Browse Topic: Data acquisition and handling

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Dynamic Stiffness Prediction in Cracked Cantilever Beams Using Enhanced ANN Techniques2026-28-0050To be published on 02/01/2026
This research paper presents an in-depth investigation into the application of Artificial Neural Networks (ANN) for predicting the dynamic stiffness characteristics of cantilever beams affected by cracks of varying depths and positions. Structural failure due to vibration remains a critical concern, and this study focuses on how crack-induced changes in modal stiffness compromise structural reliability. A significant research gap identified in previous studies is the limited consideration of crack location; this work addresses that gap by analyzing how both crack depth and position influence the natural frequency of the beam. Using modal analysis on cantilever beams with identical dimensions but different crack configurations, it was observed that deeper cracks and cracks near the fixed support led to a marked reduction in natural frequency, making the structure more vulnerable to resonance. Cracks located at the free end, however, showed negligible impact. To model and predict these
SB, Harshini, Rajkumar, Manjari, R, Krithika, K, Anusha, K, Divya, Bhaskara Rao, Lokavarapu
TOPSIS-Based Multi-Criteria Optimization of WAAM Parameters for Automotive Component Fabrication2026-28-0026To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) can make huge, complicated metal parts quickly and cheaply, making it popular in the automobile sector. This study uses TOPSIS, a multi-criteria decision-making method, to improve WAAM process parameters such wire feed rate, travel speed, and current. Automotive applications need surface roughness, deposition rate, and dimensional precision, which WAAM-fabricated components must meet. TOPSIS normalizes and ranks experimental findings to find parameter combinations that perform best across many quality metrics. The results show that integrated optimization improves component quality and consistency, making WAAM a feasible and economical sustainable vehicle manufacturing option.
Pasupuleti, Thejasree
Effect of Minimum Quantity Lubrication during Machining of Incoloy 800H Using Vegetable Oil Enriched With Nano Particles2026-28-0038To be published on 02/01/2026
In this experimental work an effort has made to investigate the machining of difficult to machine Incoloy 800H super alloy. Cryogenically treated textured cutting inserts and vegetable oil enriched with nano particles were used for this study. The turning experiments were performed with different parameters such as Cutting speed, feed rate and depth of cut. Surface Roughness, Cutting Force, Tool-Tip Temperature and Power were the output responses considered. Grey Relational Analysis (GRA) and Analysis of Variance (ANOVA) analysis were used to optimize the machining parameters and to find the most influencing machining parameter on the output responses. The results showed that the optimal machining conditions considered were cutting speed at 35 m/min, feed rate at 0.06 mm/rev and depth of cut at 0.5 mm for the multiple-characteristic performances and it was improved by 3.86 % with this approached method. Also, Tool-tip temperature and Power were reduced during machining with cryo
Natarajan, Manikandan
Experimental Investigation of Process Parameter Effects in WAAM of Nickel-Based Alloys2026-28-0027To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) is an evolving discipline in metal additive manufacturing characterized by rapid deposition, cost-effectiveness, and centrality to the near-net-shape fabrication of large-scale components. This study will concentrate on WAAM application to deposit nickel alloys, which find extensive use in aerospace, power generation, and marine industries, due to their good mechanical strength, corrosion resistance, and thermal stability. However, some drawbacks in WAAM processing of nickel alloys include thermal distortion, porosity, and residual stress. The research looks at how key WAAM process parameters, including wire feed rate, travel speed, current, and interpass temperature affect geometric accuracy, surface integrity, and mechanical performance of the fabricated components. Microstructural characterization was done using optical microscopy, and hardness distribution was measured as an indication of mechanical uniformity throughout the builds. It is
Natarajan, Manikandan
AI-Driven Process Optimization in Wire Arc Additive Manufacturing of Nickel Alloys Using ANFIS2026-28-0025To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) is gaining significance in recent years as a process for additive manufacturing that produces large and complex components with high deposition rates at economically feasible rates. Nickel-based alloys, praised for their unique features, like mechanical strength, corrosion resistance, and thermal stability, find widespread application in the aerospace, marine, and energy sectors. However, achieving the desired surface finish, dimensional precision, and mechanical performance of WAAM nickel alloy structures is a continuing challenge because of the complex interactions occurring between the process parameters. The study presents an experimental analysis conducted to establish the effect of important process parameters of WAAM-wire feed rate, travel speed, welding current, interpass temperature-on the quality indicators such as surface roughness, dimensional accuracy, and hardness. For the purpose of prediction and process control, the Adaptive Neuro
Natarajan, Manikandan
Parameter Optimization for Improved Surface Finish in WAAM-Fabricated Steel Structures2026-28-0023To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) is a cost-effective and scalable metal additive manufacturing technique widely used for fabricating large metallic components. However, surface finish remains a critical challenge, especially for structural applications where post-processing needs to be minimized. This study focuses on optimizing key process parameters to improve the surface finish of ER70S-6 steel structures fabricated using the WAAM process. Experiments were designed using a statistical approach to systematically vary parameters such as wire feed rate, travel speed, voltage, and inter-pass cooling time. Surface roughness was measured using standard metrology tools, and the results were analyzed to identify the most influential parameters. An optimized parameter set was proposed based on the experimental outcomes, leading to a significant reduction in surface roughness without compromising the structural integrity of the deposited material. The findings contribute to enhancing
PN, Vani
Effect of Sebastiana rostrata Fiber Loading on the Tribological Behaviour of Polycaprolactone Biocomposite.2026-28-0021To be published on 02/01/2026
This study investigates the tribological performance of Sesbania rostrata fibers (SRFs) reinforced polycaprolactone (PCL) composites, focusing on the effects of fiber loading under varying normal loads. Composite samples with fiber contents of 10%, 20%, and 30% were tested at sliding velocities of 0.5 m/s and a fixed sliding distance of 500 m. Results show that the inclusion of SRFs significantly reduces the coefficient of friction (COF) and specific wear rate (SWR) compared to neat PCL, particularly at fiber loadings up to 20%. Beyond this threshold, fiber agglomeration leads to increased wear. The results highlight the potential of SRF/PCL composites for use in wear-critical automotive and orthopedic applications due to improved load-bearing capability and surface resistance.
Raja, K., Senthil Kumar, M.S.
Statistical Modeling of Wire Arc Additive Manufacturing Parameters Using Regression Analysis2026-28-0024To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) is gaining prominence for fabricating large metal components due to its high deposition rate and cost-effectiveness. However, the quality and performance of WAAM-fabricated parts are strongly influenced by process parameters such as wire feed rate, travel speed, arc voltage, and current. This study presents a statistical modeling approach using regression analysis to predict the effects of these key parameters on critical output responses, including bead geometry, surface roughness, and material deposition rate. Experiments were designed using a structured methodology, and the collected data were analyzed to develop linear and multiple regression models. The accuracy and reliability of the models were evaluated using statistical metrics such as R² and p-values. The results demonstrate that regression-based models can effectively predict WAAM outcomes, enabling process optimization and quality control. This approach provides a valuable tool for
Pasupuleti, Thejasree
Statistical Optimization of Wire Arc Additive Manufacturing Parameters Using Taguchi Methodology2026-28-0035To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) is an emerging metal additive manufacturing process known for its high deposition rates and cost-effectiveness in fabricating large-scale components. However, the quality of the final build is highly sensitive to process parameters such as wire feed rate, travel speed, voltage, and current. In this study, the Taguchi method is employed to statistically optimize key WAAM process parameters with the aim of improving surface finish and mechanical integrity of the deposited parts. An L9 orthogonal array was used to design experiments, and signal-to-noise (S/N) ratios were analyzed to identify optimal parameter settings. Analysis of variance (ANOVA) was performed to determine the significance of each factor on the selected output responses. The results demonstrated that specific combinations of process parameters can significantly reduce surface roughness and enhance bead geometry. The optimized settings obtained through the Taguchi approach provide a
PN, Vani
Evolution of a Novel Hybrid Stab-Link for a Born Electric Sports Utility Vehicle2026-28-0001To be published on 02/01/2026
The present study details the design evolution and failure analysis of a novel hybrid stabilizer bar link (stab link) developed for the front suspension of a born electric sports utility vehicle (SUV) platform characterized by higher gross vehicle weight (GVW), increased wheel travel, and constrained packaging space. To address these challenges, a unique hybrid stab link was designed featuring dual plastic housings at both the metal ball joint ends, connected by a steel tube, and achieving a 30% weight reduction while offering enhanced articulation angles for extremely lower turning circle diameter (TCD) of the vehicle, compared to the conventional stab link. The unique hybrid stab failed under complex loading conditions during accelerated durability testing (ADT), prompting a comprehensive investigation. The failure analysis included road load data acquisition across various stab bar diameter configurations evolved during suspension tuning, different stabilizer link designs evolved
SELVENDIRAN, P, J, Ramkumar, NAYAK, Bhargav, M, Sudhan, Patnala, Avinash
Parameter Selection Strategies for High-Performance WAAM Structures in Automotive Engineering2026-28-0033To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) has gained traction as an advanced fabrication technique for producing large, structurally demanding components in the automotive industry. Achieving high-performance WAAM structures requires precise control and selection of key process parameters, which directly influence mechanical strength, dimensional accuracy, and surface quality. This study investigates the effect of critical process variables—including wire feed rate, travel speed, current, voltage, and inter-layer cooling time—on the structural integrity of WAAM-fabricated components designed for automotive use. A systematic experimental approach, supported by statistical modeling and optimization tools, was employed to identify parameter combinations that yield optimal results. The outcomes demonstrate improved microstructural uniformity, reduced residual stresses, and enhanced tensile properties in the fabricated parts. The findings serve as a foundation for developing robust parameter
V, Aravind
Multi-Objective Optimization of WAAM for Sustainable Production of Automotive-Grade Components2026-28-0030To be published on 02/01/2026
Wire Arc Additive Manufacturing (WAAM) has emerged as a promising technology for fabricating large-scale metal components with reduced material waste and lead time, aligning with the goals of sustainable manufacturing. This study presents a multi-objective optimization approach for WAAM to enhance both the manufacturing efficiency and sustainability of automotive-grade components. Key process parameters such as travel speed, wire feed rate, and current were systematically varied to evaluate their impact on dimensional accuracy, mechanical performance, and energy consumption. A statistical design of experiments framework was employed, and multiple performance objectives were optimized simultaneously using suitable decision-making techniques. The results demonstrate that optimized WAAM settings not only meet structural and functional requirements for automotive applications but also contribute to material and energy savings. This approach supports the integration of sustainability into
Pasupuleti, Thejasree
Historical Memory-Based Database Management System for Effective ADAS Configuration in Autonomous and Connected Vehicles2026-28-0057To be published on 02/01/2026
The ability to store data is crucial for an autonomous vehicle system. Memorizing traffic signs, including their locations, can be easier than trying to read and understand them on the fly. Using the vehicle's location and other vehicle data, decisions can be made faster when using existing memory data. This study outlines a technique that utilizes an environmental structure database as a tool for decision-making in diverse vehicle scenarios. By collectively learning and storing vehicle location, historical data, and past actions, this paper results in a way to predict the environmental structure in advance and use the necessary control signals to optimize vehicle operation. In a connected vehicle system, there can be many reasons and factors to be considered in various events such as vehicle speed, parking location, speed changes, speed bumps, braking, etc. These are stored in a separate database, and the accuracy of the results is increased by comparing the actual data at that time
Santhiyagu, Arulanantha Samy
Multi-Band Frequency Approach to Correlate Proving Ground and Road Simulator for an Electric Two-Wheeler2026-26-0556To be published on 01/16/2026
Real-world usage subjects two-wheelers to complex and varying dynamic loads, necessitating early-stage durability validation to ensure robust product development. Conducting a full life-cycle durability testing on proving grounds is time-consuming, extremely difficult for the riders involved, and costly, which is why accelerated testing using rigs such as the road simulator system have become a preferred approach. The use of road simulators necessitates, accurately measured inputs and precise simulation to ensure proper actuation of the rig, thereby enabling realistic representation of road undulations. Road Load Data Acquisition (RLDA) is used to measure various inputs such as displacement, acceleration, strain, and predicted forces from the road which are then used to simulate real-world conditions in a road simulator. This paper covers two important aspects essential for achieving an accurate and clear representation of road simulation in a 4-DOF road simulator, encompassing both
Ganju, Shubham, V, Vijayamirtharaj, Prasad, Sathish, R S, Mahenthran
Quantifying the Energy Consumption of HVAC Operation and Its Impact on the Range of Electric Buses Across Diverse Climatic Conditions2026-26-0204To be published on 01/16/2026
Electric buses (e-buses) are essential to sustainable public transport, but their real-world efficiency and range are heavily affected by auxiliary systems, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This study investigates how ambient temperature variations and HVAC loads influence energy consumption, range, and efficiency in e-buses operating under diverse climatic conditions. The methodology combines field data collection from urban e-buses across seasons—including extreme summer and winter—with controlled lab testing. Field measurements included ambient temperature, HVAC demand, vehicle speed, state of charge (SOC) variation, and energy consumption. These inputs were used to develop real-world duty cycles, replicating actual thermal loads, passenger profiles, idling periods, and driving patterns. In the lab, these cycles were simulated using a chassis dynamometer and environmental chamber, with HVAC systems tested at controlled ambient temperatures
Vishe, Prashant, Dalela, Saurabh, Joshi, Madhusudan, Saraswat, Shubham
A Novel Approach to Optimal Strain Gauge Placement for Structural Assessment: Integrating Close Looping Configuration with Calibration Techniques2026-26-0551To be published on 01/16/2026
Addressing the challenge of optimal strain gauge placement on complex structural joints and pipes, this research introduces a novel methodology combining strategic gauge configurations with numerical optimization techniques. Traditional methods often struggle to accurately capture combined loading states and real-world complexities, leading to measurement errors and flawed structural assessments. For intricate joints, a looping strain gauge configuration is proposed to comprehensively capture both bending and torsional effects, preventing the bypassing of applied loads. A calibration technique is used to create strain distribution matrices and access structural behavior under different loading conditions. Optimization algorithms are then applied to identify gauge placements that yield well-conditioned matrices, minimizing measurement errors and enhancing data reliability. This approach offers a cost-effective solution by reducing the number of gauges required for accurate stress
Shingate, Uttam, Yadav, Dnyaneshwar, Deshpande, Onkar
Multi-Model Analysis of O-Ring Compression and Relaxation Force Predictions Using Machine Learning and CAE Simulations2026-26-0466To be published on 01/16/2026
O-rings play a critical role in ensuring leak-proof seals in a wide range of engineering systems. Accurate prediction of their compression and relaxation behavior under various material and geometric configurations is essential for optimal design and reliability. This study presents an analysis of machine learning techniques to predict two key performance outputs, compression force and relaxation force (after 10 minutes) trained on computer-aided engineering (CAE) simulation data. The experimental setup was represented in CAE simulation and the results were compared with experimental data conducted at ZF test facilities. Simulation results correlated well with the experimental data (deviation was less than the 5%). To create a dataset for training machine learning (ML) models, realistic ranges for the input parameters such as hardness and geometrical parameters were determined, and simulation data were generated using design of experiments (DOE). Multiple ML models were developed and
Kosgi, Durgaprasad, Alva, P Pancham, Dangeti, VenkataKrishna Pavan
High-Performance Computing for Software-Defined EV Powertrains: Design and Real-Time Validation with NXP S32E278 Domain Controller2026-26-0684To be published on 01/16/2026
Software-Defined Vehicles (SDVs) are redefining the automotive landscape by decoupling hardware from software and enabling dynamic, over-the-air updates, advanced control strategies, and real-time decision-making. To support this transformation, EV powertrain systems require high-performance computing (HPC) platforms capable of real-time control, data processing, and cross-domain communication. This paper introduces a fully SDV-compatible EV powertrain architecture built around the NXP S32E278 domain controller, which integrates a multi-core, lockstep-enabled processor specifically designed for zonal control and automotive functional safety. To meet the bandwidth and latency demands of modern SDV applications, the system replaces conventional CAN-based communication with automotive Ethernet, enabling gigabit data transfer, Time-Sensitive Networking (TSN), and low-latency coordination across software-defined control domains. The S32E executes critical functions such as field-oriented
Pawar, Ganesh, Inamdar, Sumer Deepak, Kumar, Mayank, Deosarkar, Pankaj, Tayade, Nikhil, Kanse, Dattatray, Chopade, Vipul
A Dynamic Cybersecurity Benchmarking Framework for Automotive Cyber-Physical Systems2026-26-0612To be published on 01/16/2026
As the automotive industry accelerates towards greater connectivity and automation, ensuring robust cybersecurity has become a critical priority, especially in emerging markets like India. This paper introduces a comprehensive framework designed to assess and enhance the cybersecurity of production-ready vehicles, with a particular focus on addressing the unique challenges posed by both domestic and international markets. The proposed system combines advanced threat modeling, vulnerability assessment, and dynamic evaluation techniques to continuously monitor and quantify a vehicle’s cybersecurity posture throughout its lifecycle. In the Indian context, where the automotive market is rapidly evolving and the adoption of connected and automated vehicles is on the rise, the need for tailored cybersecurity measures is particularly pressing. By evaluating the impact of cyber threats on safety, data privacy, financial risks, and vehicle functionality, the system generates a detailed
Shah, Ravindra, Awasthi, Vibhu Vaibhav, Karle, Ujjwala
Navigating the Field of Automotive Cybersecurity Relevant Regulations2026-26-0623To be published on 01/16/2026
In recent years many automotive cybersecurity relevant regulations have been released and some have already started to come into effect. Moreover, some other regulations will come into effect in the next few years. These regulations provide requirements and guidance to automotive organizations with different degree of specifics. In this paper, we review a number of different cybersecurity relevant regulations such as UNR 155, UNR 156, AIS 189, AIS 190, GB 44495, GB 44496, EU Cyber Resilience Act, and BIS Final Rule. We break down and categorize these regulations based on their scope and highlight key areas relevant to different teams within the organizations. These key areas include Cybersecurity Management System (CSMS), Software Update Management System (SUMS), secure software development and software supply chain security, continuous cybersecurity activities (monitoring, incident response), and vulnerability disclosure and management. We then map responsibilities from the
Oka, Dennis Kengo, Vadamalu, Raja Sangili
Addressing Safety Belt Discomfort Among Female Driving Population Through Data Collection and Simulation2026-26-0016To be published on 01/16/2026
The rate of female drivers in India is increasing alongside the rapid growth of the Indian automotive industry. A driving comfort survey conducted among female drivers revealed that many experiences dis-comfort when wearing Safety belts-whether while driving or as front-seat passengers. This discomfort is primarily due to a phenomenon referred to as "neck cutting." The root cause of neck cutting is likely related to vehicle design, which is traditionally based on Anthropometric Test Devices (ATD's) representing the 5th, 50th & 95th percentile (%tile) of the global population. However, a literature review indicated that the anthropometric dimensions of the Indian populations are generally smaller than those of the global for the respective candidate. This highlighted the need to collect region-specific anthropometric data for India. To validate the neck-cutting issue, various female candidates were asked to sit in the Driver's seat for physical measurements trials. Accordingly
Kulkarni, Nachiket A, Chitodkar, Vivek V, Eknath Chopade, Santosh, Mahajan, Rahul, Yamgar, Babasaheb S
Considerations for Vulnerability Management in the Automotive Industry2026-26-0626To be published on 01/16/2026
With the emergence of Software-Defined Vehicles (SDVs), more complex software and connectivity technologies are introduced to support new advanced use cases such as phone as a key, smart parking and vehicle management. However, complex software functionality and external connectivity also increase the attack surface of vehicles and its ecosystem. In this paper, we first perform a classification of recent automotive cybersecurity attacks. We further perform an analysis of these attacks and associated vulnerabilities considering the application of best practices of vulnerability management approaches including Common Vulnerability Scoring System (CVSS), Exploit Prediction Scoring System (EPSS), and Stakeholder-Specific Vulnerability Categorization (SSVC). CVSS is a standardized framework used to assign severity scores to known vulnerabilities and helps organizations prioritize vulnerability remediation based on severity. EPSS is a predictive model that estimates the probability of a
Oka, Dennis Kengo, Vadamalu, Raja Sangili
Generative AI based methodology for Road Profile Mix Creation2026-26-0674To be published on 01/16/2026
The acquisition of road profile data is crucial for various automotive testing applications, including vehicle dynamics analysis, chassis endurance tests, and simulation of vehicle-road interactions. This is necessary for conducting virtual tests to accelerate research and development processes and can significantly reduce testing costs. However, most of the on-road measurements lack comprehensive and relevant road profile data. Conducting on-road trials to acquire this data is a laborious and time-consuming process, often impeded by logistical and environmental challenges. This research proposes a generative AI-based methodology for creating diverse and realistic road profile mixes from the existing on-road dataset of front axle displacement and road profile measured with a laser sensor. By leveraging advanced machine learning techniques, the proposed approach seeks to generate synthetic road profiles that accurately reflect real-world conditions, thereby reducing the dependency on
Rajappan, Dinesh Kumar, Venkatesh, Anirudh Anand, R, Suresh, N, Gopi Kannan
Legislative Framework and Emission Testing using PEMS for NRMM (Non-Road Mobile Machinery) in India: A Technological Perspective2026-26-0139To be published on 01/16/2026
Emission Regulations for NRMM in India have evolved significantly over past two decades. India has progressively adopted stricter standards to align with global best practices. The latest regulations have introduced stringent caps on particulate matter (PM), nitrogen oxides (NOx), and other pollutants, ensuring compliance with environmental sustainability goals. Future legislative frameworks are expected to impose even more rigorous emission limits, focusing on further reduction of NOx and PM levels while incorporating real-world emission monitoring. This will require manufacturers to integrate advanced after-treatment systems and adopt cleaner combustion technologies to meet compliance standards. To validate compliance with these stringent limits, rigorous testing methodologies are employed. Portable Emission Measurement Systems (PEMS) have become a crucial tool for real-world emission assessment. PEMS technology allows for on-road and field testing of NRMM under actual operating
Rastogi, Aadhar, Garg, Varun, Ragot, Nicolas
RAKSH: A Blockchain Derived In-Vehicle Key Management System2026-26-0620To be published on 01/16/2026
Modern cars have advanced significantly with the rapid growth of connectivity and communication technologies. In the wake of rising cyberattacks and enforcement of regulations, implementation of cybersecurity is imperative to safeguard vehicles. The cybersecurity controls such as secure boot, secure updates, secure communication require cryptographic primitives (keys/certificates). These security features are largely dependent on robust Key Management System (KMS), as keys are the sensitive assets that must be protected throughout the lifecycle of vehicle. Several security critical applications like over-the-air and car-to-car interaction essentially needs robust KMS to protect the vehicle assets from expanding attack vector. Traditionally KMS is established centrally in a backend server. The cloud based KMS is becoming complex due to increased number of keys/certificates required to provision in a vehicle. We propose a self-governing in-vehicle key management system for a gateway
Goyal, Yogendra, Sutar, Swapnil, Jaisingh, Sanjay
Securing the Future of Electric Vehicle Charging: Identifying and Mitigating Cybersecurity Vulnerabilities2026-26-0614To be published on 01/16/2026
This paper examines the key security vulnerabilities present in modern EV charging infrastructure, with a focus on the potential threats to payment systems, communication protocols, physical security, and data privacy. In particular, it looks at the vulnerabilities associated with unencrypted communication, the risks of unauthorized access to charging stations, and the potential for denial-of-service (DoS) attacks. As the global adoption of electric vehicles (EVs) accelerates, the importance of a secure and reliable EV charging infrastructure becomes paramount. However, the expansion of charging stations and the growing integration of smart technologies introduce significant cybersecurity risks that must be addressed to ensure the safety, privacy, and reliability of the network. In addition to discussing emerging risks in relation to new technologies, such as Vehicle-to-Grid (V2G) systems, and outlining the necessity of industry-wide collaboration and the development of standardized
Aggarwal, Akshit, Gupta, Saurabh, Sirohi, Kapil, Arisetty, Venkatesh, Chatterjee, Avik
MOSAIC-TARA: A Comprehensive TARA Methodology for Automotive Cybersecurity2026-26-0609To be published on 01/16/2026
Threat Analysis and Risk Assessment (TARA) is a continuous activity, acting as a foundation of cybersecurity analysis for electrical and electronics automotive products. Existing TARA methodologies in the automotive domain exhibits challenges due to redundant and manual processes, particularly in handling recurring common assets across Electronic Control Units (ECUs) and functional domains. Two primary approaches observed for performing TARA are Manual-Asset-Centric TARA and Catalogue-Driven TARA. Manual-Asset Centric TARA is constructed from scratch by manually identifying the assets, calculating risks by likelihood, and impact determination. Catalogue-Driven TARA utilizes the precompiled likelihood and impact against identified assets. Both approaches lack standardized and modular mechanisms for abstraction and reuse. This results in poor scalability, increased efforts, and difficulty in maintaining consistency across vehicle platforms. The proposed method in this research overcomes
Goyal, Yogendra, Sinha, Swati, Sutar, Swapnil, Jaisingh, Sanjay
Cybersecurity threats for connected passenger EV Buses of state transport units in India.2026-26-0610To be published on 01/16/2026
There are several chances to enhance fleet management, passenger safety, and operational efficiency by integrating connected technology into electric passenger buses, especially in Indian state transport agencies. But this connectivity also brings with it a fresh set of cybersecurity threats that might jeopardize these cars' dependability and security. With an emphasis on India's state-run bus fleets, this study examines the cybersecurity risks unique to connected electric vehicles (EVs) utilized for public transportation. Vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and fleet management communication systems are examined for key vulnerabilities. Potential attack vectors are highlighted, including data interception, GPS spoofing, unauthorized access, and cyberattacks on charging stations. We also look at how these cybersecurity threats affect public safety, operational disruptions, and passenger privacy. This paper also assesses the difficulties Indian state transport
Mokhare, Devendra Ashok
Enhancing Engine Reliability Testing with IoT, AI and ML: Real-Time ML-based Limit Monitoring and Predictive Maintenance with IoT sensor2026-26-0647To be published on 01/16/2026
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT, AI and ML technologies can greatly enhance the efficiency of engine reliability testing. The paper includes the following case studies: 1. ML-based Limit Monitoring for test data: With the help of machine learning models, we monitor the Engine health by comparing predicted and measured values during real-time testing. Intelligent algorithms predict and compare values, detect anomalies, and identify root causes in real-time
Yadav, Sanjay Kumar, kumar, prabhakar, R, Dinesh, Joon, Sushant, Rai, Ayush, Tripathi, Vinay Mani
Unlocking Edge AI in Software-Defined Vehicles: Balancing model complexity, compute resources, and data access2026-26-0686To be published on 01/16/2026
The potential of Edge AI in automotive goes beyond ADAS and automated driving. AI-driven optimizations in driving efficiency, in-cabin personalization, predictive maintenance, and enhanced safety require intelligent processing at the vehicle edge. However, integrating AI into current vehicle electrical/electronic architectures (EEAs) presents significant challenges. This paper examines how AI models can run on general-purpose ECUs, gateways, and domain controllers while balancing model complexity, compute resources, and data availability. We explore trade-offs, architectural choices, and the need for software configurability to ensure AI models evolve with new data and vehicle parameters. We conclude by providing a glimpse into Sonatus’ latest AI product that encapsulates a holistic approach optimized for vehicle platforms—spanning cloud-based model development, optimization, and validation to in-vehicle inference—that unlocks the potential of Edge AI and SDVs for the automotive
Khatri, Sanjay, Sah, Mohamadali
Post-EOL Cybersecurity Validation for Automotive Production ECUs2026-26-0608To be published on 01/16/2026
ISO/SAE 21434 emphasizes comprehensive cybersecurity risk management throughout the automotive lifecycle. However, specific guidance on validating cybersecurity measures at the production level remains limited. This paper addresses the gap in production-stage validation, particularly after End-of-Line (EOL) flashing, which includes configurations of security hardware and software protection (e.g., UCB confirmation, JTAG, and P-flash password settings). Problem articulation: Current automotive cybersecurity validation methods, despite adherence to ISO/SAE 21434, lack specific procedures for the production stage. The existing system-level validation using the ASPICE V-model (e.g., SWE.6, SYS.5) does not ensure the integrity and functionality of cybersecurity features in the final manufactured unit post-EOL flashing. This gap poses a risk of vulnerabilities being introduced during the EOL process, compromising critical security measures. Proposed solution: To mitigate the cybersecurity
Chakraborty, Sucheta, Kulanthaisamy, Nagarajan, Sankar, Ganesh
Accelerating engine auxiliaries’ development by deploying ML techniques in CFD virtual verification2026-26-0374To be published on 01/16/2026
The automotive and off-road industries are heavily investing in research and development (R&D) to improve both physical and virtual verification and validation techniques. Recent software and hardware advancements have extended these techniques from simple component evaluations to complex system assessments such as involving multi-physics scenarios. Despite the benefits of virtual validation tools like structural analysis and computational fluid dynamics (CFD), they often come with high development costs, particularly in CFD applications. Virtual verification methodology, especially when combined with data science, offer significant advantages over traditional physical methods by enhancing computer-aided engineering (CAE) efficiency and reducing resource consumption which can greatly improve product design and validation efficiency across many industries. The success of machine learning applications depends on effective data processing, adequate computational resources, and the right
Jadhav, Mitali, Kumbhar, Appaso, Tirumala, Bhaskar, Nisha, Kumari
Enhanced Methodologies for predicting Automotive Wheel Bearing life and damage using RLDA2026-26-0482To be published on 01/16/2026
The durability of wheel bearings is assessed in terms of raceway life and flange life. Raceway life focuses on the performance and damage tolerance of rolling elements, while flange life evaluates the structural integrity of wheel flanges under operational stresses. Traditionally, durability predictions relied on conventional design methods and analytic formulas for raceway spalling, as well as static load assumptions for flange fatigue analysis. Recently, integrating design of experiments (DOE) with traditional approaches has enhanced these methods, enabling systematic evaluation of design variables and loading conditions. This paper introduces a methodology for analysing raceway life and damage in automotive wheel bearings using RLDA (Road Load Data Acquisition) data. The process involves acquiring raw deterministic load data, filtering it to preserve high-peaked signals, and transforming the filtered data into block cycles derived from load time histories. Each block cycle contains
Narendra, Vishwanath, Mane, Yogiraj, Paua, Ketan, Singh, Ram Krishnan, Vellandi, Vikraman
Integrating Road Load Data Acquisition and Signal Processing Techniques to Generate Reliable Drive Files for Electrodynamic Shakers in Testing the Exhaust Systems of Off-Highway Applications2026-26-0552To be published on 01/16/2026
Generating a reliable drive file for an electrodynamic (ED) shaker from Road Load Data Acquisition (RLDA) and validating its correlation with real-world conditions through damage and fatigue analysis is crucial for accurate component testing, particularly in complex systems like off-highway exhaust systems. This paper presents a methodology for creating such a drive file and establishing its validity, highlighting the necessity of ED shakers for simulating the intricate dynamic loads experienced by these systems. The process begins with acquiring comprehensive RLDA under representative operational conditions of the off-highway vehicle. Drive files are generated using this data, which records accelerations at important exhaust system mounting locations. Advanced signal processing techniques are employed to condense the raw RLDA into a format suitable for shaker control. To establish proper correlation, the generated drive file is used to excite the exhaust system on an ED shaker
Khaire, Santosh Ramdas, Khaire, Rushikesh, Yadav, Dnyaneshwar
Next-Gen Automotive: Enablers and Monetization for Automotive SaaS and Software Defined Vehicles2026-26-0687To be published on 01/16/2026
The automotive industry is undergoing a paradigm shift, changing its model from hardware-centric products to software-driven platforms. It is driven by two underlying paradigms: the emergence of Software-Defined Vehicles (SDVs) and the increasing adoption of Automotive Software-as-a-Service (SaaS) models. Together, these trends are transforming the way the industry generates value, engages with customers, and builds new revenue models. This research examines the most important technical pillars underpinning next-generation automotive ecosystem creation. Of these pillars, centralized computing infrastructures, embedded cloud integration, efficient over-the-air (OTA) update engines, and enhanced cybersecurity models designed to protect larger numbers of connected vehicles are observed. It also evaluates the imperative significance of digital identity management to provide improved personalized experience, the critical role of edge computing to ensure real-time processing, and the promise
Saini, Gourav, Jahagirdar, Shweta, Khandekar, Dhiraj Baburao
Enhancing Factory Modelling: Integrating Drone Technology and Automated Post Processing Systems2026-26-0307To be published on 01/16/2026
The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness. By utilizing 3D modeling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize factory
Narad, Akshay Maruti, C H, Ajheyasimha, Vijayasekaran, Vinothkumar, FASGE, ABHISHEK
Development of an Software Algorithm Based Semi-automated Data Acquisition System for Gasoline Vehicle Calibration on Chassis Dynamometer2026-26-0535To be published on 01/16/2026
Vehicle level testing is one of the crucial part of the calibration development. In this, chassis dynamometer is used to collect the vehicle level data. Different engine and vehicle level parameters are logged at various speed and load points covering the entire engine operational zone. This data acquisition is a repetitive activity and covers over 30-40% of the total testing duration. This phase includes base calibration verification, transient development, and emissions-related calibrations, all of which follow a standardized and repetitive sequence. However, these tasks are predominantly performed manually, leading to potential human error and inefficiencies. This paper presents a novel and comprehensive algorithm developed using INCA FLOW software; the first of its kind for this application. Here, crucial vehicle data acquisition activities are identified, mapped into detailed steps. The algorithm introduces a semi-automated, stepwise process for data acquisition during chassis
Kavekar, Pratap Chandrashekhar, Tyagarajan, Sethuramalingam, Agarwal, Nishant Kumar, Shaikh, Wasim, Karadi, Subramanya
A Performance-driven Numerical Approach for Optimizing the Plenum Opening Area of Cowl Box2026-26-0461To be published on 01/16/2026
There is a scarcity of research in literature regarding the determination of Plenum Opening Area of cowl box. The area of the plenum opening in the cowl box significantly affects the airflow rate in fresh airflow modes, such as face and defrost modes, as well as issues related to water ingress. Primarily, the size of the plenum opening is determined by the necessary HVAC airflow rate. This study aims to investigate how the plenum opening area impacts both airflow discharge and the water ingress issue in the HVAC module. A novel approach is introduced in this research to determine the optimal plenum opening area of the cowl box, taking into account both airflow rate and water ingress concerns. The ANSYS FLUENT software is utilized to analyze airflow discharge in both face and defrost modes, while the SPH (Smooth Particle Hydrodynamics) based Preonlab tool is employed for water ingress analysis. Airflow discharge is evaluated for various plenum opening sizes in both modes, and the area
Baskar, Subramaniyan, Mahesh, A, Gopinathan, Nagarajan
Optimizing Suspension Joint Reliability: Overcoming Bolt Loosening and Torque Variability in Automotive Production2026-26-0492To be published on 01/16/2026
In modern automotive manufacturing, ensuring the integrity of suspension joints under real-world driving conditions is a critical aspect of vehicle safety and performance. These joints endure substantial transverse loads and large vibrations due to irregular road surfaces, dynamic manoeuvres, and varying environmental factors. As a result, bolt loosening becomes a significant concern, compromising joint integrity and overall vehicle reliability. This paper delves into the challenges associated with maintaining joint integrity, specifically focusing on pre-load determination, torque application, and production-related issues. The pre-load generated during torquing is the primary factor that ensures a suspension joint remains securely fastened under dynamic road conditions. This pre-load is derived using road load data acquisition (RLDA) inputs, which capture the forces acting on the joint during actual driving scenarios. RLDA inputs provide critical insights into the forces experienced
Kumar, Sabeesh, Vasant Kumar, Jesse Daniel, Mishra, Harshit, Senthil Raja, T, NAYAK, Bhargav, M, Sudhan, NAMANI, Prasad, Vibhute, Shekhar
Implementation of Automotive Cybersecurity Assurance Level2026-26-0624To be published on 01/16/2026
This paper explores the implementation of ISO-21434 Automotive Cybersecurity Assurance Levels (CAL), focusing on enhancing component level cybersecurity for a vehicle. CAL values, which range from 1 to 4, provide a metric for ensuring that assets are protected against relevant threats at various phases of the Software Development Life Cycle (SDLC). By identifying potential attack vectors and their severity early in the SDLC, specifically during the Concept phase of ISO 21434, organizations can determine the CAL values. The CAL value serves as a benchmark to determine the level of severity required during the design, development, and verification phases of the SDLC. This paper outlines a method to establish CAL values as per ISO-21434 guidelines. The proposed methodology includes a detailed analysis of threat modeling, which is crucial for identifying and mitigating potential cybersecurity risks. By conducting threat modeling, organizations can systematically assess vulnerabilities and
Ghosh, Subham, Khader Batcha, Jashic
Security monitoring for High Performance Vehicle Computers2026-26-0627To be published on 01/16/2026
With the increasing connectivity of modern vehicles, cybersecurity threats have become a critical concern. Intrusion Detection Systems (IDS) play a vital role in securing in-vehicle networks and embedded vehicle computers from malicious attacks. This presentation shares about an IDS framework designed specifically for POSIX-based operating systems used in vehicle computers, leveraging system-level monitoring, anomaly detection, and signature-based methods to identify potential security breaches. The proposed IDS integrates lightweight behavioral analysis to ensure minimal computational overhead while effectively detecting unauthorized access, privilege escalation, communication interface monitoring etc. By employing a combination of rules and systems datapoints, the system enhances threat detection accuracy without compromising real-time performance. Practical series deployments demonstrate the effectiveness of this approach in mitigating cyber threats in automotive environments
Shukla, Siddharth, Chatterjee lng, Avik
Automotive Cybersecurity: Safeguarding Connected and Autonomous Mobility2026-26-0629To be published on 01/16/2026
As vehicles transform into complex cyber-physical systems within Intelligent Transportation Systems (ITS), automotive cybersecurity has become a foundational pillar in securing safe, reliable, and trustworthy transportation. This paper examines cybersecurity challenges in connected and autonomous vehicles (CAVs), focusing on Vehicle-to-Everything (V2X) communications technologies, including Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Pedestrian (V2P) and critical systems like electronic control units (ECUs), battery management units (BMUs), and sensor fusion modules. Key vulnerabilities, such as remote hacking, denial-of-service (DoS) attacks, malware injection, and data breaches, threaten vehicle functionality, passenger safety, and privacy. Key protection mechanisms, including encryption, intrusion detection systems (IDS), cryptographic protocols, secure over-the-air (OTA) updates, and Advanced Artificial Intelligence (AI) and Machine Learning (ML
Kumar, Om, Kumar, Rajiv, Sankar M, Gopi, Haregaonkar, Rushikesh Sambhaji
Resilient Design Strategies for Wireless Charger ECUs: A Proactive Threat Analysis and Risk Assessment Framework for Ensuring Cybersecurity in EV Charging Ecosystems2026-26-0173To be published on 01/16/2026
The goal of Automotive cybersecurity is safety, reliability, confidentiality and authenticity. As the automotive industry grows smarter, so too do the cybersecurity protocols used to protect vehicles from malicious actors. Automotive cybersecurity secures communication networks, electronic systems, software and data collected by the new wave of intelligent cars The paper describes the cyber security threats its analysis ion wireless charging
Uthaman, Sreekumar, Mulay, Abhijit B, Gadekar, Pundlik
Software-Defined Vehicles: Architecting the Future of Intelligent and Connected Mobility2026-26-0691To be published on 01/16/2026
The rise of Software-Defined Vehicles (SDVs) marks a fundamental shift in automotive design, where software, rather than hardware, defines vehicle capabilities. This review explores the SDV paradigm within Intelligent Transportation Systems (ITS), emphasizing centralized computing, over-the-air (OTA) updates, and service-oriented architectures (SOA). Key enablers such as high-performance computing units, middleware platforms (e.g., AUTOSAR Adaptive), and digital twins support scalable, flexible software deployment and real-time functionality. Artificial Intelligence (AI) and Machine Learning (ML) enhance features like predictive maintenance, driver personalization, and autonomous decision-making. However, SDVs also introduce complex challenges, including software validation, real-time performance, and system interoperability. Cybersecurity becomes critical, especially as vehicles interface with cloud platforms, edge computing, and Vehicle-to-Everything (V2X) networks. The paper also
Ahmad, Aqueel, Hemanth, Khimavath, Kumar, Om, Kumar, Rajiv, Haregaonkar, Rushikesh Sambhaji
Innovative Multi-Axial Testing approach for Enhanced Reliability of Cooling Modules in Commercial Vehicles2026-26-0530To be published on 01/16/2026
This paper presents an innovative in-lab accelerated testing approach for chassis-mounted components, with a particular focus on the cooling module of commercial vehicles. The proposed method simulates real-time data acquired from field operations and replicates all critical chassis modes, including torsion. Additionally, real-time coolant circulation at specified pressure and temperature maintenance are feasible during durability testing, enhancing the realism of the test environment. The cooling modules, comprising the radiator, intercooler, and charge air cooler (CAC), often experience failures due to various multi-axial inputs and chassis modes. This paper introduces an innovative methodology for replicating field conditions in the lab, utilizing seven servo-hydraulic actuators to simulate multi-axial inputs. The accuracy of in-lab simulation for the acceleration levels at input and response locations of the cooling module exceeds 90%. This makes it a preferred choice for test
V Dhage, Yogesh, Satale, Sunil
Optimisation of frontal impact force scheme to accelerate the target setting process for development of front-end structures in vehicles.2026-26-0001To be published on 01/16/2026
A passenger vehicle's front-end structure's structural integrity and crashworthiness are crucial to ensure compliance with various frontal impact safety standards (such as those set by Euro NCAP & IIHS). For a new front-end architecture, design targets must be defined at a component level for crush cans, longitudinal, bumper beam, subframe, suspension tower and backup structure. The traditional process of defining these targets involves multiple sensitivity studies in CAE. This paper explores the implementation of Physics-Informed Neural Networks (PINNs) in component-level target setting. PINNs integrate the governing equations into neural network training, enabling data-driven models to adhere to fundamental mechanical principles. The underlying physics in our model is based upon a force scheme of a full frontal impact. A force scheme is a one-dimensional representation of the front-end structure components that simplifies a crash event's complex physics. It uses the dimensional and
Gupta, Ishan, Bhatnagar, Abhinav, Kumar, Ayush
Swarm-Inspired Cooperative Mapping for Urban Autonomous Navigation2026-26-0635To be published on 01/16/2026
Urban autonomous vehicles today largely rely on individually built maps or centralized cloud services—both rigid under intermittent connectivity and rapid scene changes (construction, pop-up events). Inspired by how ant colonies collectively explore, we introduce a decentralized protocol: each vehicle broadcasts compressed “map particles” embedded with virtual pheromone values that highlight zones of high uncertainty or recent change. Neighboring vehicles incorporate these signals to prioritize sensing and route planning. A lightweight consensus algorithm merges local graphs into a shared dynamic map, resilient to network splits. The result, a bio-inspired SLAM that enables fleets to adapt on the fly to evolving cityscapes, boosting safety, efficiency, and robustness.
Bhargav, Anirudh S, Subbarao, Chitrashree
Proactive AI-Enhanced Cybersecurity for Next-Gen Automotive Infotainment ECU2026-26-0619To be published on 01/16/2026
Abstract With the rapid advancement of connected vehicle technologies, infotainment Electronic Control Units (ECUs) have become central to user interaction and connectivity within modern vehicles. However, this enhanced functionality has introduced new vulnerabilities to cyberattacks. This paper explores the application of Artificial Intelligence (AI) in enhancing the cybersecurity framework of infotainment ECUs. The study introduces AI-powered modules for threat detection and response, presents an integrated architecture, and validates performance through simulation using MATLAB, CANoe, and NS-3. This approach addresses real-time intrusion detection, anomaly analysis, and voice command security. Key benefits include zero-day exploit resistance, scalability, and continuous protection via OTA updates. The paper references real-world automotive cyberattack cases such as Tesla's OTA vulnerability patches, BMW ConnectedDrive exploits, and Jeep Cherokee's Uconnect hack, emphasizing the
More, Shweta, Kulkarni, Shraddha, Kumar, Priyanshu, GHANWAT, HEMANT, Joshi, Vivek
GTSRB-Shield: A Novel Machine Learning Framework for Intrusion Detection in Autonomous Vehicles Leveraging the German Traffic Sign Recognition Benchmark2026-26-0611To be published on 01/16/2026
The escalating dependence of Autonomous Vehicles on Intelligent Transportation Systems (ITS) has highlighted the imperative for comprehensive security protocols to safeguard such vehicles against cyber threats. Intrusion Detection Systems (IDS’s) are pivotal in ensuring the protection of these systems by detecting and alleviating unauthorized access and nefarious activities. The German Traffic Sign Recognition Benchmark (GTSRB) database, which encompasses an extensive compilation of traffic sign imagery, functions as a vital asset for the advancement of machine learning-based IDS. This research elucidates an intrusion detection system (IDS) that employs machine learning algorithms to scrutinize the GTSRB database. The proposed IDS emphasize the preprocessing of the GTSRB dataset to extricate pertinent features that can be employed for the training of machine learning models. Research also focuses on model development with machine learning algorithms to classify traffic signs and
Patil, Kamalesh, Akbar Badusha, A., Jadhav, Savitri, Gunale, Kishanprasad
Real-time Intrusion Detection System (IDS) for Vehicle Networks Using Machine Learning for Predictive Threat Analysis2026-26-0617To be published on 01/16/2026
The rapid adoption of connected vehicle technologies and advanced driver assistance systems (ADAS) necessitates robust security mechanisms capable of identifying and mitigating sophisticated cyber threats in real-time. Traditional signature-based intrusion detection systems (IDS) are often inadequate in addressing the dynamic and evolving nature of automotive cybersecurity threats, particularly in modern vehicle networks like Controller Area Network (CAN), CAN with Flexible Data-Rate (CAN-FD), and Automotive Ethernet. This research introduces a novel Real-time Intrusion Detection System utilizing advanced Machine Learning (ML) techniques designed specifically for automotive network environments. The proposed IDS framework employs supervised and unsupervised ML algorithms, including anomaly detection, behavioral analytics, and predictive threat modeling, to achieve high accuracy and rapid threat identification capabilities. Through extensive testing in simulated and actual vehicle
chaudhary lng, Vikash, Desai, Manoj, Chatterjee, Avik
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