Browse Topic: Test procedures

Items (12,110)
This SAE Recommended Practice establishes uniform test procedures for friction based parking brake components used in conjunction with hydraulic service braked vehicles with a gross vehicle weight rating greater than 4500 kg (10 000 lb). The components covered in this document are the primary actuation and the foundation park brake. Various peripheral devices such as application dashboard switches or indicators are not included. These test procedures include the following: a Brake Related Tests 1 Brake Functional Performance 2 Brake Dynamic Torque Performance 3 Brake Corrosion Resistance 4 Brake Endurance with Torque 5 Brake Endurance without Torque 6 Vibration Resistance 7 Brake Ultimate Static Load 8 Brake Lining Wear Adjuster Function b Actuation Related Tests 1 Mechanical Actuator Functional Performance 2 Mechanical Actuator Endurance 3 Mechanical Actuator Quick Release 4 Mechanical Actuator Ultimate Load 5 Spring Apply Actuator Functional Performance 6 Spring Apply Actuator
Truck and Bus Hydraulic Brake Committee
The payload fairing of a launch vehicle is subjected to extremely high acoustic loads, with peak levels occurring during lift-off and transonic aerodynamic regimes. The external acoustic field penetrates the fairing, producing intense internal sound pressure levels that can challenge the integrity of spacecraft components. Accurate characterization of the vibroacoustic behavior of the payload fairing and its enclosed cavity is therefore essential to ensure spacecraft survivability. The internal acoustic field is governed by the coupled dynamics of the fairing structure and the spacecraft configuration, making it critical to quantify the acoustic environment for different payload arrangements. This study presents a detailed vibroacoustic analysis of a payload fairing with multiple spacecraft configurations to evaluate the resulting internal sound pressure distribution. Vibroacoustic finite element analysis is employed in the low frequency range, while statistical energy analysis is
S R, Arun RajJayan, MahindGeorge, P
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
This SAE Recommended Practice incorporates a track-based test procedure that produces a representative value for vehicle top speed when operating on a level paved road with a fully charged battery.
Motorcycle Technical Steering Committee
AMS6885/5 is the Material Specification (MS) which defines the requirements of a unidirectional carbon fiber tape epoxy repair prepreg capable of curing under vacuum for repair of carbon fiber reinforced epoxy structures. It also defines the requirements of an epoxy film adhesive to be applied in a co-bonding process with the prepreg for solid laminate and sandwich bonding.
AMS CACRC Commercial Aircraft Composite Repair Committee
This test method covers procedures to qualitatively determine the visual and physical condition of a liquid organic coating component (pigmented base, base without pigment, curing solution, or thinner) in a container. Also covered is evaluation of the component container to determine any degradation.
AMS G8 Aerospace Organic Coatings Committee
While an enlarged lead time from risk notifications to collisions is widely acknowledged to facilitate safe driving, it remains challenging to effectively notify drivers of invisible risks and non-apparent risks coming from uncertain behaviors on the part of road users. The current study examined whether verbal notifications are able to assist early awareness of predictive risks. We also attempted to identify human and environmental factors that could possibly improve the effectiveness of predictive risk information. Twenty-eight licensed drivers participated in a public road test conducted in two different urban areas on 3 days. They drove predefined courses on which potential risk locations were identified prior to the test, using a sport utility vehicle equipped with an automatic verbal notification system triggered based on the distance to the potential risk locations. After passing through the locations each time, the participants were instructed to verbally evaluate the shift in
Maruyama, MasakiKoyama, KeiichiroEzaki, ToruSakamoto, JunichiSawada, YutaMatsuoka, Takahiro
This specification covers a synthetic rubber in the form of sheet, strip, tubing, extrusions, and molded shapes. This specification should not be used for molded rings, compression seals, O-ring cords, and molded in place gaskets for aeronautical and aerospace applications without complete consideration of the end use prior to the selection this material.
AMS CE Elastomers Committee
This SAE Standard defines a method for evaluating the immunity of automotive electrical/electronic devices to radiated electromagnetic fields coupled to the vehicle wiring harness. The method, called bulk current injection (BCI), uses a current probe to inject RF onto the wiring harness in the frequency range of 1 to 400 MHz. BCI is one of a number of test methods that can be used to simulate the electromagnetic field. The test method refers to ISO 11452-4 (please refer to ISO 11452-4 for test procedures). In addition to ISO 11452-4, this test method also includes a differential bulk current injection (DBCI) test. DBCI is described in Section 4 of this document.
Electromagnetic Compatibility (EMC) Standards
This paper describes Waymo's Collision Avoidance Testing (CAT) methodology: a scenario-based testing method that evaluates the safety of the Waymo Driver Automated Driving Systems' (ADS) intended functionality in conflict situations initiated by other road users that require urgent evasive maneuvers. Because SAE Level 4 ADS are responsible for the dynamic driving task (DDT), when engaged, without immediate human intervention, evaluating a Level 4 ADS using scenario-based testing is difficult due to the potentially infinite number of operational scenarios in which hazardous situations may unfold. To that end, in this paper we first describe the safety test objectives for the CAT methodology, including the collision and serious injury metrics and the reference behavior model representing a non-impaired eyes on conflict human driver used to form an acceptance criterion. Afterward, we introduce the process for identifying potentially hazardous situations from a combination of human data
Kusano, KristoferBeatty, KurtSchnelle, ScottFavaro, FrancescaCrary, CamVictor, Trent
Weather-strip sealing systems are critical to automotive closure performance, influencing water- and dust-tightness, aerodynamic noise control, and overall NVH quality. Conventional validation often relies on flat or straight JIG-based tests that inadequately represent the curved, angled, and non-uniform geometries of real closures such as doors, tailgates, hoods, roofs, and fixed or movable glass. This disparity limits the predictive accuracy of sealing performance in actual vehicles. This study proposes a vehicle-integrated validation framework that mirrors true geometric and contact conditions. The methodology combines finite element analysis (FEA) of both flat JIG and full-vehicle CAD geometries with experimental JIG tests, establishing a baseline for pressure distribution, compression load, and sealing contact behavior. A comparative analysis highlights significant deviations between flat-section predictions and vehicle-specific closure profiles. Results demonstrate that the
Ganesan, KarthikeyanSeok, Sang Ho
Electric vehicles (EVs) face unique safety challenges under pole side impact conditions, largely due to the presence of floor-mounted battery packs. Existing regulatory test procedures, such as FMVSS 214, primarily address occupant injury using full-height cylindrical obstacles. These procedures were originally developed for internal combustion vehicles (ICVs). However, real-world roadside crashes frequently involve obstacles of varying heights, such as guardrails, curbs, and median bases. While these obstacles pose limited risk to the passenger compartment, they can intrude into the battery pack and trigger thermal runaway. This study investigates the influence of obstacle height on EV pole side impacts. Finite element simulations of a commercially available sedan were conducted against rigid obstacles of different heights. Results reveal a non-monotonic trend of battery intrusion governed by the interplay between rollover dynamics and structural stiffness. Theoretical analyses were
Ma, ChenghaoXing, BobinZhou, QingXia, Yong
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
Stochastic Preignition (SPI) is an abnormal combustion phenomenon that can occur in spark-ignition engines particularly under high-load operation. SPI is characterized by uncontrolled initiation of combustion prior to spark discharge, an abnormal combustion process that can lead to severe knock events and significant engine damage. SPI has been associated with fuel properties, lubricant composition, and engine design and operation. In this work, a single-cylinder test engine with a dry-sump oil system was utilized to study the SPI response of E10 and E25 fuels with a range of Reid Vapor Pressure (RVP). An automated test procedure was employed, consisting of ten square-waved load profile segments, with each segment composed of 5 min of low-load operation followed by 25 min of sustained high-load operation. These tests were replicated across multiple days of testing including a lubricant triple flush between tests, and an online Fuel in Oil diagnostic measurement. Exhaust particulate
Splitter, DerekJatana, GurneeshDelVescovo, DanDouvry-Rabjeau, JulienFioroni, GinaChapman, ElanaSalyers, John
The market is witnessing an unprecedented proliferation of low-emission fuel components. To effectively evaluate the suitability of these novel fuels for engine applications, fuel blenders and original equipment manufacturers require rapid and reliable assessment methodologies. Traditionally, such evaluations rely on comprehensive engine testing, which, while thorough, is both time-intensive and costly. In response to the growing diversity of emerging fuel options, this work aims to establish a streamlined screening approach capable of effectively replicating the outcomes of full-scale engine testing. We examined the use of a constant volume combustion chamber for the measurement of fuel effects on NOx emissions, with the goal of developing a method to rapidly screen or rank fuels in a small - volume experiment. A small amount of fuel was injected into air at 650°C and 20 bar, where it ignited and burned. The chamber was sampled post-combustion using a chemiluminescence NOx analyzer
Luecke, JonRahimi, MohammadMohamed, SamahNaser, NimalChausalkar, AbhijeetMcCormick, Robert
Safety assurance of Cooperative, Connected, and Automated Mobility (CCAM) systems is a crucial factor for their successful adoption in society, yet it remains a significant challenge. The SUNRISE project has consolidated previous and on-going efforts, and developed a harmonised Safety Assurance Framework (SAF) designed to operationalise the UNECE New Assessment/Test Method (NATM), targeting a wide range of stakeholders including (but not limited to) certifiers, regulators, manufacturers, suppliers, researchers, and assessors. It incorporates a scenario-based approach, underpinned by the system’s Operational Design Domain (ODD) and behaviour for safety assessment. In line with NATM, the SAF consists of multiple pillars: the Audit of manufacturer processes and Safety Management Systems, In-Service Monitoring and Reporting (ISMR) to ensure continued safety during deployment, and Performance Assurance to generate and evaluate safety evidence pre-deployment. While all pillars are integral
Zhang, XizheKhastgir, Siddarthade Vries, StefanHillbrand, BernhardOp den Camp, OlafBolovinou, AnastasiaBourauel, BryanEhrenhofer Gronvall, John FredrikMenzel, ThaddäusNieto, MarcosStettinger, GeorgJennings, Paul
High-Density Polyethylene (HDPE), Low-Density Polyethylene (LDPE) and Ethylene Vinyl Alcohol (EVOH) composite, particularly in high draw molded hollow circular configuration, present unique challenges in evaluating mechanical performance under tensile stress due to anisotropic deformation, geometric asymmetry, and localize thermal gradient. This study introduces an advanced tensile testing methodology designed specifically to assess such regions with greater precision and reproducibility. The method incorporates refines sample preparation protocols, tailored fixture geometry, and adjustable pull speed to accommodate varying thermal histories and draw ratios inherent to molded sections. Systematic variation of asymmetrical, temperature conditions, and clamping techniques revealed significant impact on tensile strength, elongation at break, and strain distribution. Findings emphasize the necessity of customized testing frameworks for molded composites geometries and demonstrate that
Bhalerao, Saurabh Shankar
Hybrid-electric vehicle (HEV) fuel economy test procedures require that the net energy change (NEC) of the battery not interfere with measuring accurate fuel consumption results. SAE J1711-2010 required the NEC to stay within 1% of fuel energy consumption, assuming that residual changes in state of charge (SOC) would have negligible impact. In practice, however, the asymmetry between fuel and electricity conversion efficiencies means that an imbalance of one unit of battery energy can translate into a likely fuel consumption error of roughly three units. A standard S-Factor, a dimensionless ratio of marginal fuel change to marginal NEC change, was introduced in J1711-2023 to improve SOC correction procedures. The method improves upon the previous J1711 (2010) accuracy by correcting all results for NEC changes and expands the NEC-to-fuel ratio (NECFR) window, enabling HEVs to use electric propulsion more aggressively and potentially achieve higher fuel economy in testing and real-world
Duoba, Michael
The final assembly of electric vehicle (EV) drive units includes an essential End-of-Line (EOL) test to ensure both component integrity and Noise, Vibration, and Harshness (NVH) quality. This screening process, which uses dynamometers to measure vibration signals, is critical for identifying defects before a drive unit is installed in a vehicle. A significant source of failure during this test is gear defects, which can arise from manufacturing or handling issues. Traditional EOL testing methods rely on time-domain analysis and the impulsiveness of vibration signatures to detect these defects, a technique with inherent limitations in accuracy. This paper introduces and evaluates a novel approach using Machine Learning (ML) to analyze vibration signals for improved gear defect detection. We discuss the methodologies of both the traditional time-domain and the proposed ML-based techniques. Finally, we provide a comprehensive comparison of their respective efficiency and accuracy
Arvanitis, AnastasiosMichaloliakos, Anargyros
Hybrid mining trucks, as core equipment for mine transportation, face high energy consumption and significant fluctuations in power demand during cyclic operations due to prolonged exposure to demanding operating conditions characterized by heavy loads and variable working conditions. To address the issues of high energy consumption and significant fluctuations in power demand during the cyclic operation of mining trucks, this paper proposes a hybrid mining truck energy management strategy based on global SOC (State of Charge) planning and neural network optimization control. First, a powertrain model was developed for a typical operating cycle of a hybrid mining truck, and its accuracy was validated by comparing it with experimental data. Using dynamic programming algorithms to plan the SOC for single-cycle operations provides a rational reference for energy allocation across different operational phases of mining trucks during a single cycle. Next, using the powerful nonlinear
Yang, JianyuZhao, ZhiguoChen, HuiyongLi, TaoZhuang, WenyuShen, PeihongTang, Peng
Vehicle system testing serves as a critical phase in obtaining road certification for prototype vehicles. While direct road testing with physical vehicles yields the most authentic data, this approach entails significant costs, challenges in reproducing extreme scenarios, and inherent safety risks. In contrast, virtual vehicle-based testing technologies represent advanced simulation methodologies for enhancing development efficiency and quality, effectively mitigating risks associated with complex real-world operating conditions and hazardous physical testing. However, virtual vehicle models often rely on idealized parameters, limiting their ability to reflect real-world dynamics and resulting in lower credibility of test outcomes. Furthermore, as evidenced in current mainstream virtual testing software, environmental simulations predominantly remain confined to the visual domain, with limited direct interaction between dynamic environmental changes and virtual vehicle responses. To
Liao, YinshengCheng, Qing HuaQu, WenyingWang, ZhenfengWu, YanHe, ChengkunZhang, JunzhiLu, Yukun
Accurate torque-trace reproduction on regulatory drive cycles is central to heavy-duty diesel certification and development testing. Conventional controllers such as Proportional Integral Derivative (PID or PI) can be enhanced with gain scheduling and feedforward (FF) maps to satisfy requirements but require extensive calibration and are sensitive to nonlinearities and delay. This paper evaluates a data-driven control framework comprising a recurrent neural surrogate of engine torque (specifically an LSTM – long short-term memory) trained on engine/dynamometer data and a reinforcement learning (RL) policy trained using this surrogate (“world model”) to track requested torque while regularizing control effort. The RL policy (specifically TD3 – twin delayed deep deterministic) is benchmarked against tuned PID and PID+FF baselines on the Environmental Protection Agency’s Heavy Duty Federal Test Procedure (HD-FTP) segments using EPA regression criteria (slope, |intercept|, R2) and tracking
Cook, JamesPuzinauskas, PauliusBittle, JoshuaHall, Spencer
Vehicle testing for fuel economy and emissions is typically performed indoors over standard dynamometer drive schedules to minimize variability and maximize repeatability of the results. In contrast, during on-road operation, operational parameters such as vehicle speed and acceleration and environmental factors such as temperature and wind will change unpredictably. These factors influence vehicle fuel economy and emissions, making on-road operation much more variable than dynamometer results. However, even though on-road conditions may be unpredictable, the on-road operational data can still be used to characterize vehicle performance. This paper describes the development of an on-road vehicle test methodology, with a focus on accounting for on-road factors with a high degree of accuracy while requiring only an achievable and reasonable amount of data. To develop this methodology, a 2016 Honda Civic was instrumented and driven multiple times over a route covering urban, rural, and
Moskalik, AndrewBarba, Daniel
At the U.S. Environmental Protection Agency’s National Vehicle and Fuel Emissions Laboratory, a development project was implemented to compare various test methods for benchmarking the operation of vehicle electric drive units (EDUs). In earlier research, several test methods were identified, of which two were used to test a Chevrolet Bolt EDU: (a) in-vehicle testing of the complete EDU on a chassis hub dynamometer and (b) stand-alone testing of the EDU’s electric motor and inverter in a dedicated test cell after removal from the vehicle. The resulting data sets were compared with each other and with similar data previously published by GM. In this paper, additional EDU test methods are explored. First, the stand-alone testing of the EDU and its subcomponents is expanded to include testing both with and without the EDU gearing. This testing allows the electric motor, inverter, and gearbox to be characterized separately and the EDU to be characterized as a complete unit. Second, in
Moskalik, AndrewSchauer, EthanBarba, Daniel
Road Traffic crash statistics highlight the importance of reducing fatalities among Powered-Two-Wheeler (PTW) riders, and suggest the necessity of a robust method to evaluate PTW crashworthiness performance. The objective of this study is to clarify the relationship between impact conditions and the Head Injury Criterion (HIC) to establish a fundamental basis for determining representative crash configurations for safety. A total of 1,272 PTW-front to car-side impact simulations were conducted by using production car and PTW models. HIC was used as a metric indicating likelihood of head injury. Velocities, impact angle, and impact locations were varied to create response surfaces. The surfaces were evaluated in terms of their accuracy in identifying the representative impact conditions. In addition, head trajectories were analyzed to clarify the kinematics until head impact. The Finite Element (FE) simulations produced the following findings. The HIC distribution by Head Impact Target
Yanaoka, ToshiyukiGunji, YasuakiZulkipli, Zarir HafizMatsushita, TetsuyaCarroll, JolyonPuthan, PradeepMohd Faudzi, Siti AtiqahD-Wing, KakMiyazaki, Yusuke
The evolution toward software-defined vehicles (SDVs) is causing disruption to the traditional automotive supply chain and breaking down the common hierarchical OEM, tier 1 supplier, and tier 2 supplier relationships. With demands for faster software release cycles, more advanced software projects involving multi-party development, and considerations for end-to-end embedded and cloud integrations, new cybersecurity challenges are introduced that no single organization can address alone. Thus, this disruption creates new trust dependencies and requires new models for collaboration, transparency, and joint responsibility in cybersecurity. This paper presents a collaborative cybersecurity model, emphasizing shared responsibility during multi-party development between OEMs, tier 1 and 2 suppliers, engineering services organizations, and technology and services providers. As such, we explore collaborative approaches for each stage in the development lifecycle including design, development
Oka, Dennis KengoVinzenz, Nico
This SAE Standard applies to mobile, construction-type lifting cranes utilizing cantilevered boom crane structures and associated jibs (see Figure 1).
Cranes and Lifting Devices Committee
This test method outlines a standard procedure for performing cyclic reversing load testing on oscillating sliding bearings. The wear data from these tests is to be used for qualification requirements and to establish bearing design criteria.
ACBG Plain Bearing Committee
This document provides vehicle-level data collection, data analysis, and data verification procedures that may be used to verify that an instrument under test (IUT) satisfies the vehicle-level requirements specified in the SAE International (SAE) J2945/1 standard. For the purposes of this recommended practice, “vehicle-level requirements” primarily consist of those requirements which can be verified external to the vehicle. The IUT for these procedures is a configured dedicated short range communications (DSRC) vehicle-to-vehicle (V2V) device as defined in SAE J2945/1 and is installed on a light vehicle. While the IUT is conceptually separated from the vehicle it is installed on, the tests outlined in this document are primarily vehicle-level so the terms “vehicle” and “IUT” can generally be considered interchangeable. Additionally, non-vehicle-level complementary tests, not included in this document, are required to verify that the entire set of requirements specified in SAE J2945/1
V2X Core Technical Committee
One primary cause of NEV fires is thermal runaway initiated by internal short circuit in power batteries, leading to subsequent thermal diffusion throughout the battery system. Severe internal short circuit damage can precipitate thermal runaway phenomena in lithium-ion batteries, potentially culminating in fire incidents involving electric vehicles. Although mild internal short circuit may not immediately induce thermal runaway, continuous charge and discharge cycling can exacerbate such conditions, progressively elevating risks associated with thermal runaway and other pertinent safety hazards. Conventional safety testing methodologies, employing techniques such as crushing and nail penetration to simulate internal short circuit, often amplify the extent of these shorts and fail to accurately replicate less severe, deeper internal short circuit. Additionally, methods incorporating foreign objects like nickel pieces for simulating internal short circuit necessitate battery disassembly
Sun, ZhipengMa, TianyiHan, CeWang, FangRen, Gaohui
This SAE Recommended Practice is applicable to all heat exchangers used in vehicle and industrial cooling systems. This document outlines the tests to determine the heat transfer and pressure drop performance of heat exchangers under specified conditions. This document has been reviewed and revised by adding several clarifying statements to Section 4.
Cooling Systems Standards Committee
In the automotive industry, increasing noise regulations are influencing product sales and passenger comfort, creating a need for more effective noise testing methods. Hardware-in-Loop (HiL) based virtual acoustic testing serves as a critical step before Driver-in-Loop testing, allowing for the assessment of vehicle performance and noise levels inside and outside the vehicle under various conditions before physical prototype testing is performed. The Hardware-in-the-Loop (HiL) simulator setup is equipped with joystick control that requires a physical representation of the vehicle dynamics model provided as a Functional Mock-up Unit (FMU) in real-time format. In contrast, the vehicle control logic is implemented in C++ code. The simulator incorporates both lateral and longitudinal dynamics. Additional interfaces are integrated to support joystick input and virtual road visualization enabling realistic vehicle maneuvering and dynamic performance evaluation. However, performing all test
Visuvamithiran, RishikesanChougule, SourabhSrinivasan, RangarajanLaurent, Nicolas
The Automobile Life Extender (ALE) comprises an on-board function, a machine learning model operating via cloud computing and a smartphone app. The on-board function receives signals such as engine RPM, throttle position, brake pedal position, and hydraulic pressure from the vehicle's ECUs. Based on this data, the on-board ALE module calculates the engine load, brake circuit load, etc., and sends it to the predictive maintenance model via the on-board IoT system. The predictive maintenance model contains recorded data about the type of engine, brake system, and their performance curves acquired from tests conducted by its OEM. Machine learning models holds a crucial role in dynamically analyzing vehicle data, identifying drive patterns, and predicting the need for maintenance of a part or system. A hybrid approach of training models based on supervised and unsupervised learning is incorporated, creating an active learning strategy to maximize the use of available data. Amazon SageMaker
Sundaram, RameshselvakumarKumar, LokeshSaint Peter Thomas, EdwinSureshkumar, SrihariMuthukumaran, ChockalingamMenon, Abhijith
This SAE lab recommended practice may be applied to corrosion test methods such as salt spray, filiform, Corrosion creep back, etc. This procedure is intended to permit corrosion testing to be assessed between Laboratories for correlation purposes.
Wheel Standards Committee
This SAE Aerospace Recommended Practice (ARP) is written for individuals associated with the ground-level testing of large and small gas turbine engines and particularly for those who might be interested in constructing new or adding to existing engine test cell facilities.
EG-1E Gas Turbine Test Facilities and Equipment
This SAE Recommended Practice provides minimum performance requirements and uniform procedures for fatigue testing of wheels intended for normal highway use and temporary use on passenger cars, light trucks, and multipurpose vehicles. For heavy truck wheels and wheels intended to be used as duals, refer to SAE J267. For wheels used on trailers drawn by passenger cars, light trucks, or multipurpose vehicles, refer to SAE J1204. These minimum performance requirements apply only to wheels made of materials included in Tables 1 to 4. The minimum cycles noted in Tables 1 through 4 are to be used on individual test and a sample of tests conducted, with Weibull Statistics using two parameter, median ranks, 50% confidence level, and 90% reliability, typically noted as B10C50.
Wheel Standards Committee
The purpose of this test is to evaluate the axial strength of the nut seat of wheels intended for use on passenger cars, light trucks, and multipurpose vehicles. In addition, a minimum contact area is recommended to ensure enough strength for the rotational force in tightening a nut against the nut seat. While this test ensures the minimum strength of the nut seat, the wheel must also have a degree of flexibility. This flexibility, as well as bolt tension, are important to maintain wheel retention.
Wheel Standards Committee
This SAE Aerospace Recommended Practice (ARP) provides a procedure for obtaining filter patch test samples from the following types of aerospace non-rotating hydraulic equipment: Mechanical/Hydraulic Units Electro/Hydraulic Units Pneumatic/Hydraulic Units
A-6C1 Fluids and Contamination Control Committee
This document establishes a standardized test method designed to provide stakeholders—including runway deicing/anti-icing product manufacturers, users, regulators, and airport authorities—with a means of evaluating the relative ice penetration capacity of runway deicing and anti-icing products over time. The method measures ice penetration as a function of time, thereby enabling comparative assessments under controlled conditions. While commonly applied to runway treatments, these products may also be used on taxiways and other paved surfaces. The test is not intended to provide a direct measurement of the theoretical or extended ice penetration time of liquid or solid deicing/anti-icing products. Instead, it offers a practical and reproducible basis for performance evaluation, supporting operational decision-making and regulatory compliance.
G-12RDP Runway Deicing Product Committee
This paper contains theoretical and experimental studies of the measurement accuracies of two methods commonly used by vehicle industries and other stakeholders to determine vehicle center of gravity (CG) height. The two methods, which both appear in international standards, are the Axle Lift method and the Stable Pendulum method. The Stable Pendulum method requires a dedicated swinging platform mechanism*, but it is generally considered to be more accurate than the Axle Lift method. Both methods rely on equations for computing CG height that are based on static balance models of a vehicle tested at various pitch angles. For each method, the accuracy of the resulting CG height computations is a function of the individual measurements needed in the model equations. The individual measurements needed depend on the method used, but they include weights, angles, and distance measurements. A theoretical error analysis study is presented that provides insight into the accuracy of both
Heydinger, GaryZagorski, ScottBartholomew, MeredithAndreatta, Dale
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
Lad, Rakesh PramodMehrotra, SoumyaMishra, Arvind
Rising environmental concerns and stringent emissions norms are pushing automakers to adopt more sustainable technologies. There is no single perfect solution for any market and there are solutions ranging from biofuels, green hydrogen to electric vehicles. For Indian market, especially in the passenger car segment, hybrid vehicles are favoured when it comes to manufacturers as well as with consumer because of multiple reasons such as reliability, performance, fuel efficiency and lower long-term cost of ownership. For automakers planning to upgrade their fleets in the context of upcoming CAFE III (91.7 g CO2 / km) & CAFE IV (70 g CO2/km) norms, hybridization emerges as the next natural step for passenger cars. Lately, various state governments have also promoted hybrid vehicle sales by offering certain targeted tax breaks which were previously reserved for EVs exclusively. Current study focuses on various parallel hybrid topologies for an Indian compact SUV, which is the highest
Warkhede, PawanKeizer, RubenSandhu, RoubleEmran, Ashraf
In the era of Software Defined Vehicles, the complexity and requirements of automotive systems have increased knowingly. EV Thermal management systems have become more complicated while having multiple functions and control strategies within software frameworks. This shift creates new challenges like increased development efforts and long lead time in creating an efficient thermal management system for Electric Vehicles (EV’s) due to battery charging and discharging cycles. For solving these challenges in the early stages of development makes it even more challenging due to the unavailability of key components such as fully developed ECU hardware, High voltage battery pack and the motor. To address this, a novel framework has been designed that combines virtual simulation with physical emulation at the same time, enabling the testing and validation of thermal control strategies without fully matured system and the ECU hardware. The framework uses the Speedgoat QNX machine as the
Chothave, AbhijeetS, BharathanS, AnanthGangwar, AdarshKhan, ParvejGummadi, GopakishoreKumar, Dipesh
Damping materials exhibit advantageous mechanical and acoustic characteristics that enhance the structural integrity of systems. These materials find extensive applications across various industries, including automotive, aerospace, and building acoustics, and are widely employed in the development of soundproofing materials. The damping characteristics of materials primarily pertain to the dissipation of vibrational energy, the reduction of oscillations, and the controlling and subsequent attenuation of vibration-induced noise emanating from structures. To improve both structural integrity and acoustic performance, it is crucial to accurately assess the damping properties of these materials. The Oberst bar test method is a standard method used in the automotive, railway and building industry for initial optimization of damping material However, questions have arisen about the degree to which the outcomes of the Oberst test truly reflect real-world applications. Numerous experimental
Kamble, Prashant PrakashJoshi, ManasiJain, SachinkumarHarishchandra Walke, Nagesh
As the brain and the core of the electric powertrain, the traction inverter is an essential part of electric vehicles (EVs). It controls the power conversion from DC to AC between the electric motor and the high-voltage battery to enable effective propulsion and regenerative braking. Strong and scalable inverter testing solutions are becoming more essential as EV adoption rises, particularly in developing nations like India. In India, traditional testing techniques that use actual batteries and e-motors present several difficulties, such as significant safety hazards, inadequate infrastructure, expensive battery prices, and a shortage of prototype-grade parts. This paper presents a comprehensive approach for traction inverter validation using the AVL Inverter TS™ system incorporating an advanced Power Hardware-in-the-Loop (PHiL) test system based on e-motor emulation technology. It enables safe, efficient, and reliable testing eradicating the need for actual batteries or mechanical
Mehrotra, SoumyaChhabra, Rishabh
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The
Joshi, Omkar PrakashShinde, VikramPawar, Prashant R
The application of AI/ML techniques to predict truck endgate bolt loosening represents a major innovation for the automotive industry, aligning with the principles of Industry 4.0. Traditional physical testing methods are both expensive and time-consuming, often identifying issues late in the development process and necessitating costly design changes and prototype builds. By harnessing AI/ML, manufacturers can now analyze endgate slam and bolt preload data to accurately forecast potential bolt loosening issues. This predictive capability not only enhances quality and safety standards but also significantly reduces the costs associated with tooling and builds. The AI/ML tool described in this paper can simulate a variety of load conditions and predict bolt loosening with over 90% accuracy, considering factors such as changes in loads, bolt diameters, washer sizes, and unexpected masses added to the endgate. It provides valuable design insights, such as recommending optimal bolt
Sivakrishna, MasaniDas, MahatSingh, AbhinavKarra, ManasaShienh, GurpreetLuebke, Amy
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