Browse Topic: Product development
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
This study details the development and experimental validation of a high-fidelity one-dimensional (1D) simulation model for a two-speed transmission designed for off-road vehicles, such as tractors and backhoe loaders used in agricultural and civil engineering applications. The model, implemented in the AMESim platform from Siemens, integrates physics-based loss sub-models for all major components, including gears, bearings, seals, and fluid drag (churning) losses. After development, the model was rigorously validated against test bench data, with efficiency measurements taken across various speed, torque, and oil level combinations, demonstrating a strong correlation with experimental results. A detailed analysis enabled the quantification of the contribution of each loss mechanism, identifying the countershaft gears and input shaft bearings as the primary contributors. Furthermore, a Machine Learning (ML)–based calibration framework, employing Bayesian Optimization, was implemented to reduce discrepancies between simulation and experiment and to generate a synthetic dataset for the creation of fast-executing surrogate models. The study concludes that the proposed methodology constitutes an effective tool for efficiency analysis and optimization during early design stages, establishing a foundation for future integration with ML techniques and the development of digital twins.
In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.
Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such approach systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active aerodynamic components in test sequences, while BEVs introduce further aerodynamic and thermal aspects for range and efficiency. Thus, extended and automated test definition down to the step-level of test sequences is introduced. Within such integrated environment, AI can be a supporting engineering tool to enhance testing. AI-based methods can assist in identifying relevant test points within complex parameter spaces and in correlating experimental and simulated results, assisting but not replacing established engineering judgment. Also, for the operating department, analyzing process data for maintenance predictions and efficiency optimizations can be assisted by AI-based methods and supporting AI-agents. The approach boosts efficiency by reducing test effort and tedious manual tasks, leading to shorter development cycles, supporting improved time-to-market. Structured workflows and standardized data handling enhance data quality, improve comparability of results, and ensure robust documentation for reliable audit trails. By combining physical testing, simulation, and intelligent processing, the wind tunnel becomes a reproducible, innovation-enabling element in modern product development, positioning software as the backbone of efficient, future-proof aerodynamic testing.
Noise phenomena in automobiles caused by the stick-slip effect are increasingly among the most frequent reasons for customer complaints and therefore represent a critical vehicle quality attribute. To proactively address such issues, stick-slip testing of contacting material pairs is commonly applied during development. However, the predictive capability of current stick-slip test methods remains limited, particularly when highly flexible materials and realistic, stochastic excitation conditions are involved. The flexibility of sealing systems often allows the actual relative motion at the contact interface to be accommodated through adhesion and elastic deformation, thereby delaying or even preventing sliding. To date, this effect has not been represented by any characteristic parameter in conventional stick-slip testing. Instead, existing evaluations focus exclusively on the analysis of occurring stick-slip oscillations. For the initiation of stick-slip phenomena, however, not only the mean displacement between two stick-slip oscillations during the sliding phase is relevant, but also the relative displacement required to initiate the first slip event of the sealing contact. With the algorithm developed in this work, which reproducibly determines the distance to first slip based on changes in the friction force slope, this methodological gap is now closed. The displacement to first slip depends on numerous influencing factors, including profile geometry, normal load, sliding velocity, excitation profile, and environmental conditions, and was previously inaccessible by both experimental and numerical approaches. In particular, the onset of slip in sealing contacts can now be determined under stochastic excitation of the friction pairing, thereby closely reflecting real operating conditions. As a result, the prevention of noise phenomena can be significantly strengthened at an early stage of vehicle development.
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 stage, 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 serve as training data, for various hole distributions. The trained model is then used to predict acoustic natural modes and natural frequencies from unseen input images representing different hole configurations in the package tray. These predictions are made in a fraction of the time required for traditional simulation methods, thereby validating the model’s effectiveness. Furthermore, we demonstrate that the latent variables embedded in the trained model can be manipulated to control the acoustic modes and natural frequencies of the sedan. This indicates the potential for artificial intelligence-driven acoustic design optimization in early-stage vehicle development, offering both time efficiency and design flexibility without physical prototyping or extensive simulations.
Kubota introduced the new SVL110-3 compact track loader at CONEXPO 2026 in Las Vegas. The SVL110-3 delivers 112.7 gross horsepower (84.0 kW), an increased torque output of 279 lb-ft (378 Nm) compared to previous models and a rated operating capacity of 3,700 lb (1,678 kg). The SVL110-3 is capable of 45 GPM (170 L/min) of auxiliary flow while operating with the same traveling speed and compact footprint as its predecessor, the SVL97-3. Kubota states that this increase in auxiliary capacity enables contractors to operate high-demand attachments like trenchers, cold planers and skid cutters at full performance without compromise.
The aerospace industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major OEMs are moving towards fully model-based development processes, with plans to deliver requirements exclusively in the form of models. It is no longer sufficient to manage requirements using traditional document-based approaches; instead, organizations must adopt tools and processes that enable the consumption, interpretation, and implementation of model-based requirements. However, MBSE itself does not ensure that the requirements defined within the model are complete or consistent. Without rigorous validation techniques, even well-structured models can carry forward poorly defined or conflicting requirements — leading to errors that propagate throughout the development lifecycle. This work proposes an approach that integrates formal methods into MBSE workflows by enabling completeness and consistency checks of SysML-based requirements within Cameo Systems Modeler. The method bridges Cameo Systems Modeler with formal analysis tool by transforming modeled requirements in Cameo into analyzable formal specification. The transformed formal requirements allow engineers to identify missing, conflicting, or unreachable requirements early in the development lifecycle, while also aiding automated test generation.
Future military operations are expected to take place in highly dynamic, contested and multi-domain environment, where speed, flexibility and survivability are essential. Fast rotorcraft are emerging as critical asset to meet future operational requirements, offering a hybrid solution that bridges the gap between conventional helicopters and fixed-wing aircraft. Given the increasing complexity of both operational requirements and system architectures, a Model-Based System Engineering (MBSE) approach has become fundamental to support concept design. However, MBSE is often neglected in the earliest phases of aircraft concept definition and proposal process, when business and mission requirements are agreed between Contractor and Supplier, due to the fast pacing of Parties interactions with respect to the time and effort required to perform modelling activities. This prevents nurturing the benefits of MBSE in this crucial phase, and it generates omissions in the systems engineering data to perform design validation in future phases. This paper describes a tailored methodology to make MBSE feasible to support project requirements agreement and initial aircraft sizing.
This paper examines the documented evolution of Kaman Aircraft Corporation's early helicopter development, specifically the progression from the K-225 evaluation aircraft to the groundbreaking HTK-1K drone helicopter. Through analysis of primary and secondary sources, this study establishes the technical and operational foundations that enabled the world's first remotely controlled helicopter. Additionally, this paper critically examines a hypothesis suggesting that 1st Lt. Donald M. Thompson may have been involved in preliminary remote-control helicopter experiments prior to the officially recognized HTK-1K program. While initially appearing speculative, this hypothesis gains substantial support from the discovery of a 1944 Army Air Forces memorandum documenting Thompson's position as Chief of Special Weapons Unit at Wright Field, with explicit responsibility for developing radio-controlled aircraft systems. This primary source evidence establishes Thompson as a documented historical figure with relevant expertise, though direct evidence of helicopter-specific work remains to be discovered. The paper outlines a methodological framework for continued archival investigation and examines the legacy of these early programs on modern unmanned aerial vehicle development.
Now that Modular Open Systems Approaches (MOSA) are being incorporated into the development of weapon systems that are acquired by the U.S. Department of War (DoW), attention is turning to transitioning disparate standalone weapon systems into an enterprise portfolio of weapon systems, a Family of Systems (FoS), whereby the effective management of a common constraining, or reference, architecture can aid in realizing the objective of 'develop once, reuse many times.' This is particularly challenging when enduring fleet legacy weapon systems are involved in addition to new development systems. Model Based Systems Engineering (MBSE) methodologies and techniques have now become the norm in system developments. It is, therefore, imperative to effectively employ MBSE techniques in establishing a FoS. This paper proposes an MBSE-based Product Line Engineering (PLE) method for implementing FoS architectures that enables controlled architectural variation while preserving enterprise reuse and architectural consistency.
The rapid expansion of electric aviation and eVTOL operations introduces tightly coupled challenges related to energy‑constrained aircraft design, battery and thermal management, mission planning, and the generation of certification‑relevant evidence. This paper presents an integrated simulation workflow developed by AVL, Unisphere, and blueflite that combines high‑fidelity electric powertrain and battery models with a guidance‑level, digital‑twin‑based 4‑D trajectory simulation driven by historical weather and operational constraints. At each mission time step, the trajectory layer provides time‑resolved environmental and routing conditions, while the system‑level models compute instantaneous power demand, state‑of‑charge evolution, and thermal response, enabling mission feasibility assessment under realistic wind, temperature, and airspace effects. The workflow is calibrated and validated using flight telemetry from blueflite's active eVTOL cargo aircraft development, ensuring alignment between simulation assumptions and real‑world mission execution. The validated framework is subsequently applied to seasonal route studies and large‑scale virtual flight campaigns spanning multiple regions and years, enabling statistically robust assessment of energy margins, thermal behavior, and mission‑duration variability. The results demonstrate how integrated, traceable simulation can bridge conceptual design and real‑world electric flight operations, supporting informed decision‑making by OEMs and operators in aircraft design, validation, and deployment planning.
Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of material variability and load-estimation uncertainty on fatigue life, cumulative probability of failure, and hazard rate. For the CH-146 demonstration case, the results indicate that the material fatigue strength uncertainty has a major impact on the lower tail of the life distribution and the corresponding reliability-based life, whereas load-estimation accuracy uncertainty has a secondary influence on risk metrics. The application of the digital twin framework to operational, search and rescue, and training mission data further shows that mission-specific usage variability plays an important role in the evolution of fatigue damage accumulation and structural risk. Overall, the proposed framework provides a more informative basis for risk-based rotorcraft life assessment by explicitly quantifying uncertainty and incorporating aircraft-specific operational data. The study is intended as a step toward validation of the framework rather than a completed operational deployment.
Ultrasonic welding (UW) provides a rapid and efficient method for joining composite components by inducing resin flow through thermally driven diffusion and crystallization at the bonded interface. However, in the absence of a multiphysics modeling framework or a digital twin approach, current practice still depends on extensive trial-and-error testing to determine key welding parameters such as vibration amplitude, weld time, weld pressure, hold time, and downspeed. While in-situ thermal cameras can monitor surface temperatures, the internal temperature at the bonded interface is often significantly higher, introducing the risk of thermal degradation and inconsistent bond quality. To overcome these limitations, GEM developed a high-fidelity multiphysics model to establish a quantitative relationship between process parameters and the evolving temperature field within welded thermoplastic parts. The model integrates coupled mechanical, thermal, and acoustic physics to simulate high-frequency vibrations and static pressure, capture the generation and spatial distribution of heat, and represent the temperature-dependent viscoelastic response that governs bond formation. A validation test matrix was designed by systematically varying weld time and vibration amplitude. Through-thickness temperature distributions were measured using infrared thermal imaging, enabling direct comparison with model predictions. Upon validation, the model was applied for process tailoring, allowing precise control of temperature distribution to achieve target bond strength. This integrated modeling and validation approach demonstrated substantial benefits, including reduced design iterations, accelerated process optimization, and improved quality and performance of welded composite structures.
This presentation outlines the U.S. Army’s H-60M Black Hawk modernization approach to address evolving operational needs and the demand for agile acquisition. To accelerate capability delivery, the Utility Helicopters Project Office leveraged a Modular Open Systems Approach and Model-Based Systems Engineering to establish a Modernized H-60M System Model aligned with Capability Program Executive Aviation’s Enterprise Architecture Framework. This authoritative model captures comprehensive mission-driven requirements to enable phased, incremental technology insertions instead of a monolithic, multi-year development cycle. A Capability Assessment Model (CAM) Framework will extract key capabilities and architecture drivers from the model, translating rigorous digital engineering into agile industry solicitations. Using Model-Based Requests for Information, the CAM Framework will allow the Army to systematically evaluate solutions, execute data-driven trade-offs, and rapidly field “minimum viable capabilities.” Ultimately, this strategy ensures the Black Hawk remains an adaptable, mission-critical asset, delivering continuous capability improvements to the warfighter at the speed of relevance.
The UH-60 Black Hawk — manufactured by Sikorsky Aircraft Corporation — is a twin turbine engine, single rotor, semi-monocoque fuselage rotary wing helicopter used primarily for Utility (tactical transport of troops, supplies, and equipment) purposes. In August of 2024, an experimental effort known as Transformation in Contact was called for, where systems would be more simple, intuitive, low signature, and iterative. This effort, along with the implementation of MBSE, has become a critical component for evaluating and refining technologies that could be needed without delay. This paper will serve to provide the collective results of the digital thread being developed for the Black Hawk as well as explore the efforts and processes utilized for this design. In particular, how the application of a Modular Open Systems Approach (MOSA), integration of a digital backbone, and utilization of the Capability Program Executive (CPE) Aviation Enterprise Architecture Framework (EAF) has enabled a cohesive standard for the rapid technology insertions while reducing cost, increasing efficiency, and improving the overall maintenance and sustainment for the aircraft.
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