs that debuted at NYIAS this year. Sealing integrity in a Formula 1 limited-slip differential High-temperature hydraulic control in a Formula 1 drivetrain requires dimensional stability, controlled sealing force, and resistance to wear under sustained pressure cycling. Inside the limited-slip differential, the sealing architecture plays a defined mechanical role in maintaining consistent torque management under race conditions. From ADAS to autonomy How engineering thermoplastics can advance sensor-based technologies. Synthetic data and the future of ADAS validation Why ADAS validation can't be solved with more miles alone. Intelligent power distribution will change the way vehicles are designed Electronic fuse (eFuse) technology can create electronic power distribution modules (ePDMs) for architectural flexibility, higher reliability, greater safety, and proactive maintenance
SAE TOMORROW TODAY - The Battery Isn't the Problem--Execution Is135716/4/2026
Batteries don't fail because of chemistry. They fail because of execution. Qnovo provides advanced software to optimize lithium-ion battery health and safety, proving that competitive advantage isn't just about the battery itself, but how intelligently you manage it. By leveraging sophisticated battery models enhanced with AI and machine learning, the company delivers real-time insights into battery performance and operation. Listen in as we sit down with Nadim Maluf, Co-founder and CEO, to discuss how software has become the soul of modern batteries. From extending smartphone battery warranties to enabling faster, safer EV charging, you'll discover why the future of electrification isn't about new materials ... it's about intelligence, scale, and trust. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends
Patterson, Lori
Zero-Shot Classification of Additive Manufacturing Anomalies2026-26-07216/1/2026
Additive Manufacturing (AM) process involves building part layer by layer. Some of the AM processes ( Laser and Electron beam based) generate a melt pool during printing process. This melt pool can be captured periodically during AM process using special optical arrangements. These images capture high intensity melted zone, heat affected zone, splattered molten metal particles and overall shape of the melt pool. These images carry similar characteristics for good AM processes within a range. When there is an anomaly the above said characteristics of the melt pool changes, for example a low intensity melted zone signifies low energy condition which can lead to defects like balling etc. Hence the captured image at this condition appears significantly different from other images. The common defects which can be detected by analyzing melt pool images are porosity, spatter, lack of fusion, cracks, balling and keyhole instability. There are many machine learning methods available to quantify
Kuppusamy, Balasundar
Data-Driven Air Traffic Management: Forecasting Demand, Modeling Delay Propagation, and Demand Capacity Balancing2026-26-07726/1/2026
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance
Senthilkumar, N., S, Gopalakrishnan, Gopinath, S
Automated Flight Manoeuver Recognition for Intelligent Flight Simulators2026-26-07136/1/2026
The rapid growth in the number of aircraft and pilots emphasises the need for an AI-enabled training framework that can offer precise, automated examination of flight manoeuvres. This will be useful in optimising the pilot's training efficiency and minimising iterations of the conduct of flight manoeuvres, thereby reducing the training time of the pilot for a flight. A general framework is developed that can be used for all kinds of flight phases and aircraft types. A pre-trained machine learning model is designed using a supervised learning technique, Random Forest, to recognise different manoeuvres. Various statistical parameters, such as mean, standard deviation, kurtosis, skewness, etc., of several flight parameters were used as the input features to train the Random Forest classifier. In the present work, the classifier is trained using several actual flight test data manoeuvres, and is also supplemented with simulated manoeuvres. The achieved gross accuracy for manoeuvre
Sahu, Akash, C, Poornima, C, Aravindh, Kaliyari, Dushyant, TK, Khadeeja Nusrath
Automating Compliance Workflows in Aerospace Systems: A Scalable Framework for Specification Review and Requirement Mapping2026-26-07146/1/2026
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 important. 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
Mirani, Harsh, Baviskar, Yash G.
Aircraft Cabin Diagnosis – Inspection of the Aircraft Cabins and Seats Using AI2026-26-07896/1/2026
Aircraft interior defects, including seat structural damage, cushion degradation, liquid contamination, and foreign object presence, contribute to increased maintenance burden, extended ground time, and operational inefficiencies. Current inspection practices rely predominantly on manual visual checks, which are time-intensive and limited in detecting concealed anomalies. This paper presents a non-contact, AI-enabled inspection framework integrating millimeter-wave (mmWave) radar sensing with high-definition optical imaging for automated aircraft seat condition assessment. The proposed system captures interior scans when the aircraft is unoccupied and compares them against a digitally established baseline reference obtained under certified, defect-free conditions. Data fusion and machine learning algorithms analyze deviations to identify surface and subsurface defects at seat-level resolution and generate zone-based maintenance maps. The primary technical contribution lies in combining
Nagoal, Chandrasekhar Reddy, Prathipati, Krishna Chaitanya, Kandukuri, Ravindra
Enabling Circularity in the Aerospace Maintenance Ecosystem through Generative-AI Driven Back-to-Birth Traceability of Life-Limited Engine Parts2026-26-07196/1/2026
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 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 life
Bhate, Ujwal, Jain, Dilip Kumar, Kulkarni, Ninad, Kalaiyarasan, Aravindh, Jha, Ashish, Shenoy, Karthik
Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency2026-26-07916/1/2026
Passenger comfort within vehicles 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 modelling 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 adapting environmental conditions. Simulations and early-stage prototypes improve energy savings and improve occupant comfort and air quality. The proposed HVAC
Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta
A GenAI-Based Approach for Real-Time Multilingual In-Flight Communication Systems2026-26-07846/1/2026
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
Enabling Digital Transformation in Aerospace: Automated Model Based Engineering2026-26-07226/1/2026
Model-based development (MBD) and Model-based 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 increase the documentation and validation burden, leading to inconsistent implementations across the project, and raise the risk of missed defects or gaps in compliance. This paper presents an automation framework designed to align with DO-331 objectives by leveraging fine-tuned large language models (LLM) 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, mutation based, and dynamic scenarios to ensure a thorough validation of model behavior. Utilizing AI agent, the framework extracts requirements and key parameters from documentation, enabling automated specification analysis and test script
Lalchandani, Tushar, Purushothaman, Kalaivani, Jeppu, Yogananda, Vijaya Kumar, Shree Harsha, Natarajan, Akilandeswari
AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal2026-26-07816/1/2026
Aerospace products operate within highly complex, safety-critical environments and endure extended lifecycles, often spanning decades. Sustaining their operational value requires rigorous management of Safety, Reliability, and Availability (SRA), while global Environmental, Social, and Governance (ESG) mandates demand parallel progress toward sustainability goals. This paper introduces an AI-driven strategy that integrates these dual imperatives—Sustenance Management and Sustainability Management—within a unified Product Lifecycle (PLC) framework. The proposed approach leverages Artificial Intelligence across five PLC phases: Generative Design, Detailed Design & Verification, Manufacturing & Industrialization, Operations & Maintenance, and End-of-Life Circularity. Anchored by a certified Digital Thread, this framework ensures seamless, auditable data flow from concept to disposal. Using Life-Limiting Parts (LLPs)—such as high-stress turbine discs—as a case study, the paper demonstrates
Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry
Predictive Model and Analysis of the Resultant Force in the Dry Machining of Mg-4Zn/Si 3 N 4 Nanocomposites2026-26-07626/1/2026
The development of lightweight materials for use in aerospace and automotive applications is extremely significant. Magnesium (Mg)-based alloys and composites are good candidate materials from the perspective of low density, good specific strength, and abundance. The Mg-4Zn alloy is one such alloy, which is a lightweight, biocompatible, and eco-friendly Mg-based alloy. In spite of these advantages, there is a strong need and scope to improve its wear resistance and mechanical properties. Mg-4Zn nanocomposites with Si3N4 reinforcements (a biocompatible bioceramic) are hypothesized to possess superior properties. Microstructural analysis of the vacuum stir-cast nanocomposites confirms grain refinement and a consequent increase in microhardness with an increase in Si3N4 reinforcement wt.%. The addition of Si3N4 reinforcement to improve the properties of the Mg-4Zn alloy could introduce challenges in machining. To make products from the nanocomposites, machining them with minimal
N, Anand, Shaju, Tony M, G, Nagamalleswara Rao, D, Bijulal, K, Jayaprakash Reddy, K, Vijayan, Chaman, Joji J
Agentic Digital Thread for Managing the Non-Conformities in Manufacturing of Aerospace Products2026-26-07636/1/2026
Aerospace manufacturing operates within an intricate ecosystem where quality, compliance and traceability are critical to success. Conventional digital thread frameworks provide connectivity but remain largely passive, lacking the intelligence to autonomously manage complex non-conformities across the product lifecycle. This paper introduces an Agentic Digital Thread powered by Agentic AI, designed to transform non-conformity management into an adaptive, self-orchestrating system that actively drives decision-making and corrective actions [1, 4]. The proposed architecture employs a Master Agent to coordinate workflows and maintain end-to-end data continuity, while specialized Agents autonomously manage domain-specific tasks. In the pre-manufacturing phase, these agents proactively validate requirements, material conformity and process planning through integration with PLM, MES, ERP, QMS and supplier systems. In the post-manufacturing phase, the framework extends to concession
Veluri, Sastry, Gopala Krishnan, Kannan
Drilling Studies on Sustainable Lightweight Hybrid Polymer Matrix High-Performance Nanocomposites2026-26-07326/1/2026
Worldwide, engineers are exploring the possibility of using polymer composites in their quest for lightweight materials. In this study, injection moulding was used to develop a biodegradable polymer PLA composite containing 20 wt.% vetiver fibers (VFs) and 2 wt.% nano-silica (nSiO2) obtained from pearl millet, which is sustainable. Materials need machining as secondary operation that required joining. Desirability analysis was used to examine and optimize machining (drilling) studies that were designed with Taguchi's design (L9 orthogonal array). Surface roughness (SR) and delamination factor (Fd) were taken as outputs, while spindle speed (SS), feed rate (FR), and drill diameter (DD) were the inputs. Drilling studies were performed on a single vertical machining center (VMC). ANOVA identifies that the FR had the most decisive influence on SR (F=559.24, p=0.001785), followed by DD and SS. FR is the dominant contributor to Fd (F=379, p=0.00263), followed by SS and DD. At low SS and high
Senthilkumar, N.
Integrating Human factors and Predictive Modeling for Enhanced Aviation Safety: A Data - Driven Approach to Risk Mitigation and System Design2026-26-07956/1/2026
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence
Valiyaparambil, Praveen
Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction2026-26-07186/1/2026
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 (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying
Mohamed, Abbas
AI Based Energy Sustained Series Hybrid Aircraft2026-26-07766/1/2026
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
Weight Estimation of Novel Aircraft Configurations in Early Design Phase2026-26-07736/1/2026
Initial weight estimation from Top Level Aircraft Requirements (TLAR) is a critical first step in aircraft design, yet existing empirical methods are inadequate for novel configurations such as those using Liquid Hydrogen (LH2) or Sustainable Aviation Fuels (SAF). This paper presents a hybrid methodology for top-level weight estimation of such unconventional aircraft. The approach is based on modifying a conventional baseline aircraft, integrating a new statistical model with component-specific weight estimations. A multivariate regression model to estimate the empty weight fraction (We/W0) was developed from a dataset of 44 conventional aircraft, yielding an R-squared value of 0.833. This statistical model was integrated with physics-based models for novel components, including cryogenic fuel tanks and fuel systems. The methodology accounts for iterative changes to fuselage structure and parasitic drag. Four configurations were analyzed: fuel types being Jet A1, SAF, LH2 with aft
Goyal, Tushar
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-07856/1/2026
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. This paper introduces an advanced Electric Health-Monitoring Wiring (E-Wiring) system that 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
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
SAE TOMORROW TODAY - The Brain Behind AI-Powered Vehicles135695/28/2026
What if the brain of your car wasn't just smart but continuously learning, scaling, and orchestrating everything from safety to autonomy in real time? Listen in as we sit down with Anshuman Saxena, Vice President and General Manager of ADAS & Robotics at Qualcomm Technologies, Inc., to explore how the Snapdragon Ride Pilot is reshaping the path to automated driving. From the rise of physical AI to systems that unify sensors, software, and real-time decision-making, we break down what it actually takes to move from fragmented ADAS features to a fully integrated, AI-refined vehicle. If you want to understand where autonomy is really headed (and what the industry might still be underestimating), this is a conversation you won't want to miss. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility
Patterson, Lori
DDPG-Based Energy-Optimal Torque Split Strategy for Dual-Motor Electric Vehicles10-10-04-00335/22/2026
This study presents a torque distribution strategy for dual-motor electric vehicles utilizing a Deep Deterministic Policy Gradient reinforcement learning algorithm designed to optimize energy consumption. By using a simplified architecture and replicable reward functions, the proposed agents rely exclusively on standard CAN bus signals, commanded longitudinal force, and the motors’ velocities, eliminating the need for specialized sensors or complex plant models. Two reinforcement agents are trained using two different reward functions: power-based and State of Charge-based. These agents are validated through high-fidelity CarSim–Simulink co-simulations across soft, medium, and severe acceleration scenarios, in which they demonstrate superior performance to traditional adaptive methods. In the most demanding scenario, a typical adaptive strategy achieves an additional 7.8% of power consumption and 85% of optimal energy recovery, while the proposed reinforcement learning strategies reach
Meléndez-Useros, Miguel, Viadero-Monasterio, Fernando, López-Boada, María Jesús, López-Boada, Beatriz
Energy Saving and Personalized Thermal Comfort Control Based on Reinforcement Learning and Decision Tree2026-99-17145/22/2026
Indoor thermal comfort is closely related to people’s health and work efficiency. Control systems typically consume a large amount of energy to maintain a comfortable thermal environment. Currently, reinforcement learning is widely applied to optimize thermal comfort control systems. However, existing research mainly adopts universal thermal comfort evaluation models that aim to satisfy the majority of people, which makes it difficult to quickly and accurately reflect the specific thermal comfort needs of individuals. As a result, the hot environment is neither comfortable nor energy-efficient in practical use. Therefore, this paper proposes an energy-saving personalized thermal comfort control method based on decision trees and reinforcement learning. First, decision tree learning is used to obtain an individual thermal comfort evaluation model from a small amount of historical data. Then, this individual comfort model is combined with energy consumption to form a reward function
Li, Xianying
SAE TOMORROW TODAY - Can Airports Become Innovation Labs?135685/21/2026
What if your airport was a living laboratory for innovation? Listen in as we sit down with Brian Cobb, Chief Innovation Officer at Cincinnati/Northern Kentucky International (CVG) Airport, to explore how CVG operates as a "city within a city," deploying emerging technologies to improve passenger experience, strengthen operational resilience, and reimagine the modern airport. Discover how CVG is advancing innovation through real-time TSA wait times, robotics testing, and the growing role of AI, autonomy, and cybersecurity. Get even more insight as Brian takes the stage as keynote speaker at the 25th anniversary of AeroTech(R) this June. He'll explore what's next for aviation innovation, from connected robotics and autonomous workflows to the standards and cross-industry collaboration needed to make it all work safely. To learn more and register, visit sae.org/events/aerotech. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast
Patterson, Lori
Hierarchical Reinforcement Learning For Task Generation of Transmission Tower Inspection Using Point Clouds2026-99-07545/15/2026
Unmanned Aerial Vehicles (UAVs) are widely used for inspecting transmission towers. However, traditional waypoint planning relies heavily on manual experience. This leads to low efficiency, incomplete coverage, and a lack of standardization. Facing these problems, this paper proposes an intelligent generation method based on Hierarchical Reinforcement Learning (HRL). This method achieves end-to-end automation, converting raw point cloud data directly into an optimal set of waypoints. Preprocess and grid the point cloud data to build a model of the coverage area. Then design a hierarchical framework to break down the complex planning task. This framework divides the task into high-level waypoint selection and low-level pose optimization. Specifically, the high-level part uses a Deep Q-Network (DQN) to learn the best sequence of waypoints. The low-level part uses Q-learning tables to optimize the pitch and yaw angles for each point. Meanwhile, design a reward function to maximize
Cui, Shicheng, Lin, Shizhong, Shao, Zhan, Chen, Ruiduan, Li, Xingyu, Luo, He
A Reinforcement Learning Trajectory Planning Model with Kalman Filter-Based Dynamic Tracking for Six-Axis Robotic Manipulators2026-99-07385/15/2026
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
Yu, Jingze, Wang, Yujia, Li, Junshen, Chen, Cong, Xu, Peng
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