Browse Topic: Identification

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Identification of Uncertain Parameters in the Dynamics Model of a Rotary Storage Mechanism Using the Stellar Oscillation Optimization Algorithm2026-99-01487/31/2026
The rotary storage mechanism is a critical component responsible for transferring cylindrical units. To accurately simulate the nonlinear dynamics characteristics of the rotary storage mechanism, a dynamics model incorporating uncertain parameters is established based on the Lagrange method. Utilizing an optimization approach, uncertain parameters of the rotary storage mechanism are identified based on test data. The Stellar Oscillation Optimization (SOO) algorithm is employed, which balances exploration and exploitation by simulating the periodic expansion and contraction of stars to achieve optimal solutions. The results show that the output of the identified dynamics model under two operating conditions closely matches the test data, validating the accuracy of the model and the effectiveness of the identification process. This provides strong support for subsequent reliability analysis and fault diagnosis studies of the rotary storage mechanism.
Li, Ang, Chen, Guangsong, Huang, Peng, Li, Hanning
Position-Dependent Milling Stability Modeling and Experimental Validation for Thin-Walled Components2026-99-02357/31/2026
Thin-walled structures with weak stiffness are widely applied in aerospace, precision machinery, and mold manufacturing; however, their machining processes are commonly challenged by insufficient rigidity, complex dynamic characteristics, and a high susceptibility to chatter. Due to the differentiated dynamic parameters of these structures at various spatial positions, the compliant interaction between the tool and workpiece during milling significantly increases the risk of chatter, thereby degrading machining precision, surface integrity and productivity. To this end, this research establishes a three degree of freedom (3-DOF) milling process dynamics model based on the full discretization method (FDM), which systematically obtains the modal parameters of the thin-walled component at different locations. Building upon this, position-dependent stability lobe diagrams for milling prediction are constructed to theoretically reveal the influence of local structural regions on milling stability. Furthermore, this paper proposes a multivariate nonlinear regression method to establish a nonlinear identification model for milling force coefficients. Key parameters were effectively identified through experiments, predicting the variation trends of force coefficients under different cutting conditions. Subsequently, cutting experiments were conducted across different spatial regions of the thin-walled structure to comparatively analyze stability performance under various combinations of cutting parameters. The results demonstrate that the established dynamic model and force coefficient identification method can effectively predict the milling stability of weak-stiffness structures at different physical locations and can well explain the spatial distribution characteristics of cutting chatter. This research proposes a novel method for position-dependent milling stability prediction, providing a theoretical foundation and experimental data support for resolving the issue of frequent chatter at different locations on weak-stiffness structures in practical machining, which holds significant engineering value for the efficient and stable processing of complex thin-walled components.
Xi, Chenhui, Zhao, Yongsheng, Xu, Jingjing, Gao, Pengfei
Parameter Identification for the Dynamics Model of a Robotic Grasping Process Based on SFOA2026-99-01237/31/2026
To precisely simulate the nonlinear dynamic characteristics of a robotic arm grasping cylindrical objects from storage units, this study establishes a dynamic model of the robotic grasping process incorporating Coulomb and viscous friction models to characterize frictional properties. Furthermore, to effectively identify unknown parameters in the dynamic model, a parameter identification method based on the Superb Fairy-wren Optimization Algorithm (SFOA) is proposed. The root-mean-square error (RMSE) between the displacement responses from the dynamic model and the experimentally acquired displacement data serves as the optimization objective. Multiple sets of experimental data are utilized to identify the unknown parameters of the dynamic model. The results demonstrate that when the identified parameters are applied to the dynamic model, the goodness-of-fit between the model’s response displacement data and the experimental displacement data exceeds 0.999. This validates the effectiveness and accuracy of the proposed method for identifying unknown parameters in dynamic models.
Shen, Shaofeng, Yang, Liu, Wang, Zihan, Han, Qunyi
Identification and Quantification of PAHs in Gasoline Soot Using ASE-GC-MS/MS2026-99-01107/31/2026
In the present work, a novel method that combines accelerated solvent extraction (ASE) and gas chromatography coupled with triple quadrupole tandem mass spectrometry (GC-MS/MS) was proposed to identify and quantify polycyclic aromatic hydrocarbons (PAHs) in gasoline soot. The n-hexane was employed to extract the target analytes, and the optimal extraction conditions were identified (cycle times = 3, extraction time = 30 min, extraction temperature = 120°C, and extraction pressure = 100 MPa). The extraction efficiency of six analytes was measured to assess the ASE method; the formation mechanism of partial PAHs was discussed, and the 18 PAHs in gasoline soot were studied both qualitatively and quantitatively under the optimal conditions. It was found that our new method reached a high correlation coefficient (between 0.9987 and 0.9997); the limits of quantification (LOQs) (S/N = 6) for these PAHs were between 0.003 and 0.009 ng/mL with a relative standard deviation (RSD) of 2.9–10.6%. Our method demonstrated good performance in determining the target analytes in soot samples, such as gasoline soot, some materials soot, co-combustion soot, gasoline, and materials. The PAHs differences in soot samples containing gasoline and materials soot samples were significant enough to obtain the observed discrimination. The method is an accurate and sensitive quantitative method to identify gasoline residues in soot samples of arson fire.
Liu, Shujun, Cao, Henan, Qi, Lijie, Liu, Yang, Li, Qi
Research on Identification of Key Geometric Errors in Five-Axis Machine Tools Based on Global Sensitivity Analysis2026-99-01597/31/2026
The geometric error (GE) accounts for a significant factor affecting the machine tool’s machining accuracy, and in most cases, large GEs will result in a substantial deviation from the required shape of the machined workpiece. GEs are often observed in five-axis machine tools, and identifying and measuring these errors turns out to be challenging. In the present work, we proposed a novel GE identification approach based on simulations and tests conducted on a BC-type dual-rotary five-axis machine tool. Specifically, a machine tool volumetric error model (VEM), incorporating 41 GEs (the complete model), was constructed using the homogeneous coordinate transformation approach. Then, Sobol sensitivity analysis in conjunction with quasi-Monte Carlo estimation was introduced to the VEM to measure how much each GE contributed to the total volumetric error. The subsequent analysis identified 21 key geometric errors (KGEs). We also compared the simplified VEM and the complete model, and it was revealed that there was little difference between the two, which confirmed the effectiveness of our method. The present work is intended to provide a reference for simplifying VEMs, error element identification, and error compensation.
Zhang, Jinlong, Shi, Zhaoyao, Yang, Hongtao
Cross-Condition Bearing Multistage Degradation Identification Based on Health Indicator Matrix and Transition Sample Enhanced Network with Multi-Branch Encoding2026-99-08587/30/2026
Precisely detecting multi-stage degradation (MD) in rolling bearings is crucial for keeping equipment in good shape. Yet, health indicator (HI) crafted with current single-method strategies often can't balance degradation sensitivity and monotonicity across different operating conditions. Also, common MD detection methods struggle to spotransitional samples between degradation stages in cross-condition settings. To tackle these challenges, this paper introduces a new cross-condition MD detection approach for bearings, which relies on a health indicator matrix (HIM) and a transition sample enhanced network with multi-branch encoding (TSEN-MBE). First, a degradation-sensitive health indicator (DSHI) is constructed by integrating the least absolute shrinkage and selection operator (LASSO) algorithm — with comprehensive fault frequency energy (CFFE) as the regression target — and the grey wolf optimizer (GWO), capturing intrinsic degradation characteristics of bearings. Meanwhile, to enhance the monotonicity of unsupervised HIs, a time-weighted Wasserstein distance (TWWD) metric is proposed by incorporating temporal degradation features into the Wasserstein distance-based HI construction. The DSHI and TWWD are subsequently combined to generate the HIM. This HIM serves as the driving force for the Gath-Geva (GG) fuzzy clustering algorithm, enabling it to adaptively allocate MD labels according to varying operating conditions. Ultimately, the TSEN-MBE model is constructed, employing multi-branch Transformer encoders integrated with multi-head attention mechanisms to encode and combine heterogeneous features. A joint loss (JL) function — comprising transition sample enhancement (TSE), local maximum mean discrepancy (LMMD), and cross-entropy (CE) losses — is designed to enhance the recognition of transitional samples and improve cross-condition MD identification accuracy. Experimental results on the XJTU-SY dataset validate the effectiveness and superiority of the proposed method, showing that DSHI achieves the highest average degradation angles, TWWD obtains optimal monotonicity, and TSEN-MBE outperforms comparative methods in cross-condition recognition tasks.
Ma, Jinghua, Wei, Lai, Hu, Guangqiao, Yu, Xiaoxia
Research on Early Warning Mechanisms for Fastener Substitution in Aircraft Production2026-99-16247/24/2026
This research overcomes the serious problem of unregulated fastener substitution in aviation manufacturing, which is due to supply chain disruption, design modification, improved production, and permanent installation of substitute fasteners other than temporary installation substitutes. It can introduce potential risks, including the differences between designed and as-built configurations, and problems with the structural strength of parts. Analysis of a 20XX aircraft model that has been documented with 9 types of fasteners reveals that shortages of 4CE5 and 1CD6 remain constant manufacturing nonconformities and a major element causing long term quality erosion. We have developed an early warning system centered on data with the introduction of the Tolerable Substitution Ratio (TSR) and the non-substitution ratio (NSR). Empirical results show that after implementation, the substituted materials can save as much as 25%, which is approximately $534,000 on domestic sourcing costs and permanently revised drawing costs. We should consider both users’ specifications and the production facility’s actual capabilities when designing the degree of substitution tolerances; substitution deviating from the original specification would not be tolerated. For an extended cycle longer than one year, phase adaptive tolerance adjustments are critical for achieving the acceptable quality limit (AQL). Real-time alignment of the key trigger point in the process stream with supply chain analytics takes away the historical trade-off between operational efficiency and the quality of the final deliverable. The result of this process is that there were more than 1,600 fewer ad-hoc deployments but higher levels of system stability, even as the processes had become more unstable. The payoff in terms of verified protocols for mitigating risk was much greater.
Feng, Yu
Starter Battery Identification and ClassificationJ2981_202607 (Current)7/15/2026
This document provides recommendations to identify battery group sizes and dimensions for 6 V, 8 V, 12 V, and 24 V lead acid batteries.
Starter Battery Standards Committee
A Novel Rotating Component Comprehensive Diagnostic Network Based on Stacked Auto Encoders and Sparse Filter Combined with Generative Adversarial Network2026-99-05347/10/2026
Nowadays, the majority of intelligent fault diagnosis approaches are still centered on individual faulty components, while only a limited number of models are capable of performing integrated diagnosis for rotating systems that consist of shafts, bearings, and gears. Under variable-speed operating conditions, the large scale of vibration data further complicates the process of effective feature extraction. To improve these challenges, this study develops a comprehensive diagnostic framework for rotating components, termed WGAN-SAFC. The proposed architecture integrates a Wasserstein Generative Adversarial Network (WGAN) with a hybrid structure of stacked autoencoders and sparse filtering (SAFC). SAFC integrates the feature-learning capability of SAE and the sparsity-driven representation of SF, while incorporating adversarial data generation to address sample imbalance and enhance fault diagnosis performance. Experimental verification on collected vibration datasets demonstrates that WGAN-SAFC achieves superior diagnostic accuracy and robustness compared with existing methods.
Li, Shunming, Feng, Mengqi
Research Review on Data Fusion-Based Ship Information Perception Technology2026-99-05717/10/2026
With the development of domestic vessel traffic service (VTS) systems, China has established a comprehensive maritime traffic management infrastructure. Marine sensing equipment, including radar, the automatic identification system (AIS), and electro-optical (EO) systems, provides diverse sources of ship information. In recent years, data fusion technology has attracted increasing attention for its potential to improve the accuracy and completeness of ship perception. This paper introduces key ship information sensing technologies and examines the distinct characteristics of each approach. It then reviews recent advances in three main areas: vision-based ship feature recognition, multi-source data association analysis, and ship motion prediction. Finally, the paper outlines prospective research directions, including the integration of additional data sources, real-time data processing, enhanced data security, and intelligent maritime decision-making.
Zhao, Kui, Song, Zheming, Huang, Yuantao
Aircraft Noise Event Identification Technology Based on Single Acoustic Vector Sensor2026-99-05917/10/2026
With the rapid development of China’s civil aviation industry, the problem of airport noise has attracted widespread social attention. The requirement for the real-time monitoring and evaluation of acoustic environment around airports is becoming more and more intense. The identification of aircraft noise events in the complex acoustic environment surrounding the airport is the most critical technical problem in airport noise monitoring. However, the traditional noise source identification technology is difficult to be widely used in real-time monitoring system due to its large errors and complex deployment conditions. This paper presented an aircraft noise source identification technique based on a single acoustic vector sensor. The azimuth parameters of the noise source were estimated by the three-dimensional spatial positioning algorithm of sound pressure and particle vibration velocity combined with information processing, and the three-dimensional footprint of the noise event in the complex acoustic environment was described. Finally, the event was judged as an aircraft noise event by matching the noise footprint with the aircraft flight path. By monitored and analyzed the actual noise events of aircraft departure, the results show that this method can only use a single acoustic vector sensor to locate the aircraft noise source and distinguish the aircraft noise event from the background noise event, which provide a new lightweight method for the real-time airport noise monitoring system to locate the noise source and identify the aircraft noise event
Hou, Jiayu, He, Tianlun, Zhu, Lin, Chen, Ying, Liu, Yinhui, Lv, Lei, Wang, Yuhao, Chen, Da
Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data2026-01-07787/1/2026
This study investigates the feasibility of identifying individual e-bike riders based on 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 KNN, Random Forest and Naïve Bayes. The findings demonstrate the efficacy of machine learning in differentiating riders, with Random Forest and KNN achieving the highest and most robust accuracy among the tested models. The KNN-model achieves up to 99% accuracy, the Random Forest up to 75%. The paper analyzes the influence of different signals, feature extraction methods and model parameterizations on the results. The results show that machine learning-based rider identification using CAN data is a viable approach for enhancing e-bike security, authentication, and personalization. Potential applications include theft prevention, automatic user recognition for personalized assistance settings, and access control. Future research could include the exploration of the impact of additional sensor data, real-world outdoor conditions, and deep learning approaches, with the aim to further enhance identification accuracy and efficiency.
Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner
Experimental Validation of Frame-Dependent Gyroscopic Frequency Splitting in a Freely Rotating Tire Using Wireless MEMS Accelerometers and Scanning Laser Doppler Vibrometer2026-01-07006/20/2026
Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both frames. The application of the proposed transformation maps the co-rotating branches onto the inertial observations, yielding consistent forward/backward splitting between the two measurement systems.
del Fresno Zarza, Javier, Naets, Frank
A New Approach to Identify Rigid Body Rotations When Measuring the Body Distortion of a Full Vehicle2026-01-06826/20/2026
When developing a vehicle, the overall body stiffness is an important parameter to be estimated for several automotive attributes. As a complement to the traditional experimental and computational static torsional stiffness assessment, an improved method has been developed to evaluate the body stiffness when driving the vehicle on a test track. This method, valid for both test and simulation, is called Opening Distortion Fingerprint (ODF) and uses the so-called Multi Stethoscope (MSS) to measure the dynamic distortion in each body closure opening and cross section. For evaluating the distortion, from both test and Multi Body Dynamics (MBD) simulation data, the Evaluation-line (E-line) method is used. The E-line method is a linear approach. Consequently, it is only valid in the absence of large rigid body rotations of the vehicle body. Therefore, to assess the validity of the ODF method, it is crucial to identify the frequency at which the distortion results become invalid due to rigid body rotations. To identify this frequency range, in an MBD simulation the total distance output parameter can be requested and used. But for a dynamic full vehicle test, it is a major challenge to measure the total distance. Several tests have been performed without success. To calculate this frequency range from test data, this paper presents a new approach. In this methodology two different signal processing methods (E-line and Diagonal) are combined. To check the validity of the new approach, full vehicle test data has been evaluated. In addition, a simplified beam lab experiment is presented, highlighting the difference between test and MBD simulation when measuring the distortion at large rotations.
Olger, Emma, Lindkvist, Lisa, Piiroinen, Petri, Karypidis, John, Pena, Milton, Bäcklund, Jesper, Appelgren, Peter, Marberg, Henrik, Ugale, Pravin, Weber, Jens
Crowd Counting Network for Risk Early Warning in Exhibition Scenarios2026-99-07055/15/2026
As a densely populated public place, exhibitions feature spatial layouts with multi-area linkage and instantaneous crowd flow mutations. Thus, developing a crowd flow early warning system adapted to exhibition dynamics is a key focus at the public safety and smart exhibitions to avoid risks like local congestion-induced stampedes. In general, two core challenges in exhibition crowd counting: 1) Key dynamic gathering information is hidden in high frequency components, but no correlation mechanism between frequency components and scene has been established; 2) Instant crowd gatherings cause high-frequency local density mutations, leading to time delays and spatial ambiguity of dynamic signals. To solve these, we propose a novel Crowd Counting Network for Risk Early Warning in Exhibition Scenarios with two core modules: 1) A bidirectional feature filtering module optimizes frequency information through low-frequency suppression to reduce redundancy and high-frequency activation to enhancing dynamic signals. 2) A lightweight self-attention module captures long-range dependencies of high-frequency features via frequency-domain self-attention, enabling accurate identification of feature clusters from gatherings. Validated on datasets like ShanghaiTech and UCF-QNRF, this method provides an integrated solution for exhibition security monitoring, promoting crowd counting technology from general scenarios to industry customization.
Zhang, Jin, Zhang, Wanyue, Yuan, Jingjing, Chen, Zhen, Gu, Dazhi
Pattern-Guided Implicit Sentiment Analysis: Knowledge-Augmented Prompting with Aspect-Sentiment Joint Extraction for Automotive User Feedback2026-99-07535/15/2026
Implicit sentiment analysis of automotive user feedback is crucial for understanding user opinions. Automotive user feedback often express opinions in an indirect way and are accompanied by a dense array of industry terms. Therefore, without costly fine-tuning, both aspect identification and sentiment analysis are rather difficult. We propose a Pattern-Guided pipeline for implicit sentiment analysis to achieve the joint extraction of aspect and sentiment. This pipeline first performs Pattern Anchoring, mapping colloquial expressions and slang to the standardized vehicle component knowledge system. Then, using Knowledge-Augmented Prompting, these domain rules are injected into well-designed prompt templates. In this pipeline, the large language model (LLM) is applied to output JSON records suitable for comprehending, including aspects, sentiments, confidence levels, and brief reasons. To enhance stability, we employ an improved prompt and consistency-driven confidence fusion to generate multiple JSON records with confidence-building rules. The generated JSON records further enter the Attribution & Aggregation layer, which is used to cluster negative feedback, merge synonymous expressions, and generate traceable query summaries and priority signals. On a corpus containing 2,062 Chinese samples, the 7B open-source model qwen1.5-7b-chat achieved an accuracy rate of 94.7%, outperforming supervised fine-tuning at 93.1% and direct classification at 92.7%, and approaching qwen-plus at 95.7%. In a case study focusing on the Volkswagen Magotan, this method compressed 137 negative comments into traceable and sortable problem clusters. The code and data are available at https://github.com/ppg94/Implicit-Sentiment-Joint-Extraction-Automotive.
Chang, Gengjia, Deng, Zuxing, Ma, Aonan, Yao, Jiangqi, Li, Xiaojian, Li, Ling
Amplitude-Dependent Analysis of Whirl Flutter Test Data of the Maryland Tiltrotor RigF-0082-2026-00595/5/2026
This paper investigates amplitude effects in the aeroelastic damping and frequency characteristics of the Maryland Tiltrotor Rig across four configurations: gimballed or hingeless hubs, each paired with straight or swept-tip blades. The recovery rate method is used to identify the aeroelastic parameters of the primary modes dominated by out-of-plane and in-plane wing bending from experimental free-decay strain time histories, capturing variations in dynamic behavior with the response amplitude. Results from conventional methods that assume linear (amplitude-independent) behavior are also presented for comparison. The local damping ratio of the examined modes generally decreases with increasing strain amplitude across all configurations, a trend missed by conventional linear estimation methods. The strength of amplitude effects varies as the system approaches instability: for gimballed configurations, they weaken near instability; for hingeless configurations, they become more pronounced. While the local frequency of the mode dominated by out-of-plane wing bending remains relatively constant with strain amplitude, the frequency of the mode dominated by in-plane wing bending displays significant amplitude-dependent shifts, particularly for hingeless hubs. The findings demonstrate the importance of accounting for nonlinear effects in aeroelastic parameter identification based on experimental tiltrotor data and provide insights into tiltrotor nonlinear dynamics.
Simmons, Gray, Riso, Cristina
Forward Flight Dynamic Inflow Model Identification from Experimental Wake MeasurementsF-0082-2026-00615/5/2026
Wake measurements were performed on a 2-m diameter rotor in forward flight at an advance ratio of 0.2 undergoing sinusoidal collective and cyclic inputs using 2D-3C phase-resolved PIV. Input frequencies of 0.05/rev, 0.1/rev, 0.2/rev, and 0.4/rev were tested. The goal of this study was to characterize the time-varying rotor wake and extract Pitt-Peters dynamic inflow model parameters for the lateral cyclic inflow state. In both input cases, the effects of the pitch inputs manifested as modulation of the local upwash and downwash of the trailing tip vortices near the tip path plane. It was found that additional azimuthal measurements are necessary to improve the extracted value of the steady Pitt-Peters term. However, the extracted mass term was within 12% of the Pitt-Peters value, demonstrating the ability of the presented analysis to resolve the dynamics of the wake with a limited number of azimuthal measurements.
Yu, Daniel, Sirohi, Jayant
Synthetic Rubber Hot Oil Resistant, High Swell 45 - 55AMS3222J (Current)4/28/2026
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
Investigation on Microstructure and Mechanical Properties in the Dissimilar Joining of WE43 to AA7075 Alloy using FSW2026-01-02294/7/2026
This research investigates the alterations in microstructure, microhardness, and joint strength resulting from the dissimilar friction stir welding (FSW) of WE43 magnesium alloy to AA7075 aluminium alloy. The study specifically analyses the role of FSW process parameters in the formation of intermetallic compounds (IMCs), the evolution of grain structure, the resultant microhardness distribution across the weld zone, and the joint tensile strength. A comprehensive microstructural characterization was performed utilizing optical microscopy (OM), field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDS), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD). These analyses confirmed significant grain refinement in the stir zone and the identification of various IMCs at the weld interface. Microhardness mapping indicated a gradient profile, with the weld nugget exhibiting superior hardness attributed to its dynamically recrystallized, fine-grained microstructure. Crucially, the low-heat-input FSW (LFSW) variant yielded a substantial increase in average microhardness, reaching 126 HV in the stir zone (SZ), due to grain refinement induced by severe plastic deformation. This configuration achieved a joint efficiency of approximately 68.7% relative to the WE43 base material. The enhancement in mechanical performance is directly linked to a modified joint preparation strategy that successfully suppressed the formation of brittle AlMg IMCs, instead fostering the formation of harder MgZn, Al2CuMg, and AlMgZn compounds. These findings underscore the efficacy of the LFSW technique in fabricating dissimilar WE43-AA7075 joints with favourable mechanical properties and a consistent microhardness profile. The process parameters are strategically selected to achieve better joint properties and form defect-free joints.
Ahmad, Tariq, Khan, Noor Zaman, Ahmad, Babar, Siddiquee, Arshad Noor
Advanced Wheel Attachment Loosening Identification through Algorithmic Gear Error Analysis and Probabilistic Assessment2026-01-05904/7/2026
At present, tire failures directly affect road safety, and the number of incidents caused by them is gradually increasing. Examining wheel attachment loosening on time is vital for vehicle safety. Tire-related incidents not only put people in peril but also have a detrimental effect on the economy. Therefore, the goal of this research is to develop a new and effective method for identifying wheel attachment loosening. A novel gear error reduction approach, distinct from traditional methods, combines advanced computing and probabilistic analysis. This paper involves three key components: extracting looseness eigenvalues, calculating ring gear errors, and computing the tire loosen probabilities. Gear errors derived from the Kalman filter and adjusted for speed, eigenvalues were calculated, and a tire loosening probability analysis was performed. Real-car trials across speeds and roads confirm its accuracy and reliability. This technology can improve automotive safety and maintenance, reducing accidents, claims, and pollution. It also fits autonomous and smart cars, where tire monitoring is key.
Liu, Jianjian, Zhang, Zhijie, Wang, Zhenfeng, Ma, Guangtao, Shi, Meijuan, Liu, Jing, Zhao, Binggen, Lu, Yukun
Agile Safety Case Assessments for Autonomous Vehicle Fleets2026-01-05214/7/2026
Rapidly upcoming deployment of autonomous vehicles (AVs), including robotaxis and trucks, has intensified the need for rigorous safety assessment of complex AI-driven systems. While considerable effort has been invested in constructing safety cases for AVs, systematic approaches for evaluating these safety cases remain underdeveloped. This paper presents a three-stage methodology for assessing AV safety cases. A process for assessing argumentation is presented that involves traceability to pre-reviewed and peer-reviewed safety cases such as the Open Autonomy Safety Case (OASC). Next, we present a structured process for evaluating the quality of evidence supporting these arguments. We applied this methodology to evaluate safety cases from multiple AV developers, enabling iterative refinement throughout the development lifecycle. Our agile approach supports efficient assessments by establishing clear traceability to industry standards and enabling early identification of potential gaps. This work provides regulators, operators, and developers with a practical framework for systematically evaluating AV safety cases and identifies lessons learned and areas for continued improvement.
Wagner, Michael
Sideslip Angle Estimation for Mining Trucks Based on Real-Time Cornering Stiffness Identification2026-01-06184/7/2026
High-precision estimation of key vehicle–road state parameters is crucial for ensuring the accurate and safe control of mining trucks (MT), as well as for reliable trajectory tracking. Among these parameters, the vehicle sideslip angle is particularly critical for assessing and predicting lateral stability. However, its direct measurement is challenging, and its estimation typically depends on an accurate characterization of tire cornering stiffness. For MT, large variations in loading conditions (from empty to fully loaded) pose significant challenges to sideslip angle estimation due to the resulting nonlinearity and variability of tire cornering stiffness. To address this issue, a novel joint estimation framework integrating the Moving Horizon Estimation (MHE) and Square-Root Cubature Kalman Filter (SCKF) is proposed to simultaneously achieve high-precision estimation of both tire cornering stiffness for each tire and vehicle sideslip angle. In this framework, the cornering stiffness of the front, middle, and rear axles is identified and updated in real time using MHE through a forgetting-factor least squares method based on yaw rate and lateral acceleration data within a fixed-length time window. The updated stiffness is then incorporated into the SCKF for accurate estimation of the sideslip angle. This sequential process effectively establishes a coupling between the estimation of the two parameters, forming an integrated joint estimation mechanism. The proposed framework is validated on the TruckSim–Simulink co-simulation platform, and the results confirm its superior accuracy and robustness, demonstrating its potential to improve the safety and control performance of MT.
Xia, Xue, Shen, Peihong, Jiao, Leqi, Li, Tao, Chen, Huiyong, Zhao, Kun, Jiao, Leqi, Zhao, Zhiguo
Development and Application of SAE J3260 & J3290 Recommended Practice for Minimizing Spread of Infections in Motor Vehicles2026-01-01284/7/2026
The onset of the COVID-19 pandemic in early 2020 introduced an unprecedented disruption to global industries, including automotive service and maintenance. As technicians and service shops struggled to balance operational continuity with safety, uncertainty surrounded best practices for servicing potentially dangerous vehicle cabins and air conditioning systems. This paper traces the evolution of these early efforts, from initial confusion and informal guidance to the establishment of the SAE Cabin Disinfection Practices Committee (SAE TEVCDPC) and the eventual publication of SAE J3260 and SAE J3290. It also considers work done by ASHRAE (the American Society of Heating, Refrigerating and Air-Conditioning Engineers), which simultaneously worked on ASHRAE Standard 62.1 and 241. These standards, along with contributions from subject matter experts, formalized the automotive industry’s response to infection control in vehicle environments, integrating scientific understanding with practical service protocols.
Schaeber, Steven, Mathur, Gursaran, Taylor, Dwayne
CONNECTOR, RECEPTACLE, EXTERNAL ELECTRIC POWER, AIRCRAFT, 270 VDC, 90 KWAS81790/1 (Current)2/23/2026
AE-8C1 Connectors Committee
Aircraft Tow BarARP1915E (Current)2/12/2026
This SAE Aerospace Recommended Practice (ARP) specifies dimensional and physical requirements of tow bar connections to tractor and aircraft (see Figure 1). It is applicable to all types of commercial transport category aircraft tow bar. The purpose of this SAE Aerospace Recommended Practice (ARP) is to standardize tow bar attachments to airplane and tractor according to the mass category of the towed aircraft, so that one tow bar head with different shear levels can be used for all aircraft that are within the same mass category and are manufactured in compliance with AS1614 or ISO 8267.
AGE-3 Aircraft Ground Support Equipment Committee
Dynamic Stability Enhancement during Emergency Obstacle Avoidance under Inappropriate Driver Operation10-10-02-00152/2/2026
In response to the decline in vehicle stability and the resulting safety risks caused by inappropriate driver operations during high-speed emergency obstacle avoidance, a human–machine cooperative control strategy based on driver operation recognition is proposed. The strategy establishes a vehicle controllability boundary by integrating real-time driver inputs with tire adhesion limits, enabling dynamic evaluation of the influence of operations on system controllability and identification of potential inappropriate operations. On this basis, a control authority allocation mechanism is developed, capable of adaptively adjusting to vehicle states and driver operations. By combining road boundary constraints with vehicle stability envelope constraints, the strategy dynamically regulates the steering angle, ensuring vehicle stability while retaining the driver’s effective intentions as much as possible. Unlike conventional path-tracking or single-envelope control approaches, the proposed method achieves early identification and proactive mitigation of instability risks induced by inappropriate driver operations, thereby reducing associated safety hazards. To validate the effectiveness of the strategy, two representative scenarios, double lane change and curve avoidance, were designed. Simulation and driver-in-the-loop experiments demonstrate superior performance in terms of vehicle stability, human–machine cooperation, and safety, achieving a higher level of coordinated control and performance balance. The findings provide new insights into the design of human–machine cooperative control strategies under extreme conditions, contributing to enhanced fault tolerance of intelligent driving systems against inappropriate driver operations and improved driving safety.
Liu, Yangyi, Zhou, Bing, Wu, Xiaojian, Jiang, Xiaokun, Cui, Qingjia
Aerospace Size Standard for O-RingsAS568F (Current)1/29/2026
This SAE Aerospace Standard (AS) specifies the inside diameters, cross-sections, tolerances, and size identification codes (dash numbers) for O-rings used in sealing applications and for straight thread tube fitting boss gaskets. The dimensions and tolerances specified in this standard are suitable for any elastomeric material provided that suitable tooling is available.
A-6C2 Seals Committee
Early Prediction of Relaxation Voltage & Detection of State of Battery for Functional Safety2026-26-01661/16/2026
During parking conditions of vehicles, the state of the battery is uncertain as it goes through the relaxation process. In such scenarios, the battery voltage may exceed the functional safety limits. If we cross the functional safety limits, it is hazardous to the driver as well as the occupant. In this case, relaxed voltage plays a crucial role in identifying the safe state of the battery. To estimate the relaxed cell voltage there are methods such as RC filter time constat modeling and relaxation voltage error method. The problem with these solutions is the waiting time and accuracy to determine the relaxation voltage. In this manuscript, a solution is proposed which ensures the above problem is reduced. To achieve the reduction of relaxation voltage estimation time, a python sparse identification of nonlinear dynamics (PySindy) is used which identifies and fits an equation model based on observing the battery characteristics at different SOC and temperatures. The implementation is done and compared with the existing algorithms at different temperature and SOC levels. It is validated that the manuscript predicts relaxation voltage within 1s having Mean Squared Error (MSE) of 0.04mV. In the existing method, it takes minimum 30 seconds of data to estimate relaxation voltage having a mean absolute error of 2.99mV. As a conclusion, manuscript being efficient and accurate to predict the relaxed voltage (OCV) which enhances the estimation of state of battery for functional safety aspects.
Pandey, Priyanshu, Nilajkar, Ankur, Panda, Abinash
Vehicle Interior Material Emissions and Contributions in Passenger Vehicle Cabin Air Quality2026-26-02871/16/2026
Polymer compounds used in the manufacturing of automotive interiors are traditionally consist of polymer virgin material, elastomers, additives, pigments, fillers. These compounded polymers are prone to the emission of low molecular weight chemicals over a period of usage and exposure to the environment called volatile organic compounds (VOCs) and carbonyl compounds. These released VOCs and carbonyl compounds consist of chemicals like benzene, toluene, xylene, styrene, acetaldehyde, formaldehyde, acrolein etc. Short term or long-term exposure of these chemicals have adverse health effects like nausea, headache, vomiting, cancer, even death of personnel if found beyond the permissible limits. It has been observed that the majority of passenger have the above symptoms whenever travelled using passenger cars within few minutes of boarding and exchange the car cabin air. The study was planned to understand the reasons for the concerns and further resolution. This paper is focused on the identification of parts and material, evaluation against the recommended tests like Odor, Fogging, VOC, and Carbonyl compound emissions and development of materials used inside the car cabin. This study further provides the acceptance limit of VOCs and carbonyl compound emissions inside the passenger vehicle cabin to address the adverse personnel health concern.
Shukla, Sandeep Kumar, Balaji, K V, Varatharajan, Senthilkumaran
Comparative Analysis of Charger Identification Methods in Electric Truck Routing Algorithms2026-26-02061/16/2026
Optimizing Vehicle Routing is a key application for determining the most effective sequence of locations in electric trucks. This optimization not only enhances operational efficiency but also minimizes energy consumption and reduces overall costs. A critical aspect of Optimal Vehicle Routing is identifying charging stations along the route, particularly for electric vehicles with specific range requirements. The availability of these charging stations is crucial for maintaining the continuity of operations and preventing delays. This paper explores multiple methods for charger identification, simulating and comparing their effectiveness. The primary parameter for comparison are the energy consumption, throughput, and the energy efficiency of the routes generated by various methods, which directly impacts the feasibility of real-time applications in logistics. The results of this study provide insights into the efficiency of different charger identification methods within the Optimal Vehicle Routing framework. By analyzing energy consumption, throughput and efficiency, the paper highlights the most effective strategies for integrating charger identification into Optimal Vehicle Routing algorithms, contributing to more reliable and efficient logistics operations.
Bhat, Adithya, Prasad P, Shilpa, Kolakar, Rakshita, Myers, Michael, Klein, Fischer, Shrivastava, Himanshu
Virtual Reality in Assembly Simulations: Outpacing Tecnomatix Process Simulate for Faster Time-To-Market2026-26-03981/16/2026
Virtual Reality technology is emerging as a transformative solution in the manufacturing industry. It offers significant advantages over traditional tools like Tecnomatix Process Simulate in assembly & ergonomic simulations. Analysis using PS is time-consuming and lacks real-time human interaction as it relies on detailed modelling and sequential workflows, which will delay the identification of assembly no-build conditions and ergonomic issues. This paper evaluates the time and the cost-saving potential of VR in assembly processes and explores its role in minimizing the need for physical prototypes across various stages of vehicle development. VR provides interactive environments, enabling interaction with 3D models and real-time collaboration with various teams across the globe. This leads to faster identification of assembly process flaws, quicker iteration cycles, and a reduced need for physical prototypes in the station development process for the lines. VR allows individuals to experience realistic simulations of assembly processes with multiple scenarios, without the risk of real-world safety consequences. This simulation approach through VR technology facilitates real-time ergonomic predictions, quick and accurate simulation of various assembly scenarios during the station development process before the production with minimal iterations which will ensure the assembly processes are getting optimized in the early stages of product development. It proves to be a superior alternative for validating assembly feasibility, reducing time in process sequence building, and achieving faster time to market in manufacturing. By minimizing iterations in physical prototyping and extensive validation and testing of assembly processes, VR significantly impacts time & cost.
Nagendran, Rakesh Kumar
Road Noise Improvement of a Passenger Vehicle through End-to-End CAE-Simulation Using Transfer Path Analysis (TPA) Approach2026-26-04311/16/2026
Refined NVH performance of a vehicle is a mark of premium quality. Achieving the desired NVH performance in different vehicle operating conditions is always a Herculean task and early stage “CAE design recommendations” play crucial role in overall vehicle design development. This becomes tougher when the program is very much cost, weight and timeline sensitive. This paper explores simulation approach for addressing a major noise issue for a vehicle running at a constant speed on a rough road. While working on any issue, the first and the most critical step is to identify the exact root cause of the issue. Hence, we propose a detailed full vehicle level “contribution analysis (CA) + transfer path analysis (TPA)” methodology (everything done through the simulation) and then go for the design recommendations to improve the performance. We used road excitation power spectral density (PSD) as the input at all the four wheels (spindle locations) calculated through MBD software. The first step i.e. contribution analysis, pointed out the dominant spindle location (out of 4 wheel-spindles) and the direction of the excitation. The second step i.e. TPA, gave the exact attachment point on the BIW with direction through which forces will be passed on to the vehicle cabin. The operational deflection shape (ODS) based on above root cause identification highlighted the weak design zone. With proposed design modifications the critical noise was reduced significantly to meet target performance level. In summary, given correct inputs, CA + TPA approach at full vehicle (FV) level in CAE simulations is very effective approach to track down any issue. This methodology can be extended to all the different CAE load cases (vehicle operating scenarios).
Mahajani, Mihir, Nascimento, Fabio, Adinarayana Reddy, Kodidela, Matyal, Mahantesh, Tenagi, Irappa, Sardar, Chenna
Mode Identification and Collaborative Switch for the Front-Centralized and Rear-Distributed Electric Drive Vehicle Based on BP Neural Network2025-01-733512/31/2025
Distributed-drive electric vehicles (DDEVs) significantly enhance off-road maneuverability but suffer from compromised high-speed stability and robustness. This research introduces a front-centralized and rear-distributed (FCRD) architecture that synergistically leverages the advantages of each configuration. The electric-drive-wheel (EDW) on the rear suspension can provide three working modes: (a) Drive-connected mode, (b) Drive-disconnected mode, (c) Brake mode. It is the key actuator for vehicle mode-switching, which supports the vehicle with three driving modes: (a) DDEV, (b) front-wheel drive (FWD), (c) all-wheel drive (AWD). A hierarchical control architecture employs the upper-layer controller with Back Propagation Neural Network (BPNN) for mode identification and decision-making. The lower-layer controller enables the intelligent torque distribution and collaborative control of the motors. The control strategy is pre-trained in the VCU (vehicle control unit) with off-line data annotations to achieve the maximal instantaneous system efficiency. At the same time, a cost function is designed to suppress unreasonable frequent mode switching that may deteriorate handling characteristics and ride comfort. The off-line data training results indicate that the overall accuracy of mode identification reaches 91.7%, of which the DDEV mode accuracy 98% and AWD mode accuracy 85%. Finally, the test vehicle with the calibrated EDW prototype is fully constructed and drives in Shanghai suburbs for a road test with parameters recorded throughout the whole cycle. The test vehicle has a balanced performance in power output, driving efficiency, and control robustness. The high-way cruising conditions activate the efficiency-oriented AWD mode of 82% overall propulsion system efficiency, which overcomes the problem of high-speed attenuation in electric vehicles and effectively improves the range at premise of safety and stability.
Ding, Xiaoyu, Chen, Xinbo, Wang, Wei, Zhang, Jiantao, Kong, Aijing
Ship Encounter Situation Recognition Based on Transformer2025-99-031512/17/2025
Intelligent ships represent a crucial trend in the development of the maritime industry and will become the predominant vessel form in the future. Intelligent ships must have efficient perception and situational analysis capabilities in complex navigation environments to achieve intelligent decision-making and safe navigation. Maritime traffic safety is a critical issue for the global shipping industry, and maritime situational awareness is essential for ensuring safe navigation in waterways. This paper addresses the problem of intelligent identification of potential navigational risks in ship navigation environments and proposes a Transformer-based approach for ship encounter situation recognition. This method utilizes Automatic Identification System (AIS) data to extract encounter features. Contextual Position Encoding and Coordinate Attention mechanisms are introduced into the model to capture spatial correlation and directional features, enhancing the accuracy of determining encounter situations. Through experimentation in the research area and comparison with other models, the results demonstrate that the proposed model achieves higher recognition accuracy in ship encounter situation identification. This research provides a beneficial new idea and technical approach for studying intelligent environmental perception and situational analysis for ships.
Ma, Tongyue, Pan, Mingyang, Li, Shaoxi, Hu, Jingfeng, Li, Chao
Investigation of Roll Motion Behavior in a Multi-Functional High-Speed Trimaran Unmanned Vessel2025-99-043912/10/2025
This study focuses on the multifunctional three-body high-speed unmanned boat model, and experimentally measures the roll attenuation characteristics under different draft conditions. It focuses on the influence of the initial roll angle on roll attenuation, and analyzes the change pattern of roll angle over time. Experimental results show that the model shows obvious self-oscillation period and amplitude attenuation. Based on the system identification theory and combined with improved genetic algorithms, a mathematical model used to simulate the roll attenuation motion of the boat model was constructed. The difference between experimental data and fitted values was further evaluated using identification software and verified with data at specific roll angles. In addition, the study also deeply analyzed the change trend of the roll moment coefficient with the initial roll angle. By comparing the experimental results of the three-mall boat and the catamaran, it was found that the three-mall boats were better than the catamaran in terms of roll resistance. These research results not only provide an important basis for the research on wave resistance of multi-body boat models, but also promote the technological progress of multi-body boats in wave resistance.
Zhang, Di, Tong, Wei, Yu, Qingzhu, Liu, Bofei
Risk Assessment and Identification of Freight Vehicle Trip Chains2025-99-042112/10/2025
Although the number of trucks is low, their accident rate is high, and the consequences of accidents are severe. This paper is based on GPS data from 100 trucks, with each trip chain defined by a vehicle’s stay time greater than 20 minutes. The kinematic parameters for each trip chain are then extracted, and the entropy weight method is used to calculate the weights of various parameters. A random forest model is applied to select 11 key indicators, including speed and acceleration. The entropy weight-TOPSIS algorithm is used to assess the risk of each trip chain for the trucks. Different combinations of continuous and discontinuous trip chain scenarios are constructed. Finally, support vector machines (SVM) and decision tree methods are used for risk prediction under different trip chain combinations. The results show that the 11 selected key indicators provide an accuracy of 95.74% for describing the sample. In general, the SVM model shows better prediction accuracy than the decision tree under different trip chain combinations, though the decision tree results fluctuate significantly. As the penalty parameter in SVM and the minimum leaf node in the decision tree increase, the accuracy of the model gradually decreases.
Huang, Yunhe, Xiong, Zhihua, Li, Jiayu
Identification of Critical Risk Nodes and Vulnerability Analysis in Railway Networks2025-99-042512/10/2025
As the importance of railway networks in regional transportation and economic development continues to grow, identifying critical risk nodes and assessing network vulnerability is crucial for enhancing the stability and resilience of railway systems. This study focuses on the railway network of Shandong Province, constructing a topological model to systematically analyze the structural characteristics of the network, with a particular emphasis on key nodes. To identify these critical risk nodes, four modified weighted indicators were employed, combined with the mean-square deviation TOPSIS method to quantify node importance. The analysis identified Jinan, Linyi, and Yantai as key risk nodes, as they consistently ranked high across multiple indicators. Further vulnerability analysis reveals that the failure of these critical nodes would lead to significant declines in network efficiency and connectivity, with particularly high vulnerability observed when nodes with high weighted betweenness centrality and PageRank values fail.
Xu, Chang, Han, Wen, Fan, Hongxian, Dai, Hongna
Machine Learning Based off-Road Vehicle Turn Identification Using Vehicle & GPS Parameters2025-28-034411/6/2025
Identification of different types of turns during field operation of off-road vehicles is critical in the overall vehicle development as it is helpful in identifying & optimizing machine performance, correct duty cycle, fuel economy, stability analysis, accurate path planning, customer usage pattern & designing the critical components, etc. In this study, a machine learning (ML) based methodology has been developed to detect the off-road vehicle turns using vehicle & GPS parameters. Three most common types of off-road vehicles turn conditions e.g., Straight line, Bulb turn, and Three-Point turn have been considered. Different vehicle parameters (like latitude & longitude, compass bearing, yaw rate, vehicle speed, swash plate angle, engine speed, percent load at vehicle speed, raise lower front & PTO channels) generated during field test have been used here. These vehicle parameters are further processed, analysed and used in ML learning model building. Four ML models e.g., SVM, K-NN, Gaussian Naïve Byes and Random Forest have been used here. Experimental results show that the present ML based methodology can identify most common vehicle turns considered in this study with a good accuracy.
Rai, Rohit, Gangsar, Purushottam, Joseph, Roberts, Malik, Manish, Dutta, Mausum, Fapal, Anand
Customer Support – Failure and Warranty Analysis in Off-Highway Vehicles2025-28-030011/6/2025
The reliability and durability of off-highway vehicles are crucial for industries like construction, mining, and agriculture. Failures in such machines not only disrupt operations but can also lead to significant economic losses and safety concerns. Effective failure and warranty analysis processes are essential to improve customer support, minimize downtime, and enhance equipment life cycle. This paper outlines a comprehensive 7-step failure analysis methodology tailored for off-highway vehicles, accompanied by warranty analysis using Weibull, 6MIS, and 12MIS IPTV. It details the process from problem identification through permanent solution implementation, emphasizing tools and techniques necessary for sustainable improvements. The structured approach provides an actionable blueprint for OEMs and service teams to enhance customer satisfaction, support sustainable development goals, and maintain regulatory compliance.
Mulla, Tosif, Thakur, Anil, Tripathi, Ashish
Rubber: Fluorosilicone (FVMQ) High Temperature Fuel and Oil Resistant For Seals In Fuel Systems and Specific Engine Oil SystemsAMS7273D (Current)10/24/2025
This specification covers a fluorosilicone (FVMQ) rubber in the form of molded rings.
AMS CE Elastomers Committee
Evaluation and Analysis of the Current Situation of Security Identification Management Based on Structural Equation Model2025-99-005210/17/2025
In order to explore the actual safety management effect of safety signs and better carry out on-site safety management, this article independently developed an evaluation scale for the management effect of safety signs. Taking a certain marine engineering equipment manufacturing enterprise as the object, the management of safety signs was evaluated and analyzed. Firstly, 11 questions from the SPSSAU online analysis scale were selected as measurement indicators to test safety label management. Factor analysis was used to select three factors: cognitive function, compliance behavior, and leadership attitude. Secondly, a safety identification management model was constructed based on structural equation modeling (SEM) with three factors as latent variable factors. Through fitting tests, it was found that cognitive effects, compliance behaviors, and leadership attitudes have a certain impact on management effectiveness, and there is a positive correlation between the three latent variable factors. Finally, taking the marine engineering equipment manufacturing enterprise as an example, the score of the enterprise’s safety label management effectiveness was analyzed. It was found that the enterprise had good safety label management, which was consistent with the on-site evaluation of experts. The results can provide reference for the safety label management of related enterprises.
Wang, Chunyuan, Yang, Guihua, Li, Xinyao, Zhu, Jie
No More Lost Sponges: RFID to Optimize Surgical Suite Efficiency and SafetyTBMG-5398910/1/2025
Retained surgical items are not as rare as many believe. While stories of sponges left inside patients occasionally make headlines, few realize the actual frequency: according to a systematic review of 21 studies by the Agency for Healthcare Research and Quality (AHRQ), these and other small items are left behind as often as 1.3 times per 10,000 surgical procedures.
Marking of Electrical Insulating MaterialsAS5942 (Current)9/29/2025
This specification establishes the performance requirements for the identification of wire and cable by indirect markings that have been applied to electrical insulating materials including heat shrink sleeving, wrap around labels and “tie-on” tags as well as any other types of materials used for indirect marking. This specification covers the processes used to mark these materials, including impact ink marking, thermal transfer, hot stamp, and lasers, etc. This specification does not cover the direct marking on insulated electrical wires and cables.
AE-8A Elec Wiring and Fiber Optic Interconnect Sys Install
Tungsten Carbide-Cobalt PowderAMS7879G (Current)9/18/2025
This specification covers tungsten carbide-cobalt in the form of powder.
AMS F Corrosion and Heat Resistant Alloys Committee
Enhanced Object Matching and Identification for Multi-Robot Systems in Complex Environments2025-01-04389/16/2025
Our research focuses on developing a novel loss function that significantly improves object matching accuracy in multi-robot systems, a critical capability for Safety, Security, and Rescue Robotics (SSRR) applications. By enhancing the consistency and reliability of object identification across multiple viewpoints, our approach ensures a comprehensive understanding of environments with complex layouts and interlinked infrastructure components. We utilize ZED 2i cameras to capture diverse scenarios, demonstrating that our proposed loss function, inspired by the DETR framework, outperforms traditional methods in both accuracy and efficiency. The function’s ability to adapt to dynamic and high-risk environments, such as disaster response and critical infrastructure inspection, is further validated through extensive experiments, showing superior performance in real-time decision-making and operational effectiveness. This work not only advances the state of the art in SSRR but also addresses the practical needs of end-users, providing a more robust tool for mission-critical operations.
Brown, Taylor J., Vincent, Grace, Nakamoto, Kyle, Bhattacharya, Sambit
Cable Marking for Circuit Identification in Multi-Voltage Power Distribution SystemsJ3176_202509 (Current)9/3/2025
This SAE Standard establishes the minimum circuit identification and requirements for Multi-Voltage Power Distribution Systems (MVPDS) for use on trucks and buses. A Multi-Voltage Power Distribution System is one that distributes two or three voltages, up to 60 VDC, to power the controls, instruments, and devices.
Truck and Bus Electrical Systems Committee
Identification Welding Wire, Tab Marking MethodAMS2816W (Current)7/21/2025
This specification covers procedures for tab marking of bare welding wire to provide positive identification of cut lengths and spools.
AMS B Finishes Processes and Fluids Committee
O-Ring Molded from AMS7274 MaterialAS3570A (Current)7/17/2025
This standard establishes the dimensional and visual quality requirements, lot requirements, and packaging and labeling requirements for O-rings molded from AMS7274 rubber. It shall be used for procurement purposes.
A-6C2 Seals Committee
SCREW - OVAL FILLISTER DRILLED HEAD, .138-32AS116961D (Current)6/17/2025
E-25 General Standards for Aerospace and Propulsion Systems
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