Browse Topic: Quality management systems

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Leveraging Domestic Automotive Software Development Best Practices for MOSA and GCIA/Victory Implementation2026-01-75389/22/2026
ISO 26262ISO/SAE 21434ISTQB/ASPICEMOSAGCIAVICTORY
Priemer, Douglas, Sime, Karl
Counterfeit Avoidance and Quality Assurance: Supply Chain Screening of Cots Microelectronics in Tactical System2026-01-75239/22/2026
This paper details the successful scaling demonstration of a comprehensive supply chain screening process for commercial off-the-shelf (COTS) motherboard subassemblies used in tactical servers for naval applications. Our approach leverages Power Fingerprinting (PFP) technology, which uses unintended analog emissions and machine learning to provide independent, non-destructive, and scalable integrity assessment of microelectronics. The primary goal of the effort was to demonstrate the effectiveness and scalability of the PFP screening process without disrupting or delaying the manufacturing workflow. The screening successfully detected hardware and firmware modifications and identified two cases of abnormal behavior: unusual BIOS power reset and elevated CPU sensor readings on two motherboard subassemblies. Following our quality control forensic analysis, we determined the root cause of these anomalies and their potential impact on the host platform.
Aguayo Gonzalez, Carlos R., Roberson, Ken
A Study on the Development of Brake Moan Analysis for CTBA Vehicles Using Simulation2026-01-08149/14/2026
Moan noise is a low-frequency noise occurring in the 170–500 Hz frequency ranges. While it frequently appears in vehicles equipped with a rear Coupled Torsion Beam Axle (CTBA), the exact cause, generation mechanism and clear solutions remain unidentified. For those reasons, we have developed a moan noise analysis method capable of representing the moan noise phenomenon in vehicles with rear CTBA along with an automation tool. From these results, we can use moan analysis models to reduce real moan noise problems. Consequently, this not only enhances customer satisfaction and vehicle quality but also significantly increases the work efficiency of vehicle designers through design modification in the preliminary stages of vehicle development
Kim, Sungho, Kim, Jeongkyu, Hwang, Jaekeun, Kang, Donghoon
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-08059/14/2026
Teardown evaluation of chassis system components plays a critical role in benchmarking, failure analysis, and competitive product assessment. These inspections rely heavily on experienced engineers who interpret visual defect patterns, material conditions, wear signatures, and manufacturing variations. However, expert driven evaluation processes are often subjective, difficult to standardize, and challenging to scale across global engineering teams. This paper presents a structured AI-assisted expert evaluation framework developed to enhance consistency, preserve institutional knowledge, and enable continuous improvement in chassis component teardown analysis. The proposed system integrates convolutional neural network architectures, including ResNet18 and its variants, into a human in loop inspection workflow. AI models perform initial classification of component images (e.g., OK/not OK and defect subclasses) and provide associated confidence scores. These predictions are presented as decision support, while final authority remains with the evaluating expert. Experts can confirm or override AI outputs, annotate defect regions using bounding boxes, assign subclass categories, and provide structured technical comments. All expert interactions, including AI disagreements, are systematically recorded. Correction instances are analyzed to identify model limitations, ambiguous defect conditions, and data gaps. Expert validated evaluations are incorporated into the training dataset to enable iterative model refinement. This closed loop process supports progressive improvements in model robustness and classification accuracy across varying teardown conditions and component types. A centralized cloud-based repository maintains full traceability of inspections, including timestamps, AI confidence levels, expert modifications, and annotation metadata. This structured knowledge capture converts tacit engineering judgment into a persistent digital asset, supporting auditability, cross-site alignment, and accelerated onboarding of new engineers. The framework demonstrates how AI can be effectively deployed as an assistive technology in chassis teardown evaluation, improving repeatability, enhancing data driven benchmarking, and enabling scalable knowledge preservation without displacing expert authority.
Divakaruni, Saikiran, Vaibhav, Veer, Hansen, Scott, Hood, Trevor, Agrawal, Rahul
Safety Analysis of Collision Avoidance for Semi-Autonomous Navigation Ship Based on STPA-CREAM2026-99-15549/11/2026
With the continuous improvement of ship intelligence, more intelligent onboard navigation equipment and intelligent navigation systems are used to assist in improving navigation efficiency. This study takes semi-autonomous navigation ships as research objects and adopts System-Theoretic Process Analysis (STPA) to model the complex interaction relationships of semi-autonomous navigation encounter scenarios and identify and analyze potential risks. To address the deficiency of STPA in human factors analysis capability, the Cognitive Reliability and Error Analysis Method (CREAM) is introduced to analyze human factors in semi-autonomous navigation. Finally, based on the results of the STPA-CREAM analysis, recommendations are provided to improve the safety of semi-autonomous navigation.
Zhang, Xiaojie
Conceptual Design Method Study of a New Frame Structure for Electric Heavy-Duty Trucks2026-99-11099/11/2026
To solve the multiple conflicts between user requirements and structural constraints in the new frame structure design of electric heavy-duty trucks, this research proposes a conceptual design methodology that integrates modular design theory, quality function deployment, and TRIZ theory. Firstly, the quality function deployment approach is employed to construct the requirement- technology priority matrix, translating the system of user requirements for electric heavy-duty trucks into specific engineering characteristics. Supported by modular design theory, foundation reusable modules and specialized modules requiring optimization in traditional heavy truck chassis are identified. Then, a three- dimensional requirements-space-technology heat map accurately reveals key design collisions. Furthermore, TRIZ theory is applied to extract relevant engineering parameters and leverage the contradiction matrix for rapid acquisition of innovative solutions, thereby standardizing the conceptual design process, optimizing its implementation, and improving design efficiency during product development.
Sun, Lei, Huang, Wei, Zhu, Baoli, Jin, Zhenye, Guo, Shouwu, Feng, Yu, Chen, Xianlong
Enhancing Laser Processing Head Service and Quality Assurance: How Advanced Laser Diagnostics Transform Service Operations and Product Development for High-Power Laser Applications26AERP09_029/1/2026
In the rapidly evolving arena of high-power laser technology, precision and reliability are becoming increasingly important. The increasing powers in laser applications into the 10's of kW and, in some cases, 100's of kW, present additional challenges when applied to internal laser system components. For Precitec, respected globally for their advanced laser processing heads, delivering consistent performance to customers is both a challenge and a necessity. This case study explores how Precitec's U.S. operation has integrated laser measurement technology into its workflow; a non-contact beam characterization system significantly elevates the company's service, quality assurance, and development processes for high-power laser applications.
Composite Materials Handbook Volume 5 - Revision BR-4608/27/2026
The fifth volume of this six-volume compendium publishes technical guidance and properties on ceramic matrix composite material systems. The selected guidance on technical topics related to this class of composites includes material selection, processing, characterization, testing, data reduction, design, analysis, quality control, application, case histories, and lessons learned of typical ceramic matrix composite materials. Volume 5, which covers ceramic matrix composites, supersedes MIL-HDBK-17-5 of June 17, 2002. The Composite Materials Handbook, referred to by industry groups as CMH-17, is an engineering reference tool that contains over 1,000 records of the latest test data for polymer matrix, metal matrix, ceramic matrix, and structural sandwich composites. CMH-17 provides information and guidance necessary to design and fabricate end items from composite materials. It includes properties of composite materials that meet specific data requirements as well as guidelines for design, analysis, material selection, manufacturing, quality control, and repair. The primary purpose of the handbook is to standardize engineering methodologies related to testing, data reduction, and reporting of property data for current and emerging composite materials. It is used by engineers worldwide in designing and fabricating products made from composite materials.
CMH17, CMH-17
Optimization and Control of Mechanical Testing for Batch-Produced Manned Spacecraft2026-99-03128/14/2026
To safely, efficiently, and high-quality complete the mechanical testing of batch-produced manned spacecraft during the China Space Station (CSS) phase, a series of optimization measures were proposed based on system engineering principles. These measures cover the entire mechanical testing process from preparation to implementation, including: establishing a standardized mechanical testing documentation system; reducing the number of mechanical sensors that do not affect result evaluation; pre-identifying and measuring background noise; digitizing test notching and evaluation methods; and standardizing and automating testing procedures. Additionally, targeted measures for test safety and quality control were implemented, including regular inspections of reusable spacecraft components, strict control of test hazards and operational risks, and standardized management of ground support equipment (GSE) through regular inspections. The proposed optimization and control measures have been validated through applications in batch-produced manned spacecraft during the CSS phase. The results show that: the generalization rate of mechanical testing documentation exceeds 80%; the number of mechanical sensors has been reduced by more than 10%; the test preparation period has been shortened by over 4 days; test efficiency has been improved by 30%; the single-direction test duration has been reduced by more than 50%; and the total test cycle has been shortened by 25%. These results indicate that the proposed optimization and control measures are reasonable and feasible, which effectively reduces redundant test operations and items, lowers potential test risks, improves test efficiency, shortens the overall test cycle, enhances test safety, and ensures the high-quality completion of mechanical testing for batch-produced manned spacecraft.
Peng, Huakang, Wang, Mengchen
Modeling Method of Aircraft Hydraulic Pipeline Cleaning Test Equipment Based on MBSE2026-99-03318/14/2026
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
Zhang, Yuxin, Ma, Zichen, Li, Qi, Song, Guoqiu, Li, Haiwei, Zhang, Jingjing
Discussion on Methods for Studying Material Fatigue Life: Taking Aluminum Alloy as An Example2026-99-01217/31/2026
Fatigue design is a key common quality technology for improving the quality control capability of China’s automotive products. The fatigue of materials is a multi-scale damage evolution process. Characterizing and processing the large number of three-dimensional defects inside the material, which have different shapes and distributions, and predicting the material’s lifespan based on the cross-scale damage evolution mechanism, is one of the key technologies for fatigue optimization design. This paper discusses the research methods for the fatigue life of aluminum alloy materials. Firstly, based on the staged fatigue damage experiments, the three-dimensional defect features are obtained through CT scanning and reconstruction, and a defect characterization and processing method based on k-d tree and multi-scale feature pyramid is established to accurately represent the topological and geometric relationships of non-uniformly distributed three-dimensional defects. Secondly, a mathematical model for the evolution of micro-damage and macro-cracks is constructed, and the cross-scale transformation of defects is achieved through hierarchical and recursive methods, revealing the cross-scale evolution mechanism of fatigue damage in aluminum alloy materials. Finally, a remaining life prediction model based on defect information and feature weights is established through the support vector regression algorithm (SVR). This research method can provide technical support for the fatigue life optimization design application of lightweight materials such as aluminum alloys.
Zhang, Liangxia, Niu, Zhijun, Cheng, Fangfang, Chen, Hao, Yang, Yali
Research on Machining Deformation Simulation and Prediction of Low-Stiffness Structural Components under Cutting Force2026-99-02377/31/2026
During the cutting process of low-stiffness structural components, the coupling effect between dynamic deformation and cutting forces presents a significant challenge in accurately predicting machining-induced deformations, thereby complicating quality control in the manufacturing of such parts. To address this issue, a cutting force-structural coupling simulation method that combines experiment and finite element is proposed, which takes into account the low-stiffness characteristics of structural components. Focusing on thin-plate parts as the research object, an orthogonal experimental scheme is designed considering workpiece thickness that serves as an indicator of rigidity. A milling force prediction model correlated with workpiece thickness is established. Based on the predicted cutting forces, a multi-analysis-step simulation method is introduced to analyze the machining deformation of structural parts. Additionally, a theoretical analytical model for the machining deformation of thin-plate workpieces is developed. A comparison between the theoretical and simulation results shows a relative error of less than 1.03%, validating the accuracy of the proposed simulation method. Finally, the exponential regression model for the machining deformation is constructed using training data obtained from the simulations. The prediction error of the regression model is less than 15%. The findings of this study are also applicable to predicting machining deformations in other large and low-stiffness structural components.
Zhao, Yongsheng, Gao, Pengfei, Xu, Jingjing, Liu, Zhifeng
Research on the Structural Performance of a New Modular Fireproof Wall for Converter Station Valve Hall in High-Altitude Areas2026-99-08327/30/2026
Aligned with the “3060 dual carbon” goal, the rapid growth of new energy installation capacity in China’s western high-altitude regions has caused an urgent demand for UHV converter station construction. This paper suggests a prefabricated structural system by using embedded ear-shaped tongue-and-groove UHPC wall-column connections to meet the challenges of traditional cast-in-place concrete firewalls, such as prolonged construction periods and difficulty in quality control in harsh environments. The seismic performance of the connection was investigated through pseudo-static tests and finite element analysis. The results show that failure mainly occurs on the wall–column interface, with cracks mainly appearing at the wall panel corners. The scaled model demonstrated full hysteresis loops, indicating stable energy dissipation. The ear-shaped tongue-and-groove connection showed superior initial stiffness and ultimate load-bearing capacity (404.3 kN) compared with the straight-type connection (177.5 kN). An increase in the semicircular diameter improved load capacity, while the axial compression ratio had little effect. This study proposes a theoretical reference for the design and application of prefabricated valve hall structures in high-altitude regions.
Wang, Fengyun, Yan, Yong, Zeng, Cheng, Zhou, Ting, Ren, Zhaoyang
Dynamic Assessment and Optimization of Bridge Resilience under Multi-Hazard Coupling2026-99-08297/30/2026
Traditional methods for assessing bridge resilience often focus on single hazards or static conditions. Yet bridges today face more complex multi-hazard threats. To address this, this research develops a dynamic model to evaluate bridge resilience under multi-hazard conditions, which is intended to provide scientific support for decision-making to improve resilience. The study first establishes an index system that measures a bridge’s ability to absorb impacts, adapt during an event, and recover afterward. We also propose a method to calculate the coupling degree, which quantifies the amplification effect of multiple hazards, such as an earthquake followed by a flood, on each other’s impacts. Next, we clarify the interrelationships among key resilience factors. Using this understanding, we construct a system dynamics model that simulates the variation of bridge resilience over a full disaster cycle. Finally, a numerical simulation is carried out for a concrete continuous girder bridge in China’s coastal areas as a case study. The results confirm the model is valid and clearly show the differences in bridge resilience between single-hazard and multi-hazard events. More importantly, they prove that combined hazards make the bridge system much more vulnerable. The model also identifies the best strategies for intervention: a strategy that coordinates actions across all disaster phases performs best, as it most effectively reduces the impact of compound hazards and keeps the resilience curve smoother. In short, this study presents a new method for assessing bridge resilience and provides engineers and managers with a practical tool to identify structural weaknesses and optimize resource allocation for resilience improvement.
Lin, Jiachen, Chai, Liang
Calculation and Test on Ultimate Burst Strength of Tubing2026-99-16057/24/2026
Tubing Ultimate burst strength Full scale test
Cheng, Wenjia, Yang, Hongbin, Ge, Yuan, Zhong, Chongdi, Meng, Lingkun, Ji, Bingyin, Shi, Jiaoqi
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
Study on the Removal of Volatile Organic Compounds in Water by Ultracrosslinked Network Hydrogels2026-99-16427/24/2026
-2-1
Chen, Ruini, Lu, Bing
Generating Machine-Processable Specifications from Natural Language Using Large Language Models in Automotive Commissioning and Testing2026-01-07687/1/2026
In vehicle production, commissioning and testing processes of electric and electronic components are essential for value creation and quality assurance. The emergence of software-defined vehicles, however, leads to an increased scope and complexity of these processes as software functions depend on electric and electronic components for perception, execution, and processing tasks. In this context, this paper tackles a common challenge: Software that is deployed in vehicle production to implement commissioning and testing processes is developed upon specifications that define prerequisites, procedures, and target results in natural language. Therefore, extensive human interpretation and manual translation into executable code are needed being susceptible to errors as well as time-consuming. The large number of vehicle configurations and rapid changes in vehicle software further complicate the development of commissioning and testing software, particularly as verbose textual dependency descriptions risk impairing comprehensibility. Machine-processable specifications facilitating automated validation and code generation or direct execution could consequently ensure consistency, reduce manual effort, and accelerate the development process. For this purpose, we examine the processability of commissioning and testing specifications in natural language by proposing a pipeline designed to systematically transform these specifications into a machine-processable format. In particular, we introduce a unified schema that serves as an input format for the large language models tasked with the transformation. Subsequently, several large language models are evaluated in practical trials, based on their ability to translate commissioning and testing specifications into a machine-processable notation. In summary, this study aims to enable more efficient and data-driven software development based on textual requirements. This work offers valuable insights into the suitability and applicability of large language models within the planning of automotive commissioning and testing processes, targeting enhanced automation and efficiency.
Köhler, Katja, El Asad, Aiman, Hahn, Michael, Reuss, Hans-Christian
Evaluating the Impact of Fuel Additives on Stochastic Pre-ignition Using Center for Quality Assurance (CQA) Approved Methodology03-19-03-00166/22/2026
Stochastic preignition (SPI) or low-speed preignition (LSPI) is an abnormal combustion phenomenon observed in downsized turbocharged direct-injection spark-ignition engines at highly boosted conditions. SPI results from the ignition of the air-fuel mixture from a fuel or oil droplet or a detached deposit before the spark discharge, and its occurrence can lead to extremely high peak pressures and severe knock, which can cause physical damage to the engine. This phenomenon limits the downsizing and boosting potential of direct-injection spark-ignition engines, thereby constraining the efficiency benefits that can be achieved. The propensity for SPI to occur is impacted by engine operating conditions as well as the properties of the fuel, fuel additives, lubricant, and lubricant additives. To mitigate its occurrence, it is important to understand the factors that impact the frequency of SPI events. As this abnormal combustion phenomenon is relatively recent, there was a lack of a standard procedure to detect the impact of a parameter on SPI frequency. This study details the development and validation of an engine dynamometer test procedure—the TOP TIER™ Standardized Dynamometer Test Method to Evaluate Additized Detergent Gasoline for SPI—approved by the Center for Quality Assurance (CQA), to evaluate gasoline additives for their impact on SPI. In this project, the newly validated SPI test protocol was used to compare the relative SPI tendencies of four TOP TIER™ fuel additives at maximum retail concentration against unadditized SPI test fuel, which served as the baseline. All four fuel additives were tested three times in randomized order. The results revealed that none of the TOP TIER™ additives tested had a statistically significant impact on the SPI rate.
Gopujkar, Siddharth, Davis, Richard, Worm, Jeremy, Tuma, Nic, Shukla, Prajwal, Reilly, Veronica, Chapman, Elana, Ciaravino, Joseph, Seyfried, Philipp
Methodology Development for Electric Drive Module Vibro-Acoustic Assessment Using Simulation Measurement Comparison and Root Cause Analysis2026-01-06916/20/2026
The virtual development of Electric Drive Modules (EDMs) for Battery Electric Vehicles (BEVs) requires proven and predictive methodologies. One part of the development investigates the vibro-acoustic assessment for the low- and high-frequency ranges within the targeted operating range. The efficient use of such a methodology requires an understanding of the accuracy and validity of the achievable results, as well as the derivation of suitable improvement measures for goals that have not been achieved. The use of reference data from experimental investigations and a detailed root cause analysis (RCA), to directly link a specific response and behavior to the excitations, modal content, and transfer functions, is an essential and non-trivial part of the methodology development. This paper describes the development of such a methodology using the example of a new EDM virtual model for Noise, Vibration and Harshness (NVH) analysis, including the simulation approach, validation, and evaluation procedure. It discusses how RCA can be applied to different observed phenomena in EDM NVH behavior and detected deviations between the initial model and the measurements, the main influencing parameters, and the identified improvement potential for simulation models.
Klarin, Borislav, Pevec, Denis, Resch, Thomas, Esposito, Sara, D'Alessandro, Vincenzo, Spanu, Giorgio
Best-in-Class NVH Optimisation for Electric Powertrains, Holistic Front-Loading Approaches2026-01-06896/20/2026
Achieving best-in-class Noise, Vibration, and Harshness (NVH) in electric powertrains demands a paradigm shift in development methodology. This paper presents a practice-oriented overview of simulation methods in NVH development methodology for electric drive units. This includes target cascading and multi-objective optimisation, and by attacking NVH at the source using KPIs early in the design cycle, significant reductions in development time and reliance on traditional testbed loops are realised. Machine learning (Neural Network) algorithms are utilized to find the best-in-class design, using multi-objective optimisation as well as refining simulation accuracy by adding tolerance effects while target cascading ensures alignment of system-level performance objectives down to subsystem contributions. Combined, these strategies enable rapid and robust NVH optimisation, using simulation for next-generation electric powertrain development. Several applications and real-life examples demonstrate how simulation helped with NVH issue identification or improvement.
Mehrgou, Mehdi, Garcia de Madinabeitia, Inigo, Graf, Bernhard, Gojo, Josef
FBS as Key Enabler for Efficient Root Cause Analysis in NVH Simulations2026-01-07106/20/2026
Noise, Vibration, and Harshness (NVH) performance is critical in the automotive development process, yet identifying the true root causes of unwanted dynamic behavior remains a challenge in full vehicle or system-level finite element (FEM) models. This work demonstrates how Frequency Based Substructuring (FBS) provides an efficient framework for understanding NVH phenomena and facilitates new root cause analysis (RCA) types and processes. To begin, we prove the numerical accuracy of the FBS algorithm deployed in the presented investigation by comparing its results with those obtained with superelements and without substructuring. We point out that because the used FBS process starts with a modal representation of the components rather than their frequency response functions (FRF) a different class of RCA type becomes available. Then we introduce new RCA types starting with an analysis named Modal Influence (MI) that reveals the effect of the modes of any component on a certain response. Its key characteristic is that MI analysis is not restricted to the response component opposite to the well-known modal participation factors. Finally, a second novel analysis type is introduced, an advanced variant of Transfer Path Analysis (TPA). While standard TPA assesses the paths between only two system components, the new Expanding TPA is a multi-step process that identifies the most critical path across all components in a fully automated way.
Herbst, Markus
Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection2026-01-07176/20/2026
In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: ① extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; ② label imbalance caused by the relatively small amount of defect data; ③ reduced labeling accuracy due to human error; ④ decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); ⑤ diminished model reliability when new defect arise during development; and ⑥ constraints imposed by compatibility requirements with existing test equipment. This study proposes a Convolutional Autoencoder (CAE) based framework trained on NVH datasets collected from normal and defective Column-type Electric Power Steering (C-EPS) systems. Latent variables at the bottleneck layer are used for dimension reduction, enabling visualization and unsupervised classification using a clustering algorithm. A classification model derived from the encoder is fine-tuned with clustered data, and Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable AI (XAI) technique, is applied to extract Feature Frequency Maps (FFM) highlighting defect-related noise and vibration characteristics. The proposed approach does not rely on the deep learning model to directly classify defect. Instead, it utilizes extracted FFM as weights(mask) to detect defect. This method enables quantitative data representation and ensures high applicability with existing EOL equipment. Post-processing within the FFM enables root cause analysis, reducing issue resolution time and supporting integration with conventional signal analysis techniques.
Park, Jun-Seo, Jo, Hyeon-Choel, Cho, In-Je, Seo, Jae-Yong, Yoo, Seong-Sik
Direct Delivery Authorization Guidance for Aerospace CompaniesAS9107 (Current)6/12/2026
Limited to the commercial aerospace industry where a request is made for a PO to have Direct Delivery Authorization (DDA), which includes an Appropriate Arrangement (AA) between the PO and the Design Organization (DO). In this process, the DO is responsible for ensuring the continuous updating of approved design data to the PO, whilst the PO is responsible for assurance that the manufactured article conforms to approved design data. The PO is responsible to provide airworthiness release documentation.
SAE Committee
Delegated Product Release VerificationAS9117A (Current)6/12/2026
This standard specifies requirements for DPRV to establish common product/service requirements for use at all levels of the supply chain. This standard shall apply when an organization elects to delegate product release verification by contractual flow down to their supplier (reference 9100 and 9110 standards) to perform product acceptance on their behalf. The delegating organization shall use this standard as the baseline for establishing a DPRV process, although they may include additional contract requirements to meet their specific needs.
SAE Committee
Requirements Extract for AS13100A Quality Management System Requirements for Aero Engine Design and Production Organizations SA-DIG-000076/9/2026
This digital standard is a requirements extract of AS13100A Quality Management System Requirements for Aero Engine Design and Production Organizations. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
Requirements Extract for AS9145 Requirements for Advanced Product Quality Planning and Production Part Approval Process SA-DIG-000096/9/2026
This digital standard is a requirements extract of AS9145 Requirements for Advanced Product Quality Planning and Production Part Approval Process. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
Requirements Extract for AS4159 Specification For An Automated Interchange Of Standards Data SA-DIG-000046/9/2026
This digital standard is a requirements extract of AS4159 Specification For An Automated Interchange Of Standards Data. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
Requirements Extract for AS13001A Delegated Product Release Verification Training Requirements SA-DIG-000066/9/2026
This digital standard is a requirements extract of AS13001A Delegated Product Release Verification Training Requirements. This file contains a general requirements extraction as well as files that are optimized for use with Doors Classic, Siemens Polarian, and PTC.
Digital Model for AS9100D Quality Management Systems - Requirements for Aviation, Space, and Defense Organization SA-DIG-000086/9/2026
This digital standard is a digital model of AS9100D Quality Management Systems - Requirements for Aviation, Space, and Defense Organization. This file contains an MBSE model in a mdzip file for use in modeling applications.
Design Optimization of Aircraft Light Optics: Eliminating Fasteners by Flexure-Based Clamp to Mitigate the Crack Formation and Validation through HALT2026-26-07506/1/2026
Aircraft lighting systems play a vital role in ensuring operational safety, visibility, and regulatory compliance. Exterior lighting systems are essential for aircraft identification, navigation, collision avoidance, and ground operations under varying environmental conditions. These systems typically include navigation lights, anti-collision lights, landing and taxi lights. An aircraft lighting system comprises light sources, optical elements, electronic control units, power interfaces, wiring harnesses, and mechanical mounting structures. Among these components, optics are critical as they control light distribution, intensity, color accuracy, and efficiency while withstanding harsh aerospace environments such as vibration, thermal cycling, and aerodynamic loads. Aircraft exterior lights are subjected to severe thermo-mechanical stresses due to aerodynamic loading, vibration, and thermal cycling. The use of high-performance optical polymers such as Cyclo Olefin Polymers (COP) provides excellent light transmission and stability; however, their relatively lower mechanical toughness makes them susceptible to stress-induced cracking during assembly. In the baseline configuration, the Circuit Board Assembly (CBA) was fastened directly onto the optic using self-tapping screws. During assembly, frequent crack initiation was observed in the optic around the fastener locations, leading to concerns regarding reliability and maintainability. To address this issue, a redesigned mounting approach was developed that eliminated direct fastener penetration into the optic. Instead, the CBA is retained using a precision clamping mechanism, thereby distributing assembly loads uniformly and avoiding localized stress concentrations. COP material was retained due to its superior optical characteristics and compliance with photometric requirements for aircraft lighting applications. The redesigned optic-CBA interface was validated through Highly Accelerated Life Test (HALT), incorporating combined vibration, temperature, and thermal shock profiles. Test results confirmed that the new clamping design prevented crack formation, improved mechanical robustness, and ensured long-term optical performance. This paper presents the problem definition, root cause analysis of fastener-induced cracking, the design rationale for adopting a clamp-based mechanism, and detailed HALT validation results. The study highlights the importance of integrating material properties, fastening strategies, and environmental testing in the design of aerospace lighting systems. The proposed design methodology provides a pathway to enhance reliability and lifecycle performance of critical optical components in aircraft applications.
Vialta, Frederico, S, Nikhil, Katageri, Praveen, SP, Pradeep, Singh, Abhimanyu Kumar
Agentic Digital Thread for Managing the Non-Conformities in Manufacturing of Aerospace Products2026-26-07636/1/2026
Aerospace manufacturing operates within an intricate ecosystem where quality, compliance and traceability are critical to success. Conventional digital thread frameworks provide connectivity but remain largely passive, lacking the intelligence to autonomously manage complex non-conformities across the product lifecycle. This paper introduces an Agentic Digital Thread powered by Agentic AI, designed to transform non-conformity management into an adaptive, self-orchestrating system that actively drives decision-making and corrective actions [1, 4]. The proposed architecture employs a Master Agent to coordinate workflows and maintain end-to-end data continuity, while specialized Agents autonomously manage domain-specific tasks. In the pre-manufacturing phase, these agents proactively validate requirements, material conformity and process planning through integration with PLM, MES, ERP, QMS and supplier systems. In the post-manufacturing phase, the framework extends to concession management, enabling structured workflows for identifying, evaluating and approving deviations during inspection or final assembly. By embedding AI-driven anomaly detection, semantic search of historical concessions, and Generative AI-powered report authoring, the system accelerates resolution and predicts concession acceptance with high confidence. Continuous feedback loops between design, production and quality assurance transform the digital thread from a static data conduit into an intelligent ecosystem that ensures compliance, reduces delays and rework, and fosters continuous improvement. This approach delivers a resilient and adaptive aerospace manufacturing process aligned with the demands of next-generation aircraft production [9, 10].
Veluri, Sastry, Gopala Krishnan, Kannan
Automating DO-178C Verification and Security Compliance in DevSecOps CI/CD Pipelines for Safety Critical Avionics Software2026-26-07176/1/2026
This paper presents an automated framework for security compliance and quality assurance in DevSecOps CI/CD pipelines, specifically designed for safety-critical avionics software. The framework integrates regulatory compliance checks, security validation, and robust verification directly into the software development lifecycle, supporting continuous integration and delivery for aerospace applications. Automated processes such as code compilation, coding standards compliance, Cyclomatic Complexity Measurement, Sources Line of Code and CRC validation on target hardware are seamlessly orchestrated to maintain consistency and reliability. The system generates comprehensive compliance reports, highlights coding standard violations and security issues, and notifies relevant stakeholders to facilitate timely resolution and corrective actions. As new code is checked in, the framework automatically initiates all verification and compliance tasks, ensuring that every software update is thoroughly validated without manual intervention. Daily automated testing and coding standards checks are performed to maintain ongoing software quality and compliance. By automating key verification and compliance activities, the framework minimizes human error and supports efficient regulatory compliance throughout the development process. Integration of these capabilities within DevSecOps pipelines enables rapid, repeatable, and auditable software releases, significantly reducing manual effort and accelerating delivery of high-quality builds to customers. The framework enhances digital verification, validation, and certification readiness by providing comprehensive evidence required for regulatory audits, ultimately improving overall project assurance and reducing technical debt for aerospace software teams. These automation techniques collectively help organizations achieve verification processes of DO-178C standards more effectively, ensuring that safety-critical software meets stringent industry requirements while streamlining the certification process, reducing time-to-market, and enabling faster deployment of reliable solutions to end users and stakeholders.
Bhagwat, Shashank Ravi, Changappa, Naveen Kumar, Nath, Sunny
Quality Assurance: The Hidden Key to Battery ManufacturingTBMG-552636/1/2026
Evaluation of Mechanical Tensile Properties on TIG-Welded AISI 304L Austenitic Stainless Steel Using Principal Component Analysis05-19-04-00275/14/2026
The reliability of welded joints is a vital factor in modern manufacturing, directly affecting product performance and durability. This study investigates methods to enhance the mechanical and metallurgical quality of butt joints in AISI 304L stainless steel welded by the gas tungsten arc (GTA) process. A systematic experimental design was implemented using the Taguchi method with an L9 orthogonal array, considering welding current, gas flow rate, and travel speed as the main parameters. To determine overall weld performance, the joints were characterized by measuring ultimate tensile strength (UTS), yield strength, percentage elongation, and examining their microstructural morphology. An experimental strategy based on the Taguchi approach has been implemented. The welding performance of the material was investigated, and the process parameters were optimized using multiresponse optimization through principal component analysis (PCA), incorporating an orthogonal array design, signal-to-noise (S/N) ratio, and analysis of variance (ANOVA). C1G1S3—the predicted optimal parameter combination—is the ideal factor configuration as determined by PCA (welding current = 100 A, gas flow rate = 10 L/min, travel speed = 2 mm/sec). Results demonstrate that precise control of process parameters significantly enhances weld quality. The methodology also provides a systematic framework that engineers and practitioners can apply to produce reliable stainless steel welds with improved accuracy and predictability.
Ghosh, Nabendu, Roy, Angshuman
Delegated Product Release Verification Training RequirementsAS13001A (Current)5/13/2026
This standard establishes the common requirements for training of DPRV personnel for use at all levels of the aerospace engine supply chain. This standard shall apply when an organization elects to delegate product release verification by contractual flow down to its suppliers (reference 9100 and 9110 standards) and to perform product acceptance on its behalf. It is intended that organizations specify their DPRV requirements through the application of AS9117. While the delegating organization will use the AS13001 standard as the baseline for establishing DPRV process and product training, it may include additional contractual training requirements to meet its specific needs. The DPRV training material was primarily developed for aerospace engine supply chain requirements. However, this standard may also be used in other aerospace industry sectors where a DPRV process requiring specific training can be of benefit.
G-22 Aerospace Engine Supplier Quality (AESQ) Committee
Potential Failure Mode and Effects Analysis (FMEA) Including Design FMEA, Supplemental FMEA-MSR, and Process FMEAJ1739_202605 (Current)5/8/2026
This FMEA standard describes potential failure mode and effects analysis in design (DFMEA), supplemental FMEA-MSR, and potential failure mode and effects analysis in manufacturing and assembly processes (PFMEA). It assists users in the identification and mitigation of risk by providing appropriate terms, requirements, rating charts, and worksheets. As a standard, this document contains requirements—”must”—and recommendations—”should”—to guide the user through the FMEA process. The FMEA process and documentation must comply with this standard as well as any corporate policy concerning this standard. Documented rationale and agreement with the customer are necessary for deviations in order to justify new work or changed methods during customer or third-party audit reviews.
Automotive Quality and Process Improvement Committee
Gear Coatings for Loss of Lubrication: System Level Transmission Experiment within VIPERF-0082-2026-00405/5/2026
The Army requires rotorcraft drive systems to operate for 30 minutes following a loss of lubrication event to make an emergency landing. Coatings research has shown great promise for loss of lubrication, but coating repeatability and quality control is a primary hurdle. The Army partnered with Acree Technologies via a Small Business Innovation Research (SBIR) effort to develop an optimized gear coating for loss of lubrication. The research culminated in a system level transmission experiment that maintained flight relevant torque and speed through a helicopter gearbox without oil for three hours. The authors decided to shutdown the experiment for inspection after three hours of operation without oil because the temperature and vibration signals maintained steady state conditions without signs of failure. Teardown analysis showed the transmission gear surfaces did not scuff, scanning electron microscope analysis showed coating remained on the gear teeth, and cross-sectional SEM analysis showed a measurable coating thickness remaining on the gear teeth after three-hours of operation without oil.
Riggs, Mark, Pomplon, William, Fetty, Jason, Milligan, Ryan, Woods, Ron, Wong, Kelvin, Matzke, Caleb, Jacques, Kelly, Hood, Adrian
Application of an Automated HUMS Data Chain for Rapid Engine Installation Diagnostics in New Generation Aircraft Production EnvironmentF-0082-2026-02335/5/2026
This paper presents the implementation of a fully automated Health and Usage Monitoring System (HUMS) data chain designed to accelerate installed engine performance diagnostics during the pre-delivery phase of new-generation helicopters. Ensuring that engine performance remains consistent with original engine manufacturer (OEM) baseline data is a critical step in the final assembly process, yet traditionally time-consuming. The developed system automates data offloading and integrates three distinct streams: OEM engine performance characteristics, in-flight Engine Power Checks (EPC), and high-frequency continuous recordings. The core innovation lies in a multi-source data fusion methodology combined with a physics-based model to differentiate between genuine installation discrepancies and sensor anomalies through temperature deviation analysis. Results from the production environment demonstrate that this automated approach significantly reduces troubleshooting lead times and ensures on-time aircraft delivery. By shifting advanced monitoring from in-service operations to manufacturing, this system establishes a new digital benchmark for quality control in helicopter production.
Esterle, Florent, Lecauchois, Claire, Maisonneuve, Pierre-Loïc, Calvet, Thomas
Predictive Modeling and Sensitivity Analysis of Thermal Runaway in 800V Lithium-Ion Battery Module Using DFSS Methodology2026-01-01244/7/2026
Thermal runaway in high-voltage lithium-ion battery modules should focus on critical safety and design challenges in electric vehicle applications, which need predictive methods that enhance passenger safety and support regulatory compliance. The primary purpose of a lithium-ion battery in an electric vehicle is to provide reliable energy storage while maintaining safe operation under different operating conditions. This study proposes a Design for Six Sigma (DFSS) methodology to virtually predict and correlate thermal runaway and its propagation in an 800V high-power lithium-ion battery pack module. Conventional propagation analysis relies heavily on physical testing, whereas the DFSS-based virtual framework enables cost-effective evaluation at early design stages. Input factors included are heat transfer pathways, which are sensitive to the temperature changes, as well as thermal propagation time. Control factors are the design or process parameters that engineers use to establish the functional performance of a system. The noise factors capture material variability and manufacturing tolerances affecting thermal properties. Output responses included the maximum cell temperature Versus time, thermal propagation time to adjacent cells, and total propagation duration across the module, measured in minutes. The validated 1D GT-SUITE model shows strong correlation with experimental data, confirming its reliability to predict thermal propagation time and supporting safer, thermally optimized battery pack designs. The validated model can be integrated into system (battery pack) level 1D thermal simulations, offering a calibrated model for future pack level propagation studies and supporting the development of safer, thermally optimized battery architectures.
Dixit, Manish, Raja, Vinayak, Gudiyella, Soumya
Modeling Non-Uniform Temperature Distribution in Lithium-Ion Batteries2026-01-01204/7/2026
Non-uniform temperature distribution within lithium-ion battery cells is a critical challenge that accelerates degradation, compromises safety, and reduces pack-level performance in electric vehicles (EVs). This work focuses on modeling and minimizing these thermal gradients through the structured optimization of a liquid-based Battery Thermal Management System (BTMS). A one-dimensional transient thermal model is developed to capture the axial temperature differentials (ΔT) in a cylindrical cell under dynamic drive-cycle loading, incorporating detailed heat transfer from the cell interior through thermal interface materials (TIM) and an aluminum cooling plate to the coolant. Using a Design for Six Sigma (DFSS) approach with an L18 orthogonal array, key control factors—including coolant flow rate, inlet temperature, TIM properties, and plate geometry—are systematically analyzed to identify configurations that optimally balance low average temperature with minimal internal temperature variation. The results provide a data-driven framework for designing robust cooling systems that mitigate the risks of localized hotspots and thermal runaway, thereby enhancing the durability and safety of EV battery packs.
El-Sharkawy, Alaa, Asar, Mona, Serpento, Stan, Sheta, Mai
Application of DFSS for Optimizing Thermal Protection in Electric Vehicles during Battery Thermal Runaway Events2026-01-01424/7/2026
Battery thermal runaway is a major safety concern in electric vehicles because of the extreme heat and hazardous gases released during cell failure. These venting events can quickly raise the temperature of the battery enclosure and cabin floor, threatening occupant safety. To address this challenge, this study employs the Design for Six Sigma (DFSS) methodology to design and optimize a thermal protection system that delays and limits heat transfer to the cabin. A physics-based transient heat-transfer model was combined with DFSS principles to systematically evaluate insulation materials, shield layouts, surface emissivity, and layer geometry. An L-18 orthogonal array was used to identify key parameters and quantify their influence on thermal robustness. The optimized architecture reduced cabin-floor temperature rise under severe runaway conditions (600–900 °C vent gas), meeting occupant-egress safety requirements. Findings confirm DFSS as an effective framework for developing high-robustness EV thermal protection systems under uncertainty and extreme boundary conditions.
El-Sharkawy, Alaa, Asar, Mona, Taha, Nahla, Sheta, Mai
The Necessity of a Holistic Safety Evaluation Framework for AI-Based Automation Features2026-01-05224/7/2026
The intersection of Safety of Intended Functionality (SOTIF) and Functional Safety (FuSa) analysis of driving automation features has traditionally excluded Quality Management (QM) components from rigorous safety impact evaluations. While QM components are not typically classified as safety-relevant, recent developments in artificial intelligence (AI) integration reveal that such components can contribute to SOTIF-related hazardous risks. Compliance with emerging AI safety standards, such as ISO/PAS 8800, necessitates re-evaluating safety considerations for these components. This paper examines the necessity of conducting holistic safety analysis and risk assessment on AI components, emphasizing their potential to introduce hazards with the capacity to violate risk acceptance criteria when deployed in safety-critical driving systems, particularly in perception algorithms. Using case studies, we demonstrate how deficiencies in AI-driven perception systems can emerge even in QM-classified components, leading to unintended functional behaviors with critical safety implications. By bridging theoretical analysis with practical examples, this paper argues for the adoption of comprehensive FuSa, SOTIF, and AI standards-driven methodologies to identify and mitigate risks in AI components. The findings demonstrate the importance of revising existing safety frameworks to address the evolving challenges posed by AI, ensuring comprehensive safety assurance across all component classifications spanning multiple safety standards.
Abbaspour, Ali Reza, Mahadevan, Shabin, Zwirglmaier, Kilian, Stafford, Jeff
Maintaining a Healthy Safety Culture in Autonomous Vehicle Development and Operations2026-01-05284/7/2026
Safety isn’t just the absence of accidents - it’s the presence of trust, empowerment, and accountability at every level. The result is a high-trust culture where process becomes practice and safety is a shared achievement. When people closest to the work feel supported to act on what they see, safety becomes the standard. Thus, the deployment of autonomous driving systems (ADSs) requires not only technical rigor but also a resilient organizational safety culture that supports continuous learning, accountability, and transparent communication. This paper examines how safety culture can be operationalized in ADS development and operations by integrating guidance from standards such as UL 4600 and best practices from SAE AVSC. UL 4600’s requirements for systematic hazard analysis, safety case maintenance, and safety performance indicators (SPIs) are used as a foundation for quantifying organizational behavior within a Just Culture framework. This work draws on Human and Organizational Performance (HOP) research, including foundational contributions from Hollnagel, Reason, Dekker, Conklin, and Rasmussen, linking cultural dynamics to workforce involvement and effective safety controls. We propose a taxonomy of seven safety-culture SPIs that trace directly to UL 4600 § 16.2.5 and demonstrate how they can be deployed within an incident-handling process. Each SPI is defined mathematically and mapped to process steps, enabling both leading- and lagging-indicator assessment of safety culture maturity. This proposed framework, which requires formal research validation, transforms SPIs from compliance metrics into qualitative diagnostic tools for trust, empowerment, and system learning. The approach aligns organizational processes with Just Culture principles, distinguishing human error, at-risk behavior, and reckless conduct, while supporting continuous improvement and evidence-based conformance with UL 4600 and related ADS safety standards.
Wagner, Michael, Gittleman, Michele
Sensorless Estimation of In-Cabin CO 2 Concentration for Model-Based Air Recirculation Control2026-01-01304/7/2026
This paper introduces a sensorless approach for data-driven modeling of in-cabin CO2 concentration to optimize air recirculation flap control without the need for a dedicated CO2 sensor. Elevated CO2 concentrations, resulting from passenger exhalation, can impair occupants’ cognitive function and comfort. Current state-of-the-art solutions rely either on time-based control strategies, which lack responsiveness to actual cabin conditions, or on direct CO2 measurements via sensors, which increase system complexity and costs. In contrast, the proposed approach aims to replicate the benefits of sensor-based control without requiring physical sensors. In this study, a model-based methodology is presented, utilizing empirical CO2 measurement data collected from real-world test drives at varying occupancies, fan stages, vehicle speeds, and flap positions. Data acquisition involves a multi-gas analyzer positioned within the passengers’ breathing zone under controlled operation of the vehicle’s climate control unit. Based on these measurements, time-dependent CO2 concentration profiles are represented using exponential functions. These regression curves capture CO2 accumulation, depletion, and balancing behaviors, considering factors such as cabin leakage, pressure differentials at varying speeds, and ventilation conditions. These influences are inherently included in the calibration curves due to their empirical basis. The derived regression curves are implemented into a control model to simulate CO2 concentration throughout the drive, including situations where outside pollution is high and prolonged air recirculation is necessary – such as when driving through tunnels or behind trucks. On the baseline of this simulation, the sensorless control strategy adjusts flap positions accordingly, thereby minimizing both excessive CO2 buildup and unnecessary energy losses due to overventilation. By omitting CO2 sensors and relying solely on existing in-vehicle databus signals, this approach offers a cost-effective solution for cabin air quality management. Future work will focus on real-world validation of the control model and integration of exterior air quality monitoring as a complementary input.
Stürmer, Michael, Geier, Bertram, Hofstetter, Martin, Hirz, Mario
Predicting Electric Vehicle Interior Noise Quality from Drive Unit End-of-Line Vibration Testing2026-01-02234/7/2026
The Noise, Vibration, and Harshness (NVH) quality of electric vehicles (EVs) is heavily influenced by the performance of the electric drive unit. As a critical step in production, End-of-Line (EOL) testing of drive units is used to assess and control component-level NVH before vehicle assembly. However, the correlation between EOL test results and final vehicle interior noise quality, which directly impacts customer satisfaction, is not always fully understood. This paper presents a methodology for characterizing and predicting vehicle interior noise quality based on data from drive unit EOL vibration testing. Our study investigates the intricate relationship between drive unit assembly variations, component tolerances, and the resulting vibration response. We establish a robust correlation between these drive unit characteristics and both objective vehicle interior noise levels and subjective customer perception. The findings provide a framework for using EOL data to proactively identify critical manufacturing risks and optimize processes. This approach not only facilitates the delivery of a superior, noise-concern-free product but also contributes to reducing manufacturing costs by minimizing scrap and rework. This research advances NVH quality assessment for EVs and provides manufacturers with a vital tool to enhance customer experience and satisfaction in a competitive market.
Arvanitis, Anastasios, Jangid, Kuldeep
Open Source LLM Performance in Automating Embedded C Software Quality Improvements for Automotive2026-01-01034/7/2026
The study presented in this paper explores the potential of five open-source Large Language Models (LLMs) with parameter counts between 32 billion and 49 billion to automate enhancements in code quality and developer productivity. The evaluated models – CodeLlama [1], Command-R [2], Deepseek R1-32B [3], Nemotron [4], and QwQ [5] - were assessed on their ability to refactor a large and complex automotive mechatronic C language function. This assessment focused on adherence to provided code quality standards and successful compilation of the refactored function within a larger code module. The evaluation also compared the impact of parameter count, hyperparameter tuning, model architecture, and fine-tuning. This comparison revealed that larger models showed superior overall performance, though with notable exceptions where smaller models performed better in specific rule categories. Additionally, hyperparameter tuning yielded modest improvements in performance. The study also highlighted that model architecture and fine-tuning had less predictable effects, suggesting further exploration is required. Furthermore, some rules were more difficult to apply than others, and generated code often contained critical logical issues such as uninitialized variable use, excessive placeholders, and missing logic. This paper provides insights into patterns and behaviors observed in the study related to the strengths and weaknesses demonstrated by these open-source LLMs.
Struck, Daniel, Kumaraswamy, Samanth
Quality Assurance Sampling and Testing, Corrosion- and Heat-Resistant Steel and Alloy ForgingsAMS2374G (Current)3/12/2026
This specification covers quality assurance sampling and testing procedures used to determine conformance to applicable material specifications of corrosion- and heat-resistant steel and alloy forgings.
AMS F Corrosion and Heat Resistant Alloys Committee
How higher-quality gasoline keeps modern engines CLEAN AND EFFICIENT26AUTP03_033/1/2026
Gasoline direct injection (GDI) engines are the most common technology on American roadways in 2025, and soon, an industrywide gasoline quality standard will better reflect their unique operational needs. Here's why that's important. It's no secret that fuel economy has been one of the greatest driving forces of automotive evolution over the past several decades. As corporate average fuel economy (CAFE) standards have grown increasingly lofty, OEMs eke out new efficiencies from every area of the vehicle. One of those areas, of course, is the engine, and many OEMs have deployed gasoline direct injection (GDI) technology, which is becoming the most common engine technology on American roadways. But while GDI engines proliferate, varying fuel additization throughout North America has not necessarily kept pace with their unique needs and can, in fact, hinder those engines from meeting and sustaining their full fuel economy potential.
Blackburn, Brett
Manufacturing Spotlight: Rugged Computing for Infinite Exploration26AERD03_043/1/2026
In the rapidly evolving aerospace and defense landscape, simply keeping pace with trends isn't enough. Technology is advancing faster than ever, and in mission critical applications, failure is not an option. Systems must endure harsh environments while meeting uncompromising quality standards - an imperative that demands relentless innovation. Enter the Coyotes: WOLF's specialists in next generation rugged embedded systems, small form factor design, and bold, practical ideas. Whether on Earth or in orbit, they expand what high performance embedded computing can do across ground, orbital, lunar and deep space operations. Their work spans R&D, rapid prototyping and new product development for edge computing and artificial intelligence (AI) enabled imaging.
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