Browse Topic: Quality control

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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
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
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
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
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
Study on the Removal of Volatile Organic Compounds in Water by Ultracrosslinked Network Hydrogels2026-99-16427/24/2026
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Chen, Ruini, Lu, Bing
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 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.
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 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.
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.
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
Software Reliability Program Implementation GuideJA1003_201205 (Current)5/26/2026
This document provides methods and techniques for implementing a reliability program throughout the full life cycle of a software product, whether the product is considered as standalone or part of a system. This document is the companion to the Software Reliability Program Standard [JA1002]. The Standard describes the requirements of a software reliability program to define, meet, and demonstrate assurance of software product reliability using a Plan-Case framework and implemented within the context of a system application. This document has general applicability to all sectors of industry and commerce and to all types of equipment whose functionality is to some degree implemented by software components. It is intended to be guidance for business purposes and should be applied when it provides a value-added basis for the business aspects of development, use, and sustainment of software whose reliability is an important performance parameter. Applicability of specific practices will depend on the reliability-significance of the software, application domain, and life cycle stage of the software. Following guidelines in this document does not guarantee required reliability will be achieved, or that any certification authority will accept the results as sufficient evidence that requisite reliability has been achieved. Following guidelines in this document will provide insight into what level of reliability has been achieved. With proper customer, certification authority, and supplier negotiation and interaction in accordance with these guidelines, it is more likely that the achieved reliability will be acceptable.
G-41 Reliability
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
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
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
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
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.
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
Detecting Fuel Adulteration for Otto-Cycle Vehicle Using Onboard Diagnostics Data and Supervised Machine Learning04-19-02-00082/26/2026
Fuel adulteration affects operating costs, vehicle efficiency, and air pollution. Published estimates suggest it accounts for at least 10% of global sales. The Brazilian National Petroleum Agency (ANP) reported noncompliance in about 23% of inspections in 2023, including 4.3% confirmed adulteration. Quality verification requires laboratory equipment, and sensor-based approaches are often inaccessible to end consumers. This article proposes a sensorless (software-only) method that detects water adulteration in hydrated ethanol from standard Onboard Diagnostics (OBD) data using supervised machine learning, enabling on-vehicle fuel quality monitoring without additional hardware. The proposed approach is evaluated on real-world driving data from two production vehicles with three water adulteration levels in hydrated ethanol (0.0%, 2.5%, and 5.0%), achieving 84.85%–95.85% multiclass classification accuracy. These results indicate that software-only, OBD-based monitoring can provide a practical solution for in-use fuel quality control.
Marchezan, Andre Ricardo, Giesbrecht, Mateus
Counterfeit Materiel; Assuring Acquisition of Authentic and Conforming MaterielAS6174B (Current)2/17/2026
This SAE Standard provides requirements and guidance to: Develop a Materiel authenticity plan. Procure Materiel from reliable sources. Assure authenticity and conformance of procured Materiel, including methods such as certification, traceability, testing, and inspection appropriate to the Commodity/item in question. Control Materiel identified as counterfeit. Report Suspect or Counterfeit Materiel to other potential users and Authorities Having Jurisdiction.
G-21 Counterfeit Materiel Committee
Efficient Multi-Parameter Optimization of a Twist Beam Rear Axle for Electric Vehicles Using TOPSIS Method for Durability2026-28-00672/12/2026
This study investigates the parameter optimization of a Rear Twist Beam (RTB) for an electric vehicle (EV) during the early stages of product development. Adapting an RTB design from an Internal Combustion Engine (ICE) vehicle platform presents several challenges, one of the challenges is accommodating increased rear vehicle load while minimizing cost, with maintaining existing rear hard points. To address this, we employed an experimental study for Computer-Aided Engineering (CAE) using the Taguchi DOE, which avoids costly physical durability tests. The key design parameters considered were the thickness and material grade of the RTB's components, specifically the cross beam, trailing arms, and reinforcements while preserving their original shapes. L8 Orthogonal array is constructed to design the experiment and identify the influence of the design parameters on durability performance, and the optimal combinations for maximizing durability are identified by using TOPSIS multi objective method. This approach offers significant cost savings by avoiding different iterative physical testing during vehicle development stage. The study found that while changes in the thickness or material of components had mere effect on the rear axle's stiffness, the thickness of the cross beam and trailing arms significantly impacted its durability under various loads.
Madaswamy, Arunachalam, Dhanraj, Sudharsun, Govindaraju, Karthik, Lokaiah, Srinivasan
Ensuring Functional Safety in Electric Vehicle Inverters: A Case Study on ISO 26262 Implementation2026-26-01581/16/2026
The transition to electric vehicles (EVs) has brought about significant advancements in automotive technology, with inverters playing a crucial role in converting DC power from the battery to AC power for the electric motor. Ensuring the functional safety of these inverters is paramount, as any failure can have severe implications for vehicle performance and passenger safety. This case study explores the successful implementation of ISO 26262 standards in the development and validation of EV traction inverters. This paper begins by outlining the functional requirements and safety goals specific to EV inverters, followed by a detailed analysis of the potential hazards and risks associated with their operation. Using ISO 26262 as a framework, we describe the systematic approach taken to identify, assess, and mitigate these risks. Key methodologies such as Hazard Analysis and Risk Assessment (HARA), Failure Mode and Effects Analysis (FMEA), and Fault Tree Analysis (FTA) are employed to ensure comprehensive safety coverage. This case study showcases the integration of key safety mechanisms—such as redundancy, fault tolerance, and real-time monitoring—to significantly enhance the reliability and robustness of the inverter system. It also explores the challenges encountered during implementation, including the complexity of managing safety critical high-voltage systems and the need to stay aligned with evolving safety standards.
Ramachandra, Shwetha, V, Sushmitha
Integrated Quality Assurance Frameworks for Testing Advanced Automotive Systems in the Era of ADAS and E-Mobility2026-26-05681/16/2026
Ensuring the safety and functionality of sophisticated vehicle technologies has grown more difficult as the automotive industry quickly shifts to intelligent, electric, and connected mobility. Software-defined architectures, electric powertrains, and advanced driver assistance systems (ADAS) all require strong quality assurance (QA) frameworks that can handle the multi domain nature of contemporary vehicle platforms. In order to thoroughly assess the functionality and dependability of next generation automotive systems, this paper proposes an integrated QA methodology that blends conventional testing procedures with model-based validation, digital twin environments, and real-time system monitoring. The suggested framework, which includes hardware-in-the-loop (HIL), software-in-the-loop (SIL), and over-the-air (OTA) testing techniques, concentrates on end-to-end traceability from specifications to validation. Simulating intricate situations for ADAS, electric vehicle battery temperature management, and dynamic system updates in connected platforms are prioritized. This study also outlines the main obstacles to integrating QA methods with changing regulatory environments and draws attention to discrepancies between operational performance in real-world scenarios and compliance benchmarks. Early fault detection, lifecycle validation, and continuous improvement are made possible by the QA process's transition from reactive to proactive through the integration of digital twins and predictive analytics. A strategic roadmap for QA specialists and test engineers to adjust to changing industry demands is presented in the paper's conclusion. In addition to promoting safety and dependability, the suggested framework speeds up time to market, lowers development costs, and increases consumer confidence in cutting-edge automotive technologies.
Komanduri, Arun Srinivas, Srivastava, Anuj
Totem-Pole PFC and LLC Resonant Converter based 6.6kW On-Board Charger for LEV Applications2026-26-01601/16/2026
As light electric vehicles (LEVs) gain popularity, the development of efficient and compact on-board chargers (OBCs) has become a critical area of focus in power electronics. Conventional AC-DC topologies often face challenges, including high inrush currents during startup, which can stress components and affect system reliability. Furthermore, DC-DC converters often have a limited soft-switching range under light load conditions, leading to increased switching losses and reduced efficiency. This paper proposes a novel 6.6 kW on-board charger architecture comprising a bridgeless totem-pole power factor correction (PFC) stage and an isolated LLC resonant DC-DC converter. The main contribution lies in the specific focus on enhancing startup behavior and switching performance. In PFC converters, limiting inrush current during startup is crucial, especially with fast-switching wide-bandgap devices like SiC or GaN. Conventional soft-start techniques fall short in of ensuring smooth voltage transitions. Moreover, maintaining stable operation across a universal input voltage range and achieving a high-power factor under light load conditions remain persistent challenges. Although resonant converters are widely used for their natural soft-switching ability, achieving zero voltage switching (ZVS) over a wide range of loads, especially at light load conditions, is still a technical challenge. Existing solutions rely on complex control strategies or hardware modifications, which increase cost and design complexity. The proposed architecture was modeled and simulated using MATLAB/Simscape to assess dynamic and steady-state behavior under a range of operating conditions. Results demonstrated high input power factor, line/load regulation, and switch-node waveforms to confirm ZVS operation. Additionally, the proposed charger exhibits low harmonic distortion, ensuring compliance with IEC 61000-3-2 power quality standards. These findings confirm the topology’s effectiveness for high-performance LEV charging and set a strong foundation for future experimental validation and hardware development.
Patil, Amruta, Bagade, Aniket
Nexifi11D: A Comprehensive Multidimensional Framework for Futuristic Manufacturing Ecosystems2026-26-02821/16/2026
This paper presents Nexifi11D, a simulation-driven, real-time Digital Twin framework that models and demonstrates eleven critical dimensions of a futuristic manufacturing ecosystem. Developed using Unity for 3D simulation, Python for orchestration and AI inference, Prometheus for real-time metric capture, and Grafana for dynamic visualization, the system functions both as a live testbed and a scalable industrial prototype. To handle the complexity of real-world manufacturing data, the current model uses simulation to emulate dynamic shopfloor scenarios; however, it is architected for direct integration with physical assets via industry-standard edge protocols such as MQTT, OPC UA, and RESTful APIs. This enables seamless bi-directional data flow between the factory floor and the digital environment. Nexifi11D implements 3D spatial modeling of multi-type motor flow across machines and conveyors; 4D machine state transitions (idle, processing, waiting, downtime); 5D operational cost breakdowns covering electricity, tooling, labour, coolant, and depreciation; 6D AI/ML-based failure prediction using temperature and pressure inputs; 7D predictive downtime triggers based on learned thresholds; 8D sustainability analytics measuring CO₂ emissions per motor; 9D workforce optimization via virtual shift scheduling and fatigue simulation; 10D supply chain resilience through simulated part delays and buffer modeling; and 11D risk and quality management using defect simulation and risk scoring. All data are generated live and visualized through Grafana dashboards, enabling real-time monitoring of OEE, energy use, defects, and AI-based alerts. Nexifi11D establishes a unified, cyber-physical platform for intelligent, sustainable, and predictive manufacturing, making multidimensional factory optimization practically demonstrable within one connected environment.
Kumar, Rahul, Singh, Randhir
Dimensional Management in Automotive Vehicle Design & Development for Perceived Quality (PQ) Improvement in Buses2026-26-04471/16/2026
Perceived quality (PQ) is one of the most important factors in engineering signoff as well as customer delight and product improvement (feel, look & touch). The PQ is something related to feel of product in terms of gap, flushness, fitment and appearance as per the costumer perceptions and expectations. Validation of design and engineering quality with respect to perceived quality is required for overall product appearance in the eyes of prospective customers. This is equally applicable in today’s automotive bus industry along with the other customer oriented industry. In this paper we have explored the dimensional management scope in improving the PQ requirements and expectations by utilizing the dimensional variation analysis (DVA) approach. We have tried to explain the fundamentals of vehicle aggregates fitment process and impact of fitment tolerances as used in DVA model to resolve vehicle packaging issues (critical gaps & clearance variation as per expected no. of vehicles to be manufactured in future at initial stage of the vehicle design) and demonstrated the same though live case performed on Tata Motors bus project. It is further suggested to include the DVA approach in the system level DFMEA for the detection against failure modes related to bus body PQ assessment.
Singh, Vinay Kumar, Dewangan, Ved Prakash, Kumar, Rahul, Deep, Amar
Study of Real Time Impact on Automotive Cabin Air Quality due to the Various Usage Conditions in the Severely Polluted City2026-26-05311/16/2026
This study investigates the concentrations of PM2.5 and PM10 inside an automobile under real-world driving conditions, one of the most polluted cities globally. India faces severe air pollution challenges in many cities, including Delhi, which are consistently ranking among the most polluted cities in the world. Major contributors to this pollution include vehicular emissions, industrial activities, construction dust, and biomass burning. Exposure to PM2.5 and PM10 has been linked to numerous adverse health effects, including respiratory and cardiovascular diseases, aggravated asthma, decreased lung function, and premature mortality. PM2.5 particles, being smaller, can penetrate deeper into the lungs and even enter the bloodstream, causing more severe health issues. In big cities like New Delhi, long driving times exacerbate exposure to these pollutants, as commuters spend extended periods in traffic. Measurements were taken both inside and outside the vehicle to assess the real-world impact of various scenarios encountered viz. doors/windows opening e.g. at tolls, stepping in/out from car etc. The above scenarios were tested with a PM2.5 filter installed in the car. The results indicate significant variations in particulate matter concentrations in different scenarios, highlighting the importance efficient in-vehicle air quality management. This research provides valuable insights into the effectiveness of PM2.5 filters and the potential health implications for commuters in severely polluted urban environments.
Gupta, Rajat, Pimpalkar, Ankit, Patel, Abhishek, Kumar, Shubham, Joshi, Rishi, Kumar, Mukesh
Combustion Noise Quality Improvement in a Common rail Diesel Engine at Low Idle using Taguchi DOE2026-26-03291/16/2026
Noise quality at idle condition is an important factor which influences customer comfort. Modern diesel engines with stringent emission norms together with fuel economy requirements pose challenges to noise control. Common rail engine technology has advantage of precise fuel delivery and combustion control which needs optimization to achieve the conflicting requirements of noise, emission and fuel efficiency. Engine noise at low idle condition is dominated by combustion noise which depends on rate of pressure rise inside the cylinder during combustion. The important parameters which influence cylinder pressure rise are fuel injection timing, pilot injection quantity and its separation, rail pressure and EGR valve position. The study on effect of these parameters at varying levels demand large no of experiments. Taguchi design of experiments is a statistical technique which can be used to optimize these parameters by significantly reducing no of experiments needed to achieve the desired results. These five CR parameters are varied at five different levels using an L25 Taguchi orthogonal array and noise measurements are conducted. The results of experiment have indicated that rail pressure has the highest effect on noise quality with 5dBA difference between the lowest and highest level of rail pressure. The second most significant parameter is pilot quantity with 3 dBA improvement by introducing pilot injection and the quantity of pilot injection needs to be kept minimum. Main injection timing has the potential of 1dBA and EGR valve position and pilot separation has very less influence. Engine calibration is optimized based on above inputs to meet the emission requirements and with the optimized calibration noise is improved by 5dBA at low idle
P, Priyadarshan, Chavan, Amit, A, Kannanswamy, Patil, Sandeep, Chaudhari, Vishal V
Overview of Automotive Policies, Evolution of Automotive Regulations, and Striking a Balanced Approach for Harmonization2026-26-02421/16/2026
The automotive regulatory landscape in India is evolving rapidly, driven by a dynamic policy intervention by GOI, striking push for sustainable mobility, safety, technological advancements, dEnvironmentally soundeeper localization, energy self-reliance, product quality control and simplified registration process. Key regulations cover areas like vehicle safety norms, emission norms, fuel economy norms, BIS QCO, the promotion of EVs and alternative fuel vehicles, R & D roadmaps, ELVs, incentive policies and vehicle registration reforms. India has been keeping a close eye on the automotive regulatory progress in the Europe as well as other developed countries as a cornerstone for technical harmonization, cross learning, gauge benefits and economic implications. India is progressively aligning its automotive regulations with global standards, particularly with UN Regulations and GTRs, while also considering unique Indian driving and environmental conditions. This alignment is crucial for integrating into global value chains and enhancing India's competitiveness in the automotive sector. At the same time, GIO also has been taking the cognizance of ground realities regarding technological readiness, skilled workforce, supply chain resilience, infrastructure and cost competitiveness. Therefore, GOI always has been striking a balanced approach between harmonization of regulations and business implications (TCO, business sustenance and growth). This balancing act aims to foster a globally competitive automotive industry ensuring safety and environmental responsibility while nurturing the domestic market. This paper explores the various initiatives and policy reforms undertaken by GOI time to time in shaping India’s transition to a safe and sustainable mobility. This paper also upholds the commendable and remarkable actions accomplished collectively by GOI Ministries/Departments, Test agencies, OEMs for the stride towards the destination of safe and clean mobility. The regulatory information presented in the paper focuses on four and above wheeled vehicles and excludes 2/3-wheeled vehicles.
Patil, Dharmarayagouda
Advanced Defect Detection Techniques Using AI, CAD Data, and Computer Vision2026-26-06441/16/2026
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
Kulkarni, Prasad Ramesh, Sahu, Dilip, Joshi, Chandrashekhar, Khatavkar, Akshay, Poddar, Shivani, Deep, Amar
A Quality-Driven Approach to Engineering Sign-Off for Software and Robust Product Development2026-26-05811/16/2026
Automotive Product Development is a very complex process involving many functions across the organization along with the application of numerous technologies. Generally, most original equipment manufacturers follow a stage-gate process for any new product development. The increasing application of electrical and electronic systems, software and enhanced regulations focusing on overall safety of the eco-system further increases the complexity during development. This paper details the development and implementation of a comprehensive framework designed to enhance the quality and governance of the product development in the automotive industry. As the sector undergoes significant transformation, the need for structured development approach and robust oversight has become critical to success. The paper introduces a newly developed framework for Final Data Judgment (FDJ) and Engineering Sign-Off (ESO), representing a next-generation strategy towards defect free design, robust engineering quality management and product maturity assurance. This methodology emphasizes customer-centric deliverables, risk-based assessments, and technical rigor to minimize post-SOP issues and ensure alignment with end-user expectations. The approach prioritizes end-user impact by embedding rigorous quality checkpoints that align engineering outputs with customer expectations. Key elements in this framework include vehicle application-specific signoffs, critical risk identification through DFMEA, requirements management, issue management, component /vehicle level validation, comprehensive feature/function finalization, software validation and laboratory vehicle testing, Importantly, the framework integrates deliverables from critical safety standards—Functional Safety (ISO 26262) and Cybersecurity (ISO/SAE 21434)—with formal assessments embedded at designated review gateways. To further strengthen software assurance, a dedicated Software Quality Gate assessment derived using ASPICE (Automotive Software Process Improvement and Capability Determination) framework has been instituted, establishing structured criteria for software verification, traceability, and process maturity. A pivotal enabler of this approach is the digitization of FDJ/ESO processes within the Requirements Management and Design Verification Validation (RMDVV) tool, enabling centralized data governance, real-time status, and full traceability across stakeholders. This paper demonstrates how the proposed framework drives continuous improvement, elevates product reliability, and aligns with enterprise goals of zero unscheduled service visits and best-in-class customer satisfaction. By embedding stringent checkpoints and digital tools, this process fortifies governance, ensuring alignment with organizational goals to meet elevated standards for quality and reliability.
Digikar, Ashish, Pathak, Isha, Kothari, Bhushan
Achieving Full ASIL-B Level Functional Safety (FuSa) for Automotive Clusters2026-26-00081/16/2026
Integrating advanced technologies into modern vehicles has led to an increasing focus on Functional Safety (FuSa), especially for the Automotive Integrated Cluster Module (ICM) to ensure the safety of the driver and passengers. This paper highlights the need to bring certain ICM components under an Automotive Safety Integrity Level B (ASIL-B) context using Classic AUTOSAR. This paper discusses the challenges faced and the solutions implemented for achieving compliance with ISO 26262 standards along with the Classic AUTOSAR framework. We are proposing a standardized and structured methodology for the design of the components in compliance with the key safety principles, including Freedom from Interference (FFI), execution under privileged levels, and integrity verification, particularly by adopting Classic AUTOSAR frameworks. This paper also presents the Functional Safety (FuSa) goals for these components and also extend to their configuration management and updating strategies within the ICM integrating Classic AUTOSAR with Graphics libraries or tools (such as Qt or OpenGL) and deploying them over a Secure Operating System (OS) enables not only compliance with functional safety requirements but also provides a standardized foundation for communication, error handling, cybersecurity, and other essential Classic AUTOSAR services.
Singh, Iqbal, Kumar, Praveen
Comprehensive Assessment of Volatile Organic Compounds and Efficacy of Vapor Recovery System at Fuel Stations2026-26-02331/16/2026
In densely populated urban environments, fuel retail outlets represent sources of Volatile Organic Compounds (VOCs), particularly benzene, toluene, and xylene. These emissions occur during various operations including storage tank filling, underground storage, and vehicle refuelling at retail outlets. The contribution of VOC by fuel distribution infrastructure to urban VOC pollution has been adequately addressed by oil marketing companies (OMCs) by the installation of vapor recovery system which is deployed for the comprehensive capture of fugitive emissions. This study employed a novel approach at an OMC Retail Outlet in Delhi, to evaluate benzene concentrations with different operational case studies. The methodology integrated continuous ambient air monitoring system equipped with VOC analyser of Gas Chromatography – Photo Ionization Detector (GC-PID) technology alongside targeted forecourt measurements with handheld PID instrument. Benzene emissions during peak and off-peak hours, vehicle throughput, fuel sales volume, and operational activities are studied. The results demonstrated that with VRS implementation, average concentrations remained below OSHA's permissible exposure limit (1000 ppb), though maximum values periodically spiked during high-traffic periods and underground tank filling operations. Temperature variations (8-21°C), traffic density, vehicle idling time, and operational practices were identified as critical determinants of benzene concentrations. With VRS system, benzene limits are within the 15-minute Short-Term Exposure Limit (STEL) values, ambient levels occasionally exceeded National Ambient Air Quality Standards (1.567 ppb) during cooler conditions with reduced atmospheric dispersion. This case study addresses a critical finding by quantifying VOC emissions in real-world retail outlet operating conditions, establishing that properly implemented vapor recovery technology significantly reduces occupational exposure to further minimize both worker exposure and environmental emissions from fuel retail infrastructure.
Mayeen, Hafiz, Ahuja, Muskan, Kalita, Mrinmoy, Kumar, Prashant, Sithananthan, M, Arora, Ajay
Case Study: Nanocomposite-Based 3D Printing and the Implementation of Quality Management to Enhance the Production of Automotive Parts2025-36-020612/18/2025
Additive manufacturing is one of the pillars of technologies of the industry 4.0 and enables rapid prototyping, testing of new materials, and customized manufacturing of parts with personalized design. Poly(lactic acid) (PLA) is a bio-based and biodegradable polymer that is used in packaging, medical applications, and consumer goods. However, it presents low mechanical strength and thermal stability, which limits its use in automotive parts. The use of reinforcement materials such as cellulose nanofibers (CNF) aim to increase the mechanical strength and thermal stability of PLA without reducing its ecological appeal. However, the addition of nanofibers in the 3D printing process can lead to reproducibility problems and constant clogging of the extruder nozzle due to the material’s lower printability. These difficulties may restrict its application to industrial processes due to reduced productivity. To address the challenges in the production of automotive parts with PLA/CNF composites, this paper explored the implementation of a quality management tool to improve the additive manufacturing process using nanocomposite-based filaments. The Ishikawa diagram was used to understand the difficulties observed during production and based on the potential root causes an action plan was developed using 5W2H (five whys and two hows) technique. Through the implementation of the Ishikawa diagram, it was possible to identify critical areas of improvement. Through the 5W2H, specific actions were defined in the whole process, from the nanocomposite and filament manufacturing process to 3D printing parameters. Additionally, standard operating procedures (SOPs) with special routines for the maintenance of the equipment and also for continuously monitoring all the actions that were implemented along the processing stages. For future work, new quality management tools such as checklists and operation flowcharts will be implemented to guarantee better performance of nanocomposites in 3D printing manufacturing processes.
Oliveira, Vinícius, Horiuchi, Lucas Nao, Gonçalves, Ana Paula, Souza, Mariana, Polkowski, Rodrigo
Manufacturing Optimization of Automotive Coatings: Applying Lean Six Sigma to Improve Filling Line Performance2025-36-001112/18/2025
This study presents the results of applying a Lean Six Sigma-based analytical approach to optimize the manufacturing of automotive coatings, specifically in a PU primer filling process. Through production flow mapping and the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology, unplanned stoppages in the filling line were significantly reduced, addressing critical inefficiencies in automotive coating production. The research was driven by the need to enhance manufacturing productivity and ensure process reliability in the production of coatings used in the automotive sector. To achieve this, Quality Management tools, such as Pareto Analysis and the Cause-and-Effect Diagram, along with Lean Manufacturing techniques, including Kaizen Blitz, were applied. These methods facilitated the identification and mitigation of key causes of unplanned downtime, improving process efficiency and reliability. The results demonstrated a significant reduction in downtime, enhanced operational efficiency, and an increase in Overall Equipment Effectiveness (OEE). Furthermore, the implementation of Reliability-Centered Maintenance (RCM) practices contributed to process stability and improved failure prediction, ensuring higher consistency in automotive coating production. This study highlights that integrating lean methodologies with data-driven analysis is a highly effective strategy for improving manufacturing performance in the automotive industry, reducing operational costs, and strengthening supply chain resilience for automotive coating manufacturers.
Filho, William Manjud Maluf, Rodrigues, Mateus Ferreira, Carriero, Emily Amaral, Yoshimura, Sofia Lucas, Marini, Vinicius Kaster, Siqueira, Gonçalo, Alves, Marcelo Augusto Leal
Injection Molding Parameters Optimization to Enhance Mechanical Properties of Glass-Fiber Reinforced PA9T Polymer2025-36-012412/18/2025
The use of polymeric materials and polymer -based composites as alternatives to metals in conventional applications is a widely adopted strategy. These materials provide advantages in terms of processability, cost-effectiveness, and, most notably, weight reduction. This study aimed to develop and optimize the injection molding process for producing PA9T (Polyphthalamide 9T) components reinforced with varying amounts of glass fiber to achieve optimal mechanical and physical properties. To enhance mechanical performance, different glass fiber loadings were investigated. The study employed the Taguchi method with an L9 orthogonal array design. The selected variable parameters were material composition (PA9T reinforced with 30, 35, and 50 wt% glass fiber), injection pressure (1000, 1500, and 2000 bar), injection temperature (320, 330, and 340 °C), and injection speed (100, 125, and 150 mm/s). The Taguchi method was chosen because it allows for the identification of optimal process parameters and the evaluation of their influence on material properties while requiring significantly fewer experimental runs compared to a full-factorial 34 design. The materials were evaluated based on mechanical properties through tensile and flexural tests using a Universal Testing Machine. The density of the injected specimens was measured using the Archimedes principle, while fiber-matrix adhesion, porosity morphology, and fracture behavior were analyzed via scanning electron microscopy (SEM). Results indicated that the glass fiber content had the most significant influence on material strength, followed by injection pressure, temperature, and speed, in that order. The study identified 1000 bar, 320 °C, and 100 mm/s and 50 wt% glass fiber as the optimal processing parameters, ensuring that specimens produced under these conditions achieved mechanical properties such as 230 MPa stress at break and elastic modulus of 19,3 GPa.
Mendonça, Arthur S., Michelotti, Alvaro Canto, Berto, Lucas F., Salvaro, Diego B., Binder, Cristiano
Urban Mobility: A Technological Contribution to the Modeling of a Conceptual Framework in a Cyber-Physical Environment for Reducing Traffic Congestion in the Expanded Downtown Area of São Paulo2025-36-009512/18/2025
Urban mobility is one of the major challenges faced by downtown areas in cities worldwide. Understanding how to improve it is essential, as it directly impacts the quality of life of people who live and work in these regions. There is an inconsistency in the fact that vehicles are produced with high efficiency and effectiveness, yet their purpose does not align with the daily commuting needs of large city centers, especially during peak travel times. The tools used in vehicle manufacturing, such as continuous improvement, lean manufacturing, continuous flow, and the theory of constraints, have been applied to balance transportation mode options. The analyzed scenarios aim to promote sustainable development and contribute to enhancing citizens’ quality of life. This study explores the hypothesis that if the conventional unit of measurement for vehicles, typically expressed in terms of vehicle volume or flow (vehicles/hour), were replaced by a metric based on the number of people transported per square meter per hour (persons/m2/h), the resulting analyses and proposed solutions would be more effective. The research yields two main conclusions in the city of São Paulo, Brazil. First, modifying the metric through technology and conceptual modeling within a cyber-physical environment leads to a more balanced decision-making process in traffic engineering management. Second, on São Paulo’s traditional avenues (Plano de Avenidas), the number of people transported is equivalent to reducing 29.5% of cars per hour during peak hours, which in turn lowers gas emissions from motor vehicles by 64.0%, all while maintaining the same transport capacity without altering the mode of transport. This shift results in several benefits: from a social perspective, it encourages vehicle sharing, both private and collective; from an environmental perspective, it reduces pollutant emissions by promoting the use of chartered buses and ultra-compact electric shared vehicles; and from an economic perspective, it decreases travel times while enabling variable and shared pricing models for vehicles using the city’s main roads.
Mello Filho, Luiz Vicente Figueira de, Canteras, Felippe Benavente, Meyer, Yuri Alexandre, Emiliano, William Machado, Júnior, Vitor Eduardo Molina, Gabriel, João Carlos, Iano, Yuzo
Analyzing the Operation of Road Cold Chain Transportation Using Big Data in the Context of Cost Reduction, Quality Improvement, and Efficiency Enhancement2025-99-030312/17/2025
In recent years, the market size of cold chain transportation in China has been expanding, but the industry has problems such as low cold chain circulation rate, low efficiency, high damage rate, and high cost. Under the background of reducing costs and improving quality and efficiency in transportation and logistics, an index set for operational analysis covering average freight rates, daily average number of over-temperature alarm incidents, daily average driving distance, and daily average driving time was established from the perspectives of economic efficiency, quality, and efficiency. Based on data from a third-party platform, including vehicle trajectories, temperatures, speeds, and freight rates, the running situation of road cold chain transportation industry was analyzed. The analysis results show that in 2023, the average freight rate of China’s highway cold chain will rebound, the fluctuation range will significantly narrow, the standardization level of temperature control equipment will continue to improve, the over temperature alarm during transportation will be significantly improved, and the vehicle utilization efficiency and traffic efficiency will be improved.
Li, Sicong, Ye, Jing, Cao, Mengfei
Research on EBOM Scheme for Aviation Mechanical and Electrical Products2025-99-040112/10/2025
With the continuous improvement of information technology in aerospace manufacturing enterprises, the need for the integration and connection of various links in the product development process is becoming increasingly urgent. This article mainly introduces the research on BOM product structure, BOM effectiveness management, and product dataset management solutions for electromechanical products, and elaborates on the key technical content involved in detail, providing a basic capability framework for the comprehensive implementation of XBOM construction in the future.
Zhang, Dong, Zhou, Wenzao, Zhou, Huachuang
Trebro's automated sod harvester deploys motion control on multiple levels25TOFHP12_0812/1/2025
Automating harvesters started out as a necessary solution to a severe labor shortage in 1990, Trebro Manufacturing states on its website. The Billings, Montana-based manufacturer has been producing turf harvesting machines since 1999, and its automated sod harvesters and entire harvesting process feature self-driving, automated-control functions. The company's tag line, “The Future of Turf Harvesting,” refers to its position of being the first in the industry to offer automated turf harvesting products. Trebro's AutoStack 3 harvester is an automated combine for turf that steers itself while an operator monitors and performs quality control actions when needed. The harvesting process combines several automated control processes.
Metal Powder Feedstock Size ClassificationsAMS7025 (Current)11/26/2025
This specification covers particle size classifications and corresponding particle size distribution requirements for metal powder feedstock conforming to a classification.
AMS AM Additive Manufacturing Metals
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