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This specification covers a nitriding grade of premium aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock. AMS6496 and AMS6497 cover UNS K23280 with other quality levels.
AMS E Carbon and Low Alloy Steels Committee
This specification covers a magnesium alloy in the form of investment castings (see 8.6).
AMS D Nonferrous Alloys Committee
This specification covers an aluminum alloy in the form of rolled or forged rings up to 6 inches (152 mm), inclusive, in thickness (see 3.3.1.1.1) and an OD to wall thickness ratio of 10 or greater (see 8.5).
AMS D Nonferrous Alloys Committee
This study investigates the gradeability performance of an L7e-class electric micro truck from both vehicle dynamics and thermal perspectives. A 1D simulation model (Amesim) was developed and validated with multiple test results. Using inputs such as motor characteristics, drivetrain configuration, and vehicle mass, the model analyzed vehicle performance on a 20% gradient, calculating the required torque, achievable motor speed, and corresponding vehicle speed. Furthermore, gradeability limits were evaluated, and the effects of gear ratio and airflow rate around the air-cooled motor on both gradeability and thermal behavior were examined. The findings provide practical insights for improving the powertrain and cooling system design of lightweight electric vehicles. The results showed that selecting an appropriate gear ratio can enable the motor to operate more efficiently under demanding driving conditions. A 20% increase in the gear ratio was found to delay motor heating by up to 10
Turan, AzimKantaroğlu, Hasan HüseyinAkbaba, MahirKasım, Recep FarukYarar, Göktuğ
As a consequence of the introduction of mathematical human body models (HBMs) in consumer information programs, there is an increased need for reliable methods that can demonstrate and build trust in the capability of HBMs to predict human response and injury risk in crashes. Therefore, a framework for validation of strain-based injury prediction is proposed. The framework comprises stepwise validation with the final step to validate the utility of risk predictions by means of the area under the curve (AUC) combined with Brier scores. SAFER HBM V11.1.0 previously validated at component and body part levels was selected for the demonstration of the final step of the framework to validate the capability to predict fracture risk in frontal, oblique, and lateral loading. For frontal loading, five postmortem human surrogate (PMHS) test series with 43 PMHS (age range: 19–88 years) were reconstructed. The predicted rib fracture risk for 2+ and 3+ fractured ribs was compared to the number of
Pipkorn, BengtNiranjan Poojary, YashOsth, JonasLarsson, Karl-JohanIraeus, Johan
This specification covers a copper-zinc alloy (brass) in the form of sheet, strip, and plate (see 8.6).
AMS D Nonferrous Alloys Committee
This SAE Recommend Practice establishes for passenger cars, light trucks, and multipurpose vehicles with GVW of 4500 kg (10000 pounds) or less, as defined by the EPA, and M1 category vehicles, as defined by the European Commission:
Interior Climate Control Vehicle OEM Committee
This specification covers a low-alloy steel in the form of sheet, strip, and plate 4.00 inches (101.6 mm) and under in thickness.
AMS E Carbon and Low Alloy Steels Committee
This specification covers an aluminum alloy in the form of sheet from 0.063 to 0.249 inch (1.60 to 6.30 mm) in nominal thickness (see 8.6).
AMS D Nonferrous Alloys Committee
E-25 General Standards for Aerospace and Propulsion Systems
This SAE Aerospace Recommended Practice (ARP) establishes methods and identifies opportunities to sample used powder feedstock circulating within closed loop equipment of an additive manufacturing (AM) process for the purpose of showing conformance to a powder specification. Powder within the entirety of closed loop equipment cannot be represented by sampling and testing of discrete, in-process lots. Because powder processing (i.e., reconditioning, conveyance, and storage) is asynchronous with a build cycle, individual samples and their associated tests do not represent the totality of powder committed to a machine. Powder consumed as part of an individual build cycle may only represent a subset of feedstock in circulation within such equipment. Therefore, regular testing to substantiate conformance to a powder specification is required to assert conforming feedstock was consumed during individual build cycles of the AM workflow to fabricate parts or preforms. Operation of some
AMS AM Additive Manufacturing Metals
This SAE Aerospace Recommended Practice (ARP) provides the user with standardized guidelines for the measurement of effective intensity of short pulse width strobe anticollision lights for aircraft in the laboratory, in maintenance facilities, and in the field. A common source of traceability for calibration of the measurement systems, compensation for known causes of variation in light output such as the use of colored lenses, and recommendations which minimize sources of errors and uncertainties are included in this document. Estimates of uncertainty and error sources for each class of measurement are discussed.
A-20B Exterior Lighting Committee
This specification covers a magnesium alloy in the form of investment castings (see 8.6).
AMS D Nonferrous Alloys Committee
This procedure describes a method of measuring the resistance to wet color transfer of materials such as textiles, leather, and composites.
Textile and Flexible Plastics Committee
This document provides a comprehensive compilation of currently available practices, standards, regulations, and guidance material that have been considered relevant for developing an electrified propulsion system (independently or as part of an aircraft) and that may also help the applicants in the process of building their own certification approach with their Authority. It also covers unique considerations for electrified propulsion development and aircraft integration. It focuses on the particularities introduced by the new technology. This document is not intended to represent a proposed Means of Compliance (MoC) with any particular certification regulation.
E-40 Electrified Propulsion Committee
Sparse Stream DETR 3D object detection has become pivotal in autonomous driving, and previous methods achieve remarkable performance by aggregating temporal information, which also face a balance problem of precision and efficiency. Knowledge distillation offers a promising solution to enhance the efficiency of a small model without incurring computational overhead; however, previous methods lack the exploration of the Temporal Distillation knowledge for the DETR detector. This paper designs a novel Temporal DETR Query Guidance paradigm to impart temporal relation knowledge from a powerful teacher model to enable the student to associate object states across time, leverage historical context. The teacher’s queries grasp the temporal knowledge through self-attention, and the backbone uses the EVA-02 large-scale image model. The student utilizes the teacher's self-attention layer and its own learnable queries to compute the attention as its guidance and mimics the feature interaction
Yan, Yixiong
Roller bearings are used in many rotating power transmission systems in the automotive industry. During the assembly process of the power transmission system, some types of roller bearings (e.g., tapered roller bearings) require a compressive preload force. Those bearings' rolling resistance and lifespan strongly depend on the preload set during the installation process. Therefore, accurate setting of the preload can improve bearing efficiency, increase bearing lifespan and reduce maintenance costs over the life of the vehicle. A new method for bearing preload measurement has shown potential for both high accuracy and fast cycle time using the frequency response characteristics of the power transmission system. An open problem is experimental validation of the multi-row tapered roller bearing analytical model. After validation, the analytical model can be used to predict the assembled system damped natural frequency for a desired bearing preload. This work presents the experimental
Gruzwalski, DavidMynderse, James
Recent years have seen a rapid rise in edge-oriented object detection models, including new YOLO variants and transformer-based RT-DETR. Choosing an appropriate model for vehicle detection, however, remains challenged because common metrics such as precision, recall, and mAP capture only part of the trade-off between accuracy and computational cost. To better support model selection, we introduce the Multi-dimensional Equilibrium Detection Assessment Score (MEDAS), which evaluates detectors across four practical dimensions: performance, balance, efficiency, and adaptability. The framework includes a normalization strategy and adjustable weighting so that evaluations can reflect specific deployment needs, especially in resource-limited settings. Experiments on the MS-COCO vehicle dataset show that while RT-DETR models offer competitive accuracy, they require substantially more computation. In contrast, lightweight YOLO variants provide a stronger balance between accuracy and efficiency
Guo, Bin
Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a
Zhao, ZhouqiaoGershon, Pnina
Automotive Original Equipment Manufacturers (OEMs) closely guard information about their products due to the significant investment in vehicle research and development. However, advancing automotive innovation often requires insights from existing systems to improve safety, efficiency, and performance. The Controller Area Network (CAN) bus remains the industry standard for communication between electronic control units (ECUs), yet CAN message specifications are typically proprietary and undocumented. This paper presents a case study involving the reverse engineering of CAN messages from a 2024 Toyota Grand Highlander powertrain. By capturing and analyzing communication between a diagnostics tester and the vehicle’s ECUs and replicating the communication, substituting A CANcase and software in place of a diagnostics tester, we were able to reverse engineer the vehicle’s CAN bus, demonstrating a practical methodology for decoding and interpreting CAN traffic without prior access to
Bolarinwa, EmmanuelPeters, Diane
With the steady increase in autonomous driving (AD) and advanced driver-assistance systems (ADAS) aimed at improving road safety and navigation efficiency, simulation tools have become a critical part of the development process, allowing systems to be tested while mitigating the risk of physical injury or property damage upon failure. Physics-based simulators are central to virtual vehicle development, yet their control responses often differ from real vehicles, potentially limiting the transfer of controllers and algorithms developed in simulation. As these simulations play an important role in the vehicle design and validation process, a critical question is how well their predicted behavior translates to real-world physical systems. This paper presents a calibration framework for an autonomous vehicle platform that learns the motion characteristics of an experimental vehicle and uses that knowledge to correct the actuator response of a simulation model. The model is trained by
Soloiu, ValentinSutton, TimothyMehrzed, ShaenLange, RobinZimmerman, CharlesPeralta Lopez, Guillermo
The useability of development processes in the automotive sector has decreased in the past years to a level at which their application and true benefit to is being questioned. Such degradation can be attributed to new additions to the processes and introduction of FuSa and Cybersecurity standards. The processes try to keep up with the shift from the traditional ‘plan–implement–test–roll-out' methodology to more agile methods. In addition, process departments typically in charge of these processes, focus on compliance to the letter of the standard to achieve certification, often with little thought to the actual implementation and the process they will be used by their engineering teams. Process growth to meet the needs of new and more complex technologies often mandates the use of new tools, which if implemented incorrectly can lead to unnecessary bureaucracy and additional overheads. Furthermore, the language of these new processes is in a form from assessor, making it difficult for
Weber, MatthiasKmiec, MateuszRomijn, MarcelNedkov, Detelin
Introducing machine learning (ML) into safety-critical systems presents a fundamental challenge, as traditional safety analysis techniques often struggle to capture the dynamic, data-driven, and non-deterministic behavior of learning-enabled components. To address this gap, the Machine Learning Failure Mode and Effects Analysis (ML FMEA) methodology was developed as an open-source framework tailored to ML-specific risks. This paper reports on the maturation of ML FMEA from an initial conceptual framework to a proven, practice-driven methodology. We make four primary contributions. First, we extend the ML FMEA pipeline with two new stages: a “Step Zero” for problem definition and system-level hazard analysis, and a “Step 5” for constructing ground truth or reward signals. Autonomous vehicle and humanoid robot applications are presented to illustrate the practical application and safety benefits of these additions. Second, we introduce tailored Severity, Occurrence, and Detection
Schmitt, PaulShinde, ChaitanyaDiemert, SimonPennar, KrzysztofSeifert, BodoPoh, JustinLopez, JerryMannan, FahimMohammed, MajedChalana, AkshayWadhvana, NeilWagner, Michael
With the increasing adoption of electric vehicles (EVs) worldwide, ensuring the long-term reliability and performance of the battery systems has become a paramount engineering challenge. Lithium-ion cells exhibit dimensional changes throughout their operational life, characterized by reversible “breathing”—expansion and contraction during charge and discharge cycles—and irreversible swelling due to aging. Compression pads are critical components for ensuring the lifetime performance of battery packs. The primary function of a compression pad is to act as a compliant cushion between cells. It accommodates these volumetric fluctuations by exerting consistent and optimized pressure. By absorbing the stress from cell expansion and maintaining structural integrity within the module, compression pads mitigate degradation mechanisms and ultimately maximize the durability and safety of the battery system over thousands of cycles. This paper highlights the importance of tailoring elastomeric
Deng, WeilinGunashekar, Subhashini