Browse Topic: Electrical, Electronics, and Avionics

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Adaptive Neural Network Fuel Mass Correction for Structural Model Error Compensation in Diesel Engines with Variable Biodiesel Properties2026-24-0023To be published on 09/21/2026
Compression-ignition engines operating with biodiesel blends often exhibit variability in fuel properties, such as density, viscosity, and cetane number, which can lead to systematic deviations in injected fuel mass when using conventional physics-based models. These deviations can reduce combustion efficiency and increase brake-specific fuel consumption (BSFC). This study proposes a lightweight neural network–based approach to compensate for structural errors in baseline injection models, using a single-layer perceptron trained on the relative error (delta) between actual and modeled injected mass. By normalizing engine and fuel parameters and introducing a small amount of measurement noise, the network learns to predict a corrective factor that adapts the injected mass to match the desired target under varying fuel conditions. Simulation results demonstrate that the neural correction significantly reduces systematic bias: in test cases with intentionally introduced structural error
Gutierrez, Marcos, Taco, Diana
Neural-Network-Enhanced Air-Standard Thermodynamic Modeling for Spark-Ignition Engines Fueled with Low Heating Value Alternatives2026-24-0016To be published on 09/21/2026
This study presents a computational framework that integrates an air-standard thermodynamic engine model with artificial neural networks to predict the performance of spark-ignition (SI) engines operating with alternative fuels of reduced lower heating value (LHV). A deterministic thermodynamic simulator was developed in Excel, incorporating engine geometric parameters (compression ratio, bore, stroke, displacement), operating speed, and fuel properties, with particular emphasis on LHV as the dominant energetic descriptor. The model computes in-cylinder states, thermal efficiency, indicated mean effective pressure, and power output under idealized air-standard assumptions. To extend predictive capability beyond fixed-parameter analyses, a feedforward neural network was trained using datasets generated from systematic parametric sweeps of engine geometry, speed, and fuel LHV. The neural network captures nonlinear interactions between compression ratio, combustion energy release, and
Gutierrez, Marcos, Taco, Diana
Computational Electro-Thermal and Degradation Modeling Framework for Lithium-Ion Batteries in Electric Vehicles2026-24-0011To be published on 09/21/2026
This paper presents a mathematical modeling framework for lithium-ion batteries used in electric vehicles (EVs). The model is implemented using Python. At the cell level, the model describes charge and discharge behavior, state of charge (SOC), terminal voltage, internal resistance losses, and heat generation. An energy balance equation is used to estimate temperature variation during operation. Temperature-dependent resistance and capacity are included to represent nonlinear battery behavior under different load conditions. At the system level, feedback relationships between SOC, temperature, state of health (SOH), and degradation rate are modeled using system dynamics. Battery aging is represented through mathematical functions that relate capacity loss to temperature and usage cycles. This allows simulation of long-term performance under different driving scenarios. The model enables parametric and sensitivity analyses to evaluate the effects of discharge rate, ambient temperature
Gutierrez, Marcos, Taco, Diana
Cost and Emission aware strategic optimization: a fleet-level tool to support scenario-oriented design2026-24-0034To be published on 09/21/2026
The transition toward low-emission transport systems requires not only technologically optimized Battery Electric Vehicles (BEVs) but also integrated methodologies capable of supporting industrial stakeholders throughout the deployment phase. In particular, for logistics operators, fleet sizing and charging infrastructure planning are tightly coupled with vehicle configuration and mission scheduling. Therefore, decision-support tools are required to minimize total operational costs and environmental impact while ensuring service continuity. Building upon a previously developed two-level BEV design framework, this work introduces a higher-level optimization tool aimed at extending powertrain design outcomes toward fleet-level decision-making, providing an integrated methodology capable of determining not only the optimal vehicle configuration but also the optimal number of vehicles and charging stations required to satisfy operational scheduling constraints. The proposed tool performs
Bartolucci, Lorenzo, Cennamo, Edoardo, Cordiner, Stefano, Donnini, Marco, Grattarola, Federico, Lombardi, Simone, Mulone, Vincenzo, Tribioli, Laura
How micromobility is reshaping urban mobility: a systematic literature review2026-24-0025To be published on 09/21/2026
Micromobility is rapidly reshaping urban mobility by transforming travel behaviour, urban space, and transport systems. Its growing role in reducing car dependency and supporting low-carbon mobility has positioned cycling, e-scooters, and e-bikes as key components of sustainable urban transport. The aim of the study is to investigate how micromobility is reshaping urban mobility, particularly travel behaviour, urban mobility policy, urban infrastructure, and public transport integration. This study employs a systematic literature review (SLR) to provide a structured and transparent framework for synthesising existing research, identifying trends, and mapping knowledge gaps across multiple dimensions of urban mobility[MO1.1]. It combines inductive and deductive reasoning to ensure the review is both theoretically grounded and responsive to emerging patterns in the literature. The findings reveal that the most impactful areas of micromobility development are demand and changes in travel
Olkhova, Mariia, Comi, Antonio
EKF-Based Machine Learning Approach for Battery SOH Prediction in Commercial Vehicles Fleet2026-24-0002To be published on 09/21/2026
Estimating battery State of Health (SOH) from field data is essential to ensure successful operation and to increase the uptime of battery electric vehicles (BEVs). Most studies in the literature have proposed several methods for battery SOH estimation, relying on datasets acquired under controlled laboratory conditions. However, SOH estimation becomes significantly more challenging when dealing with real-world driving data due to the increased variability and complexity of operating conditions. In this work, CAN telematics data, sampled at 1 Hz, have been collected across approximately 16 months of operation from 10 electric commercial vehicles. During this period, a battery degradation of up to 2.5 - 3% is observed within the fleet. Firstly, a model-based framework is introduced, in which a battery second-order Equivalent Circuit Model (ECM) has been coupled with an Extended Kalman Filter (EKF) to estimate the battery SOH. The results confirmed that the EKF accurately captures the
D'Agostino, Valerio, Pulvirenti, Luca, Shanker, Anirudh, Cardone, Massimo, Rizzoni, Giorgio, Vitale, Francesco
Simulation-Based Development and Evaluation of Heat-Quantity-Based Thermal Operating Strategies for Automotive Applications2026-24-0012To be published on 09/21/2026
Efficient and robust thermal management is a key prerequisite for reproducible vehicle testing and energy-efficient powertrain operation. Conventional thermal operating strategies are commonly based on temperature setpoints and often neglect the dynamic relationship between heat input, system inertia and transient operating conditions, leading to increased energy demand and limited reproducibility under highly dynamic driving cycles. This paper presents a simulation-based development and evaluation of a heat-quantity-based thermal operating strategy for automotive applications. Instead of controlling the system solely via temperature targets, the proposed approach utilizes accumulated and instantaneous heat input as central decision variables for thermal control, explicitly accounting for thermal system dynamics. The investigation is conducted using a detailed vehicle simulation model including a physically-based thermal system representation. An innovative decentralized thermal
Stenger, Erik, Fiore, Luis, Weimer, Niko, Beidl, Christian
Life Cycle Assessment of Greenhouse Gas Emissions from a Plug-in Hybrid Electric Vehicle with Different Powertrain Configurations in Europe2026-24-0019To be published on 09/21/2026
Plug in hybrid electric vehicles play an important role in transportation decarbonization. Compared with battery electric vehicles, plug in hybrid electric vehicles generally have a lower production carbon footprint due to their smaller batteries, which require far less raw material. Despite their smaller capacity, these batteries are typically sufficient to cover most daily travel distances in pure electric mode. The hybrid powertrain can be configured in multiple ways depending on the number and position of electric machines within the driveline. These configuration differences significantly influence both the total carbon footprint and the use phase greenhouse gas emissions. In this study, we evaluate the life cycle greenhouse gas emissions of a plug-in hybrid electric vehicle with various powertrain configurations in the European context. All configurations share the same premium mid-size sport utility vehicle glider. Battery capacity ranges from 20 kWh to 45 kWh, enabling an
Nguyen, Duc-Khanh, Andersson, Simon, Kristoffersson, Annika
Characterisation of Brake Wear PM2.5 and PN10 Emissions Across Pad/Disc Technologies, Vehicle Types and Braking Behaviours, Under Real World Conditions2026-24-0022To be published on 09/21/2026
This paper summarises targeted brake emissions investigations undertaken as part of the Department for Transport’s Non Exhaust Emissions programme. The work was carried out to improve understanding of particulate mass and particle number emissions from friction braking and to assess the influence of component selection, vehicle technology, operating conditions, and control measures on those emissions. Starting from a pre-developed brake enclosure and sampling system methodology, these were further refined to improve airflow management, reduce leakage, and minimise sampling artefacts. The updated system was integrated with a suite of particulate measurement instruments, including MPEC (hot and cold), APC10, DMS500, and eFilter, enabling simultaneous measurement of volatile and non volatile PN10 plus PM2.5. A matrix of nine brake pad formulations and two disc types was tested on a common C segment vehicle platform across chassis dyno and on road cycles, together with controlled braking
Andersson, Jon
Fuel Cell Electric Vehicle-to-Home Strategy for Residential Power Supply Continuity and Load Management2026-24-0026To be published on 09/21/2026
Power supply continuity for household appliances during high-demand peaks or periods of low solar generation remains a critical challenge for modern residential energy systems. Hydrogen-powered Fuel Cell Electric Vehicles (FCEVs) offer a compelling solution by extending their role from sustainable mobility to versatile mobile energy storage units. This study investigates the Vehicle-to-Home (V2H) operation of a high-performance fuel cell vehicle designed to provide backup power and peak-shaving capabilities in a domestic environment during high-demand or grid overload conditions. To evaluate this integration, a comprehensive MATLAB/Simulink simulation model was developed for a residential microgrid. The system comprises a photovoltaic array, a parallel battery energy storage system, and a Fuel Cell Electric Vehicle modeled according to the technical specifications of the Toyota Mirai, equipped with a high-efficiency 128 kW fuel cell stack (330 cells) and a dedicated 5.6 kg hydrogen
Federici, Leonardo, Pistritto, Antonino, Cozzolino, Raffaello
Real-Time Road Intelligence through a Hazard Authentication Model2026-24-0027To be published on 09/21/2026
Advanced Driver Assistance Systems (ADAS) are evolving beyond onboard perception. The ability to dynamically map and share temporary road hazards is important for connected and autonomous driving, but authenticating the data is critical. An in-vehicle hazard recognition layer performs real-time video analysis and geotagging on embedded platforms. Real-time video is analyzed for road hazards—such as potholes, construction zones, waterlogging, fallen trees, roadside accidents, and debris—using onboard cameras and lightweight computer vision models. This metadata is sent to a cloud-based aggregation layer to validate hazard reports. It is of paramount importance to validate hazard reports originating from diverse sources, regardless of their accuracy. This paper presents a mathematical approach to determine an overall Hazard Confidence Score (HCS) based on data received from diverse sources. A unique hazard authentication model is introduced to quantify the credibility of each hazard
Bose, Souvik
Business approach to cope with automotive market uncertainty2026-24-0024To be published on 09/21/2026
The global automotive industry is facing an unprecedented convergence of uncertainties driven by geopolitical tensions, evolving trade policies, emissions related regulations, and increasingly volatile consumer demand. Shifting emissions legislation, including the EU’s tightened CO2 targets and long term plans to phase out internal combustion engines, is imposing strategic and financial pressures on automakers and suppliers as they navigate divergent regional regulatory trajectories. Demand side volatility further complicates the landscape. Consumer preferences are fluctuating due to economic pressures, infrastructure constraints, and uneven EV adoption patterns. While some markets show stagnation in battery electric vehicle uptake, hybrids are rising as consumers seek cost efficient alternatives amid uncertain energy and regulatory environments. Within this unstable context, the transition toward Software Defined Vehicles (SDVs) is emerging as a critical strategic response. SDVs
Cavanna, Filippo, Potenza, Luca
Comparative Analysis of BEV Performance from Simulated Environment and Physical Hardware Platform2026-24-0005To be published on 09/21/2026
The proliferation of simulation environments has accelerated technological progress across various scientific domains by offering a cost-effective and time-efficient framework for data acquisition and analysis. In the automotive sector, high-fidelity modeling of vehicle components and driving scenarios bypasses the logistical constraints associated with hardware procurement and the intensive requirements of large-scale testing infrastructures. However, pre-calibrated or native software models often imply simplified hypotheses, missing relevant aspects of the entire powertrain-to-wheel energy chain. This study presents a comparative analysis of battery performance within a battery electric vehicle (BEV) by synchronizing virtual simulations with experimental hardware at the test bench. The methodology involves the concurrent modeling of the driving environment, the vehicle chassis, and the propulsion system, followed by the execution of identical driving cycles on a physical platform
Sequino, Luigi, Sementa, Paolo, Altieri, Nunzio, Vaglieco, Bianca Maria, Sorrentino, Chiara
Experimental Comparison Between Real and Simulated Battery Pack Efficiency in Light Quadricycles Using a Custom Inertia Dynamometer2026-24-0008To be published on 09/21/2026
This study compares the energy efficiency of a real battery pack with that of a simulated battery pack using a hardware battery simulator, through experimental testing on a dedicated inertia dynamometer designed for light quadricycles. The real battery pack under investigation consists of lithium-ion cells with a nominal voltage of 48 V, a nominal capacity of 100 Ah, and a total energy content of 4.8 kWh. The pack is equipped with a Battery Management System (BMS) that monitors voltage, current, temperature, and State of Charge (SOC). The battery simulator was configured to replicate the same electrical specifications, including the OCV–SOC curve, internal resistance, and current limits, enabling a direct comparison between theoretical and experimental behavior. Experimental tests were conducted on a custom inertia test bench developed to overcome the limitations of conventional chassis dynamometers when testing light quadricycles, characterized by low mass, limited power output, and
Sementa, Paolo, Vaglieco, Bianca Maria, Altieri, Nunzio
Architecting Safety-Critical Systems for AI-Enabled Software-Defined Vehicles: A Compute, Timing, and Isolation Framework with a Brake System Case Study2026-01-0811To be published on 09/14/2026
Software-defined vehicle (SDV) platforms are reshaping safety-critical system design by consolidating braking and other motion-control functions on centralized heterogeneous edge compute that also executes physical-AI workloads. This consolidation breaks traditional assumptions of fixed ECUs and simple timing envelopes, complicating assurance of determinism, isolation, and fail-operational behavior for ASIL-D brake functions. Building on a decentralized brake-by-wire (BbW) architecture with dual controllers, redundant low-voltage power grids, and smart electromechanical brake corner actuators, this paper proposes a systems-level framework for architecting safety-critical functions in AI-enabled SDVs along three dimensions: compute, timing, and isolation. The framework classifies conventional and AI-based functions and maps them to heterogeneous compute classes; defines architectural patterns that combine safety islands, power-domain redundancy, and hardware partitioning to support
srinivasaraghavan, soumyasudharsan
Real-time Estimation of Brake Temperature using Physics-Informed Machine Learning Model2026-01-0810To be published on 09/14/2026
The current work presents a novel approach to estimate brake surface temperature in real time to aid in brake wear prognostics. Brake prognostics involve estimating the brake pad wear in real-time that helps in its predictive maintenance. Brakes are a safety critical system for vehicles, therefore, demands accurate and robust pad wear estimation, ensuring vehicle safety. However, it involves several challenges. The estimation of pad wear is fundamentally a two-stage process: the first stage involves the accurate prediction of brake pad surface temperature, while the second stage utilizes this thermal history to calculate cumulative material wear. A significant challenge in estimating brake pad wear without expensive sensor is that it is sensitive to the surface temperature prediction; any error in the thermal model propagates and compounds in the wear prediction stage. To identify surface temperature, traditional physical sensors are often cost-prohibitive or prone to failure in the
Gannavarapu, Shivadath, Pal, Anuj, Fan, Mengdi
Advanced Brake Force Distribution on trailers with Bendix Electronic Brake Controller2026-01-0797To be published on 09/14/2026
Commercial vehicle fleets frequently operate with tractors that connect to different trailers and dollies, resulting in combinations with varying brake pad wear across wheel‑ends. Traditional brake force distribution strategies whether proportioned, electronic, or load‑adaptive do not account for these pad life differences. This can lead to uneven brake utilization, irregular maintenance intervals, and increased total cost of ownership (TCO), especially in mixed‑fleet and multi‑trailer operations. This work introduces an advanced brake force distribution function, a mechatronic brake control concept that incorporates brake pad health information into supervisory brake force allocation. In the proposed architecture, the tractor is equipped with a Brake Control ECU, which collects brake pad life status from each trailer or dolly through Pad Wear Sensing ECUs over the Tractor‑Trailer CAN (TT‑CAN) network. The Pad Wear Sensing ECUs report pad wear information only and do not host braking
Ganesha, Vinodkumar
Modeling Brake Torque Hysteresis for Chassis Controls Hardware In the Loop Simulation2026-01-0824To be published on 09/14/2026
It is hardly a new trend for on road, vehicle intensive tuning and testing of chassis control features such as Anti-Lock Brakes, Traction Control, and Electronic Stability Control to move away from vehicle testing and towards non-vehicle test platforms such as Hardware-In the Loop (HIL) simulations and even further into pure math-based simulations. However, a significant acceleration of these activities has been occurring recently in the automotive industry, reducing or eliminating calibration time on vehicles and amplifying the demand for highly representative, non-vehicle test platforms to calibrate and even to validate chassis controls features. In current state of the air HIL simulation, the input (brake pressure) to output (brake torque) of each wheel brake in a vehicle’s brake system is modeled relatively simplistically, including at most pressure and brake temperature sensitivities, usually in lookup table form. Each brake corner contains over 20 different friction interfaces
Antanaitis, David, Ridenour, Nick, Miller, Bryan, Karnjate, Timothy
Time-Series Friction Torque Prediction for Drum Brake Systems via Mixed-Effects Random Forest Framework2026-01-0809To be published on 09/14/2026
The application of drum brake systems is expanding due to increasing cost competitiveness requirements in EV and purpose-built vehicle markets, as well as the introduction of EURO-7 particulate emission regulations. In spite of this trend, drum brake friction behavior is not yet fully understood because it is governed by complex and strongly coupled complicating mechanisms, including temperature history, braking conditions, and interactions between drum brake components. To address this gap, this study presents a method for developing a time-series friction torque prediction model using the Mixed-effects Random Forest (MERF) machine learning framework. Time-series data collected from sensors during drum brake dynamometer tests were analyzed to identify the key variables that govern the friction torque. Significant inputs were selected through Exploratory Data Analysis (EDA), considering test-to-test variability and potential mixed effects, and were then used to train and tune the MERF
Yoon, Jungro, Cho, Sunghyun, Kim, Wonjoon
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-0806To be published on 09/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake by- wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly
Rai, Prakhar, Gadhvi, Tirth
Predicting Brake Squeal Occurrence Using Machine Learning with Temporal Feature Analysis of Braking-Torque Test Data2026-01-0812To be published on 09/14/2026
The automotive industry's paradigm shift toward autonomous driving and electrification has introduced new competitors threatening market dominance through differentiated value propositions. In this highly competitive landscape, delivering irreplaceable customer value requires providing sustainable and authentic luxury experiences. Quiet driving represents a tangible value that customers genuinely appreciate. Brake squeal—high-frequency noise arising from torque-induced friction behavior during braking—negatively impacts customer satisfaction and must be suppressed. Despite significant advances in brake squeal prediction modeling, the irregular nature of squeal generation mechanisms has prevented development of a generalized predictive model applicable to product development processes. Development and verification remain largely experimental. This limitation constrains early-phase design validation, as brake squeal is highly sensitive to chassis and braking system design. When squeal
Cho, Sunghyun, Yoon, Jungro, Kim, Yoon Cheol, Kim, Jeongkyu, KIM, SUNGHO, baek, SongYi, KIM, Won Joon, CHOI, KYUNG ROK
Safe State Definition and Verification for Distributed Multi-Controller EMB Architectures2026-01-0800To be published on 09/14/2026
The Electro-Mechanical Brake (EMB) system is an essential technology for safe braking in modern vehicles. However, the adoption of multi-controller architectures has introduced new challenges to conventional Safe State strategies. Traditionally, the Safe State defined in functional safety means "function shutdown," and in accordance with ISO 26262 definition, aims for an "operational mode without risks exceeding reasonable levels." However, in the multi-controller architecture of EMB systems, the Fail-Operational Safe State concept is applied, where the system continues to provide limited functions even in the event of faults. It is essential to verify whether such operational modes actually satisfy the safety requirements of ISO 26262. This paper redefines the Safe State according to failure modes in EMB systems, analyzes system state transitions, and presents a coherence analysis methodology for validating the availability of resources required to provide limited functions in the
KIM, KANG SAN
Machine Learning Prediction of Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing2026-01-0808To be published on 09/14/2026
Brake pad lifespan significantly influences environmental pollution and public health due to non-exhaust particulate emissions. Global vehicle production reached approximately 7 billion units in 2025, with brake systems generating an estimated 50,000 to 100,000 metric tons of wear debris annually, underscoring the need for improved durability and material optimization. This study investigates the capability of a Fully Connected Neural Network (FCNN) to predict brake pad wear and support formulation optimization. Controlled experiments were performed using a simplified FMVSS 135 protocol on a Universal Mechanical Tester (UMT) to simulate realistic braking conditions. Wear loss was measured under varying normal loads, sliding velocities, and interface temperatures. An FCNN was selected for its ability to capture nonlinear interactions between operating conditions and tribological responses. Despite a limited but high-quality dataset, the application of data standardization
Katakam, Abhishek, Eslamiat, Hossein, Kancharla, Sai Krishna, Filip, Peter
Data to Decisions: An AI-Driven Test Intelligence Platform for Integrated Analysis of Raw Data, Test Reports, and Specification Compliance in Brake System Validation2026-01-0807To be published on 09/14/2026
Validation of brake systems is increasing in complexity due to electrification, software-defined architecture, integrated control modules, and higher functional safety requirements. Although physical testing remains the primary source of engineering evidence, interpretation of results including DVP&R/PVP&R compliance verification, anomaly detection, documentation, and milestone decision support continues to rely heavily on manual engineering analysis. This results in extended feedback cycles, inconsistent interpretation across teams, and limited traceability between raw data, reports, and governing specifications. To support engineers with more objective validation processes, there is a growing need for structured, data-driven intelligence that transforms dispersed test artifacts into actionable engineering decisions. This paper presents Data to Decisions, an AI-driven Test Intelligence Platform designed for integrated analysis of raw measurement data, test reports, validation plans
Divakaruni, Saikiran, Willey, Joseph, Srivastava, Namrata, Sankar, Arya, Namala, Divya, Gowtham, Rahul Sangani
A Study on TSC(Technical Safety Concept) design and verification in EMB(Brake By Wire) Systems2026-01-0801To be published on 09/14/2026
The Electro-Mechanical Brake (EMB) system, a dry-type Brake-by-Wire technology, eliminates hydraulic components by directly controlling friction braking through electrical actuators at each wheel. The architecture includes a Main Center Control Unit, a Backup Center Control Unit for redundancy, and four-Wheel Control Units, communicating via CAN FD. Compliance with ISO 26262 functional safety standards is critical due to the EMB system’s role in vehicle braking. As complexity increases, risks such as hardware failures and communication faults must be systematically addressed. This paper presents the design and verification of a Technical Safety Concept (TSC) for EMB systems in accordance with ISO 26262. Safety goals and Functional Safety Requirements derived from HARA analysis guide the System Architecture and TSC development. Key design principles include modularity, redundancy, fault detection, and fail-safe mechanisms. Verification is performed at both system and vehicle levels
KIM, Dokun
Bridging Human Expertise and Machine Intelligence in Chassis System Teardown Evaluation2026-01-0805To be published on 09/14/2026
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
Divakaruni, Saikiran, Vaibhav, Veer, hansen, Scott, Hood, Trevor, Agrawal, Rahul
A Study on the Development of Brake Moan for CTBA Vehicles Using Simulation2026-01-0814To be published on 09/14/2026
Not only are customers' expectations for the development of various products, but also the demand for emotional quality improvement of the products is steadily increasing. Brake NVH, which is generated during vehicle braking, has a lot of influence on the emotional aspects of customers. There are several brake NVH such as judder, squeal, and moan. First, judder is phenomena of handle, brake pedal vibration and noise that occur during braking at the high-speed. The quality of judder has been improved a lot by applying judder detection and compensation logic in internal combustion engines and the heat load on the disc is reduced by regenerative braking in electric vehicles. So the judder problem hardly occurs these days. A squeal is a noise generated during braking at a low speed. The squeal is solved by tuning and shape change of a friction material in the high frequency band and by making the robust systems design such as caliper and disc in the low frequency band. Moan noise is low
KIM, SUNGHO, Kim, Jeongkyu, HWANG, JAEKEUN, Kang, Donghoon
Parameter Identification for the Dynamics Model of a Robotic Grasping Process Based on SFOA2026-99-0123To be published on 07/31/2026
To precisely simulate the nonlinear dynamic characteristics of a robotic arm grasping cylindrical objects from storage units, this study establishes a dynamic model of the robotic grasping process incorporating Coulomb and viscous friction models to characterize frictional properties. Furthermore, to effectively identify unknown parameters in the dynamic model, a parameter identification method based on the Superb Fairy-wren Optimization Algorithm (SFOA) is proposed. The root-mean-square error (RMSE) between the displacement responses from the dynamic model and the experimentally acquired displacement data serves as the optimization objective. Multiple sets of experimental data are utilized to identify the unknown parameters of the dynamic model. The results demonstrate that when the identified parameters are applied to the dynamic model, the goodness-of-fit between the model’s response displacement data and the experimental displacement data exceeds 0.999. This validates the
Shen, Shaofeng, Yang, Liu, Wang, Zihan, Han, Qunyi
Design and Research of Robotic Arm for Belt Conveyor Roller Replacement2026-99-0187To be published on 07/31/2026
To address the high failure rate of rollers in coal mine belt conveyors, the inefficiency of manual replacement, and the operational disruptions caused by maintenance shutdowns, this study proposes a robotic arm system capable of replacing rollers without halting conveyor operations. The research focuses on the kinematic performance and path planning strategy of the robotic arm. A kinematic model is established using the Denavit–Hartenberg (DH) parameters, and the workspace distribution is analyzed via the Monte Carlo method. The results show that the horizontal reach exceeds 2020 mm and the vertical reach extends up to 2000 mm, which fully satisfies the spatial requirements for roller replacement across the entire conveyor system. In the path planning phase, an obstacle expansion model is constructed, and an improved Informed RRT* algorithm is implemented to generate collision-free trajectories, ensuring effective obstacle avoidance. To improve trajectory smoothness, path pruning and
Pu, Congyuan, Qian, Ke
Microstructure and Mechanical Properties of Vacuum Laser Welded Joints in 42CrMo Thick Plate2026-99-0132To be published on 07/31/2026
Vacuum laser welding trials were carried out on 42CrMo steel, a material widely utilized in the defense sector. By employing a 30 kW fiber laser system, complete penetration welds were successfully produced on 20 mm thick 42CrMo steel plates. The resulting joints displayed satisfactory surface quality on both the top and bottom sides, with no evident defects such as cracks or porosity. A comprehensive analysis of the joint microstructure and mechanical properties was conducted. Findings reveal that the weld zone (WZ) is predominantly composed of lath martensite, accompanied by minor quantities of plate martensite, organized as columnar crystals. The joints demonstrated high tensile strength at ambient temperature, with fracture consistently occurring within the base metal (BM). Microhardness measurements indicated higher values within the weld relative to the base metal, and no pronounced softening was detected in the heat-affected zone (HAZ). Additionally, the joints exhibited
Shi, Haicheng, Zhang, Guoyu, Li, Wuhong, Cao, Dongxu, Liu, Tianlei
Design and Development of a Durability Test Device for the Surface of Elderly-Friendly Mattresses2026-99-0139To be published on 07/31/2026
A test device for detecting the durability of the surface of elderly-friendly mattresses was designed and developed, which has functions such as force value monitoring, displacement monitoring, data recording, and hardness grade determination. Through the collaborative work of the mechanical system and the control system, high-precision reciprocating rolling tests and hardness grade determination on the mattress surface are realized. The verification test results show that the relative standard deviation (RSD) value of the mattress hardness grade test results is less than 10%, indicating that the detection data obtained by using this device is stable, meets the design requirements, and has operability.
Wang, Jin, Feng, Panpan, Shen, Guofeng, Zhang, Lei
Pseudo-Label-Guided Dual-Supervised Alignment for Bearing Fault Diagnosis2026-99-0233To be published on 07/31/2026
The inconsistency in bearing data distributions under diverse conditions often affects the representations of the faulty data and leads to indistinct decision boundaries and even negative transfer resulted from overlapping class distributions, greatly limiting the accuracy of the diagnosis model. To cope with the challenge, a pseudo-label-guided dual-supervised alignment (PDSA) method is developed for bearing fault diagnosis across diverse operating scenarios in this paper. To address the fixed alignment strategy issue, an adaptive distribution alignment layer is incorporated to ResNet18 to achieve dynamic data distribution alignment under varying condition, To enhance classification performances, a dual-supervised mechanism, comprising shallow-layer supervised contrastive learning is introduced through target domain pseudo-labels in target domain and deep-layer regularization class consistency. Experiments on two publicly available bearing datasets demonstrated this model realizes
Sun, Hao, Ren, Shijin, Gu, Zhangqing
Research on the Influence of Creping Process and Microstructure on the Properties of Crepe Paper Products2026-99-0206To be published on 07/31/2026
Crepe paper has extensive applications in the electrical field and significantly influences the operation of power equipment. The creping process and microstructure play a crucial role in determining its performance. However, optimizing them to improve the performance of crepe paper remains a challenge. Therefore, in this study, univariate and multi - factor interaction experiments were set up to explore the impact of the creping process on crepe paper. X - ray diffraction (XRD) and Fourier - transform infrared spectroscopy (FTIR) techniques were used to analyze the microstructure of crepe paper. The results show that smaller scraper angles and moderate pressures can increase the paper density, and the use of different creping aids can improve the paper’s performance. Higher crystallinity enables crepe paper to have better mechanical and thermal stability. Moreover, based on the experimental results, a scheme for optimizing process parameters was proposed to help improve the quality of
Meng, Gao, Ran, Zhuo, Yuan, La, Zengchao, Wang, Bin, Zhang
Simulation Research on Non-Destructive Testing of Pipeline Based on Ultrasonic Guided Wave2026-99-0143To be published on 07/31/2026
Ultrasonic guided waves enable long-range, low-intrusion inspection of pipelines. This study examines how array topology and axial spacing influence the quality of defect echoes when the longitudinal axisymmetric mode L(0,2) is used. We build COMSOL finite-element models of a steel pipe and excite it with PZT-4 at 80 kHz; three practical layouts are compared: (i) odd–even receiving, (ii) 8-transmit/8-receive, and (iii) 16-transmit/8-receive, arranged as two axially separated groups. The spacing between the groups is chosen to suppress parasitic modes such as L(0,1) and to strengthen L(0,2). Results show that the two-group configuration sharpens the defect echo and reduces modal interference; increasing the number of transmitters further raises the defect-wave amplitude and improves the separation from end-reflection echoes. Among the schemes, 8×8 performs well for small-defect identification, while 16×8 yields the clearest boundaries and fastest defect indication. These findings
Liao, Wei, Li, Tengfei, Zhang, Wenhui, Lin, Qingming, Guo, Yanbing
Experimental Study on Suppression of Lithium-Ion Battery Fires Using Clean and Efficient Aqueous Fire Extinguishing Agents2026-99-0133To be published on 07/31/2026
This study investigates the suppression of lithium-ion battery (LIB) fires using composite aqueous extinguishing agents, with a 20 Ah lithium iron phosphate battery as the experimental subject. Based on the functional selection of coverage-isolation and cooling-smothering, three aqueous extinguishing agents, S-E-1, S-E-2, and S-E-3, were designed and developed using hydrocarbon surfactants. The results indicate that all three aqueous fire extinguishing agents can effectively suppress LIB fires. Through a comprehensive evaluation of extinguishing time, cooling rate during extinguishing agent release, and physical and chemical parameters of the extinguishing agents, the optimal formulation was determined to be 2% SDS (sodium dodecyl sulfate), 1% SDBS (sodium dodecyl benzene sulfonate), 3.5% CAB (cocamidopropyl betaine), 1% APG0810 (alkyl polyglycoside), 1% CDEA (coconut oil acid diethanolamine), 0.32% CH4N2O (urea), 1.5% NH4H2PO4 (ammonium dihydrogen phosphate), 0.5% C2H6O2 (glycol), 1.5
Yu, Tao, Zhu, Shunbing, Li, Ke, Zhang, Menglan
Optimization of Pneumatic Conveying Elbows to Reduce Grain Particle Normal Contact Forces via CFD-DEM Coupling Method2026-99-0116To be published on 07/31/2026
This study presents a refined design for pneumatic conveying pipelines, featuring a grooved structure at the bend aimed at reducing particle breakage during transportation. Using soybean particles as a focus, the research employs a gas-solid two-phase flow approach to explore how different groove depths and widths influence the breakage rate. We used CFD-DEM simulation techniques, combining fluid mechanics with discrete element modeling to achieve a more accurate representation of particle motion and collision forces during expressing. Based on these simulations, we identified the most effective combination of groove width and spacing. Experimental results showed that a groove width of 4.5 mm coupled with a 40 mm spacing could decrease impact forces on particles by approximately 5% to 10% at expressing speeds of 15 m/s and 20 m/s. Throughout all measured time intervals, the impact forces remained stable, with turbulence exerting minimal influence on the particle forces.
Luo, Xinhao, Yang, Tiancheng, Huang, Bo, Mao, Genwu, Dong, Deliang, Shi, Heng, Li, Xiaoliang, He, Bo
Construction of a Digital Management Platform for Intelligent Workshops in the Electronics Industry2026-99-0117To be published on 07/31/2026
Under China’s intelligent manufacturing strategy, manufacturing enterprises are expected to achieve digital and networked operations by 2025, with full digital transformation by 2030. Intelligent factories, the core of this transformation, rely on interconnected, integrated, and data-fused systems. This paper focuses on the micro-assembly intelligent workshop at the Nanjing Research Institute of Electronics Technology, which produces micro-circuit modules for large-scale complex electronic systems. The workshop combines discrete and process manufacturing modes, presenting unique challenges for digital management. A digital management platform based on a five-layer architecture (device, network, data, application, and decision layers) is proposed to address multi-dimensional business needs, including production scheduling, logistics, execution, and decision optimization. A hierarchical workflow structure of the workshop, consisting of a main workflow and several sub-processes, is in
Zhang, Jian, Wang, Jiafeng, Guo, Yongzhao
A Data-Driven Method for Predicting The Operational Status of The Aircraft Assembly System2026-99-0111To be published on 07/31/2026
Aircraft assembly systems, as a critical phase in aerospace manufacturing, face significant challenges in maintaining production efficiency and ensuring product quality. This complex manufacturing system exhibits two distinct characteristics: (1) tightly coupled interactions among manufacturing elements involving process sequences, material flows, and equipment utilization; and (2) dynamic resource allocation and material distribution plans. The inherent variability in production element configurations often leads to operational instability and schedule deviations, which may result in abnormal production states. To address these challenges, this study proposes a data-driven predictive framework that integrates Long Short-Term Memory (LSTM) neural networks with multi-criteria evaluation. The developed LSTM-based model effectively forecasts two critical production indicators of cycle time and balance rate, achieving temporal prediction through historical operational data analysis. The
Chen, Bolin, Wu, Junjie, Sun, Jinfeng, Wang, Kai
Surrogate Model Study for Curvilinear Fibre Variable Stiffness Composite Laminates: Based on Dynamic Hybrid Sampling and Adaptive Surrogate Model Hyper-Parameters Co-Optimization Strategy2026-99-0241To be published on 07/31/2026
Variable stiffness composite laminates with curvilinear fibres have demonstrated significant capability in lightweight structural design, particularly regarding buckling resistance and stiffness enhancement. However, directly applying optimization algorithms often faces challenges such as high computational cost and slow convergence during the optimization design process. Consequently, the incorporation of surrogate models prior to employing optimization algorithms is necessary to simplify computations and accelerate convergence. Manual testing is a conventional approach for hyper-parameter (HP) tuning and continues to be widely used in research. However, manual tuning is suboptimal and time-consuming for many problems. Additionally, the effectiveness of these surrogate models largely depends on the training samples. Therefore, a dynamic hybrid sampling and adaptive surrogate model HP co-optimization strategy is proposed for the optimization design of the variable stiffness composite
Chen, Dengnuo, Zou, Rui, Chen, Binqi
Durability Study of Electronic-grade Glass Fibers in Salt-Alkali Environments2026-99-0219To be published on 07/31/2026
Electronic substrates and copper-clad laminates are widely used in modern life, particularly in electronic products such as coastal communication base stations and ship communication and navigation system. In these environments, salt particles carried by sea fog can adhere to the surface of the substrates along with moisture, leading to salt-alkali corrosion of the products. As a core material in electronic components, electronic-grade glass fiber urgently requires investigation into its durability under salt-alkali conditions. Therefore, this study focuses on a specific type of electronic-grade glass fiber and explores its corrosion behavior in three different environments: 2 mol/L NaOH, 1 mol/L cement solution, and 3.5 % NaCl. Soaking durations of 6, 12, 24 hours and 3, 7, and 14days were selected as key parameters. The mechanical properties and surface morphology of the fibers before and after corrosion were observed and analyzed. Experimental results indicate that the glass fiber
Pu, Qixin, Sun, Siqi, Fang, Qiang, Dong, Shuo, Li, Peng, Wang, Yu, Zhang, Mengxuan, Zhang, Yubo, Yang, Wenfeng, Guo, Peng
Research on Data-Driven Intelligent Monitoring and Prediction System for Fatigue Damage in Metallurgical Cranes2026-99-0216To be published on 07/31/2026
Metallurgical cranes have a high risk of structural fatigue damage and failure under complex working conditions such as high temperature, heavy load, and strong electromagnetic interference. This article proposes a data-driven structural fatigue damage health monitoring system. This system integrates fiber Bragg grating sensing technology, rigid flexible coupling multi-body dynamics simulation, and big data analysis methods to construct a sensor optimization layout strategy based on rigid flexible coupling virtual prototype simulation, achieving real-time perception of stress states in key parts such as the mid span and end beam corners of the main beam. Develop a visualization system that integrates health monitoring, damage diagnosis, and life prediction. This system can dynamically evaluate the structural health status of metallurgical cranes and predict the remaining life of the structure based on a nonlinear cumulative damage model. On site engineering applications have shown that
Chen, Li, Zhang, Xu, Ding, Keqin
Design and Optimization of Hollow Rotating Traveling Wave Ultrasonic Motor Based on Laser Beam Expander2026-99-0154To be published on 07/31/2026
To meet the critical need for rapid response and miniaturization in laser beam expander drive systems, this study proposes an innovative actuation solution based on a hollow rotary traveling-wave ultrasonic motor. By thoroughly analyzing the optical adjustment mechanism of laser beam expanders and the electromechanical coupling behavior of ultrasonic motors, the motor structure was systematically optimized. Using a multiphysics coupling approach, the performance of stators fabricated from three distinct materials was compared, and parametric optimization was conducted. Experimental verification confirms that the developed ultrasonic motor precisely matches the load characteristics of beam-expanding optics while fulfilling the stringent requirements for both fast response and compact design. This research provides a reference for the miniaturization drive of high-precision optical systems, with promising applications in space optics and precision instrumentation.
Qiu, Haihui, Niu, Chuanhu, Xiao, Zhong, Xu, Zhangfan, Li, Jialiang, Pan, Song
Stewart Platform Position and Attitude Error Compensation Based on Improved Particle Swarm Optimization2026-99-0104To be published on 07/31/2026
The six-degree-of-freedom Stewart platform, as a high-precision parallel robot, is widely used in fields such as aerospace and precision manufacturing. However, its complex structure and diverse sources of error (such as manufacturing errors, assembly errors, rod deformation, etc.) make it difficult to effectively control position and attitude errors. This article proposes a Stewart platform position and attitude error compensation method, relying on the improved particle swarm optimization algorithm. By establishing a position and attitude error model for the platform and optimizing the driving joint error using the IPSO optimization, the position and attitude error of the platform have been significantly reduced, providing a new solution for error compensation of high-precision parallel robots.
Zhu, Ming, Wang, Baichao
Research on Machining Deformation Simulation and Prediction of Low-Stiffness Structural Components under Cutting Force2026-99-0237To be published on 07/31/2026
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
Zhao, Yongsheng, Gao, Pengfei, Xu, Jingjing, Liu, Zhifeng
A New Ternary Phase Diagram Prediction Descriptor Combined with Machine Learning Research2026-99-0180To be published on 07/31/2026
The morphological characteristics of ternary phase diagrams play a pivotal role in optimizing material properties and facilitating the design of novel alloys. In this study, machine learning (ML) is used to predict the number of phases in ternary alloy systems. A new feature descriptor for phase diagram prediction is proposed in ML, which includes the characteristics of element properties, thermodynamic properties of materials and CALPHAD parameters. Initially, this study constructed a dataset comprising various feature descriptors and validated their correctness employing ML models such as LRC, SVM, RFC, Bagging and GBDT. Subsequently, comparing the performance of different models, and the better-performing models Bagging and GBDT were selected for further prediction studies. The models were fine-tuned using grid search and random search methods to optimize their predictive performance. Ultimately, by predicting phase diagram data for multiple ternary systems at different temperatures
Fan, Hanchao, Su, Yu, Jin, Zongxiao, Li, Jun, Lee, Soowohn, Tang, Jianguo, Fu, Huaqing, Du, Zhi
An Automatic Cleaning System for Copper Bar Chain of Cigarette Cutting Machine2026-99-0131To be published on 07/31/2026
The cutting machine is a critical component in the cigarette processing line, and its cutting quality depends on the operational condition of the copper bar chain. The grooves on the surface of the copper bar chain accumulate dust during the operation of the machine, which often causes unstable conveyance of raw materials, significant width variations, and a high defect rate. To address this issue, this study developed a linear reciprocating automatic cleaning system for copper bar chains to remove dust from the groove surface and hinge joints. Experiments have verified that the system improved the cutting qualification rate, increased the operational stability of the cutting machine, reduced manual cleaning workloads, and reached higher cleaning efficiency. This innovative system is also expected to provide a valuable reference for similar equipment manufacturers and advance technological innovation in the cigarette processing industry.
Li, Haitao, Zhang, Chunyuan, Fang, Junqing, Su, Lin, Chen, Peng, Guo, Zhiwei, Xing, Dongdong
Micro-Groove Fabrication and Simulation Analysis on PEEK Surface under UV Nanosecond Laser Irradiation2026-99-0225To be published on 07/31/2026
This study investigates the interaction mechanism between ultraviolet nanosecond pulsed lasers and polyetheretherketone (PEEK). By integrating finite element simulations with experimental validation, the work explores the laser microtexturing characteristics of PEEK surfaces and evaluates the influence of microtextures on the material’s surface biocompatibility. First, the interaction between the laser and the PEEK polymer was analyzed, and a laser ablation model was established using the COMSOL Multiphysics simulation platform. Using finite element simulation, the effects of spot overlap ratio were investigated by adjusting the average laser power, while the influence of single-pulse energy on the ablation characteristics of the PEEK surface was examined by varying the scanning speed. Subsequently, ultraviolet nanosecond laser processing experiments were conducted on planar PEEK microtextures based on the simulation results. Taking surface microgrooves on PEEK as representative
Wu, Yifan, Wang, Xiaohui, Han, Yujie, Jin, Shuo
Design and Control of a Target Plate Fastener Assembly and Disassembly Actuator for Divertor Maintenance in Fusion Reactors2026-99-0182To be published on 07/31/2026
With the development of controlled nuclear fusion technology, the tokamak device, as the most promising magnetic confinement fusion reactor for advanced engineering applications, requires remote maintenance of its internal components, which has become a key factor affecting both operational efficiency and safety. As a critical component directly exposed to high-temperature plasma, the divertor target plate needs to be periodically replaced and carefully maintained to ensure stable and reliable reactor operation. However, this region is subject to extreme conditions, including high temperature, high vacuum, and intense radiation, making conventional manual maintenance infeasible. This necessitates the development of intelligent and automated teleoperation systems. To address the automated assembly and disassembly requirements of divertor target plates, this study designs an integrated target plate actuator comprising key functional units: a positioning module, a screwing module, a quick
Zang, Xizhe, Yu, Xingzu, Cao, Zhangbin
Structural Design and Simulation Study of Carbon Fiber Electronic Enclosures for Vehicles2026-99-0160To be published on 07/31/2026
Carbon Fiber Reinforced Polymer (CFRP), as an advanced lightweight structural material, exhibits significant application potential in the protection of electronic devices under extreme vehicle-mounted conditions due to its excellent specific strength and specific stiffness, superior energy absorption capacity, and unique damping and vibration reduction properties. This study closely integrates the characteristics of the complex and variable service environment in vehicles and adheres to the principle of equivalent stiffness matching to conduct innovative design explorations for electronic enclosures made of CFRP. To comprehensively evaluate the dynamic strength performance of CFRP vehicle-mounted enclosures under vibration and impact conditions, in-depth and detailed analyses were conducted using ANSYS software to simulate the power spectral density curves of random vibrations and the loading of post-peak sawtooth waveforms. The results indicate that the carbon fiber enclosure not only
Zhang, Shuhui, Ma, Qihua
Research and Application of Low-Cost Aerospace Chip Burn-in Systems2026-99-0158To be published on 07/31/2026
In view of the key problems—low chip burn-in efficiency and high burn-in costs—caused by high R&D costs and a limited number of veneer stations in the traditional burn-in system used in the military aerospace field, this project has carried out a series of innovative research. Through systematic scheme optimization design and strict cost control measures, a new burn-in system with significant cost advantages and supporting multi-station parallel processing has been successfully developed for the aerospace field. The core technical breakthroughs of the system are mainly reflected in three aspects: first, through architectural reconstruction, the number of single incubator stations has been increased by leaps and bounds from the traditional 60 to 720; secondly, the use of intelligent monitoring technology can expand the scale of the workstation while using the display for process monitoring and data collection; Finally, the modular design concept is innovatively introduced, which greatly
Gu, Zucheng, Kang, Xiao, Jiang, Shang
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