Browse Topic: Optics

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An experimental investigation of the liquid jet breakup characteristics in a vaporizer under equivalent operating conditions of a microturbine combustion chamber2025-01-8458To be published on 04/01/2025
This study experimentally investigates the liquid jet breakup process in a vaporizer of a microturbine combustion chamber under equivalent operating conditions, including temperature and air mass flow rate. A high-speed camera experimental system, coupled with an image processing code, was developed to analyze the jet breakup length. The fuel jet is centrally positioned in a vaporizer with an inner diameter of 8mm. Airflow enters the vaporizer at controlled pressures, while thermal conditions are maintained between 298 K and 373 K using a PID-controlled heating system. The liquid is supplied through a jet with a 0.4 mm inner diameter, with a range of Reynolds numbers (Reliq=2300-3400), and aerodynamic Weber numbers (Weg=4-10), corresponding to the membrane and/or fiber breakup modes of the liquid jet. Based on the results of jet breakup length, a new model has been developed to complement flow regimes by low Weber and Reynolds numbers. The analysis of droplet size distribution
ha, Nguyen, Quan, Nguyen, Manh, Vu, Pham, Phuong Xuan
Technical Paper
Application of AI/ML based Image Analytics in Auto Component Fracture Analysis2025-01-8228To be published on 04/01/2025
This paper introduces an innovative digital solution for the categorization and analysis of fractures in Auto components, leveraging Artificial Intelligence and Machine Learning (AI/ML) technologies. The proposed system automates the fracture analysis process, enhancing speed, reliability, and accessibility for users with varying levels of expertise. The platform enables users to upload images of fractured parts, which are then processed by an AI/ML engine. The engine employs an image classification model to identify the type of fracture and a segmentation model to detect and analyze the direction of the fracture. The segmentation model accurately predicts cracks in the images, providing detailed insights into the direction and progression of the fractures. Additionally, the solution offers an intuitive interface for stakeholders to review past analyses and upload new images for examination. The AI/ML engine further examines the origin of the fracture, its progression pattern, and the
Sahoo, Priyabrata, Rawat, Sudhanshu, Garg, Vipin, Naidu, Garima, Sharma, Amit, Narula, Rahul, Bindra, Ritesh, Khera, Pankaj, Goel, Pooja, MONDAL, ARUP
Technical Paper
The Effect of Tracker and Control Point Distributions on the Accuracy of Vehicle Position and Speed Estimates from Dash Camera Video2025-01-8697To be published on 04/01/2025
Dash cameras (dashcams) can provide collision reconstructionists with quantifiable vehicle position and speed estimates. These estimates are achieved by tracking 2D video features with camera tracking software to solve for the camera position history and then calculating speed from the position-time history. Not all scenes have the same geometric features in quality or abundance. In this study, we quantified how the accuracy of vehicle position and speed estimates from dashcam video varied with large differences in 2D tracker distributions that mimicked the continuum between barren environments and feature-rich environments. We used video from a dashcam mounted in a vehicle undergoing straight-line emergency braking. The surrounding environment had abundant trackable features, including road markings, street lights, signs, trees, and buildings on both sides. We first created a reference solution using SynthEyes, a 3D object and camera tracking program, and all of the available features
Young, Cole, Ahrens, Matthew, Siegmund, Gunter P., Flynn, Thomas
Technical Paper
Accuracy of Time Stamps in Digital and Network Video Recorders2025-01-8690To be published on 04/01/2025
Video analysis plays a major role in many forensic fields. Many articles, publications, and presentations have covered the importance and difficulty in properly establishing frame timing. In many cases, the analyst is given video files that do not contain native metadata. In other cases, the files contain video recordings of the surveillance playback monitor which eliminates all original metadata from the video recording. These “video of video” recordings prevent an analyst from determining frame timing using metadata from the original file. However, within many of these video files, timestamp information is visually imprinted onto each frame. Analyses that rely on timing of events captured in video may benefit from these imprinted timestamps, but for forensic purposes, it is important to establish the accuracy and reliability of these timestamps. The purpose of this research is to examine the accuracy of these timestamps and to establish if they can be used to determine the timing
Molnar, Benjamin, Terpstra, Toby, Voitel, Tilo
Technical Paper
Validation of SynthEyes for use in Collision Reconstruction2025-01-8688To be published on 04/01/2025
Accurate reconstruction of vehicle collisions is critical for understanding incident dynamics and informing safety improvements. Traditionally, vehicle speed from dashcam footage has been approximated by estimating the time duration and distance traveled as the vehicle passes between reference objects. This method limits the resolution of the speed profile to an average speed over given intervals, minimizing vehicle accelerations. A more detailed speed profile can be calculated by tracking the vehicle’s position in each video frame; however, this method is time-consuming, can introduce positional error and variable frame rate noise, and is often constrained by the availability of external trackable features in the surrounding environment. Motion tracking software, widely used in the visual effects industry to track camera positions, has been adopted by some collision reconstructionist for determining vehicle speed from video. This study examines the accuracy and reliability of using
Perera, Nishan, Griffiths, Harrison, Prentice, Greg
Technical Paper
Effect of Camera and Video Parameters on the Accuracy of Vehicle Speeds and Accelerations Calculated from Onboard Dashboard Cameras2025-01-8691To be published on 04/01/2025
The goal of this study was to evaluate the influence of camera and video parameters on the accuracy of speeds and decelerations calculated from onboard dashboard cameras (“dashcams”). We equipped a single test vehicle with 5 commercially available dashcams. We also equipped the test vehicle with a 5th-wheel, a brake pedal switch, and a synchronization light visible to the dashcams. We conducted 9 emergency braking tests from about 60 km/h and calculated the reference speed and deceleration from the 5th-wheel data. The brake pedal switch indicated the moment of brake application. The light synchronized the dashcam videos to the 5th-wheel and brake pedal data. The 5 dashcams had horizontal fields of view (FOV) ranging from 120 to 170 degrees, frame rates of 30 or 60 frames per second (FPS), and resolutions of 1080p, 2K, or 4K. For each camera and brake test, we extracted the video from the dashcam and calculated its linear and rotational motion using SynthEyes, a 3D motion tracking and
Flynn, Thomas, Ahrens, Matthew, Young, Cole, Siegmund, Gunter P.
Technical Paper
Terrain Environment Estimating Method for Off-Road Vehicle Anticipated Driving Area Based on Stereo Vision2025-01-8279To be published on 04/01/2025
Off-road vehicles are required to traverse a variety of pavement environments, including asphalt roads, dirt roads, sandy terrains, snowy landscapes, rocky paths, brick roads, and gravel roads, over extended periods while maintaining stable motion. Consequently, the precise identification of pavement types, road unevenness, and other environmental information is crucial for intelligent decision-making and planning, as well as for assessing traversability risks in the autonomous driving functions of off-road vehicles. Compared to traditional perception solutions such as LiDAR and monocular cameras, stereo vision offers advantages like a simple structure, wide field of view, and robust spatial perception. However, its accuracy and computational cost in estimating complex off-road terrain environments still require further optimization. To address this challenge, this paper proposes a terrain environment estimating method for off-road vehicle anticipated driving area based on stereo
Zhao, Jian, Zhang, Xutong, Hou, Jie, Chen, Zhigang, Zheng, Wenbo, Gao, Lun, Zhu, Bing
Technical Paper
Mechanical Analysis of a Non-Pneumatic Tire’s Spokes2025-01-8327To be published on 04/01/2025
Mechanical analysis was performed of a non-pneumatic tire, specifically a Michelin Tweel size 8x8.55 N10, that can be used up to a speed of 30 mph (48.3 km/h). A Parylene-C coating was added to the rubber spoke specimens before performing both microscopic imaging and cyclic tensile testing. Initially, standard ASTM D412 specimens type C and A were cut from the wheel spokes, and then the specimens were subjected to deposition of a nanomaterial. The surfaces of the specimens were prepared in different ways to examine the influence on the material behavior including the stiffness and hysteresis. Microscopic imaging was performed to qualitatively compare the surfaces of the coated and uncoated specimens. Both coated and uncoated spoke specimens of each standard type were then subjected to low-rate cyclic tensile tests up to 500% strain. The results showed that the Parylene-C coating did not affect the maximum stress in the specimens, but did increase the residual strain. Type C specimens
Collings, William, Li, Chengzhi, Schwarz, Jackson, Lakhtakia, Akhlesh, Bakis, Charles, El-Sayegh, Zeinab, El-Gindy, Moustafa
Technical Paper
A Proactive System to Anticipate and Store the Damage Information on a Parked Car using Machine Learning Algorithm2025-01-8211To be published on 04/01/2025
Vehicle ADAS Systems majorly comprises of two functions: Driving and Parking. The most common form of damage to the vehicle which goes unnoticed with unidentified cause are parking damages. A vehicle once parked at a certain location may get damaged without knowledge of the user. In this work developed a solution that not only pre-warns the driver but also prepares the vehicle beforehand if it suspects a damage may occur. This eliminates the latency between damage and information capture, detects small damages such as scratches, classifies the type of damage and also informs the user beforehand. This is solution is different from our competitors as the existing solutions informs the user about the scratches/damages, but these solutions are expensive, have high response time, and the damage information is captured after the damage has occurred. The solution consists of the following check blocks: Precondition, Sensor Control and Action Module. The Precondition Module observes the
Debnath, Sarnab, PATIL, Prasad, Belur Subramanya, Sheshagiri, Govinda, Shiva Prasad
Technical Paper
A Method for Lens Distortion Correction of Algorithmically Altered Images2025-01-8680To be published on 04/01/2025
Photogrammetry is a commonly used type of analysis in accident reconstruction because it allows the position and orientation of vehicles to be quantified as seen in photographic and video evidence. Lens distortion is an important consideration when analyzing photographs and video through photogrammetry. Failure to account for lens distortion can result in inaccurate spatial measurements, particularly when elements of interest are located toward the edges and corners of images. A variety of methods for removing lens distortion are commonly used in photogrammetric analysis. However, these methods assume that lens distortion is solely the result of curved camera lenses and has not been altered algorithmically by the camera system. Today there are several cameras on the market that algorithmically alter images before saving them. These camera systems use proprietary distortion correction features to visually change photographs and videos before they can be viewed. A video or photograph
Pittman, Kathleen, Mockensturm, Eric, Buckman, Taylor, White, Kirsten
Technical Paper
Image-based Machine Learning Methods in Materials Microstructure and Failure Analysis2025-01-8324To be published on 04/01/2025
Image-based machine learning (ML) methods are increasingly transforming the field of materials science, offering powerful tools for automatic analysis of microstructures and failure mechanisms. This paper provides an overview of the latest advancements in ML techniques applied to materials microstructure and failure analysis, with a particular focus on the automatic detection of porosity and oxide defects and microstructure features such as dendritic arms and eutectic phase in aluminum casting. By leveraging image-based data, such as metallographic and fractographic images, ML models can identify patterns that are difficult to detect through conventional methods. The integration of convolutional neural networks (CNNs) and advanced image processing algorithms not only accelerates the analysis process but also improves accuracy by reducing subjectivity in interpretation. Key studies and applications are further reviewed to highlight the benefits, challenges, and future directions of
Akbari, Meysam, WANG, Andy, Wang, Qigui, Yan, Cuifen
Technical Paper
Mitigating Paint Blistering Defects in Aluminum Casted Parts: A QC Story Approach Utilizing Computed Tomography2025-01-8229To be published on 04/01/2025
Blistering in aesthetic parts poses a significant challenge, affecting overall appearance and eroding brand image from the customer's perspective and blister defects disrupt painting line efficiency, resulting in increased rework and rejection rates. This paper investigates the causes and effects of blistering, particularly in the context of internal soundness of Aluminium castings, emphasizing the crucial role of Computed Tomography in defect analysis. Computed Tomography is an advanced Non-Destructive Testing technique used to examine the internal soundness of a material. This study follows a structured 7-step QC story approach, from problem identification to standardization, to accurately identify the root Cause and implement corrective actions to eliminate blister defect. The findings reveal a strong link between internal soundness and surface quality. Based on the root cause, changes in the casting process and die design were made to improve internal soundness, leading to reduced
D, Balachandar, Nataraj, Naveenkumar
Technical Paper
Driving in the Rain: Evaluating How Surface Material Properties Affect Lidar Perception in Autonomous Driving2025-01-8016To be published on 04/01/2025
Light Detection and Ranging (LiDAR) is a promising type of sensor for autonomous driving that utilizes laser technologies to provide perceptions and accurate distance measurements to obstacles in the vehicle path. In recent years, there has also been a rise in the implementation of LiDARs in modern and autonomous vehicles to aid the self-driving features. However, navigating in adverse weather remains to be one of the biggest challenges in achieving Level 5 full autonomy due to sensor soiling, which subsequently leads to performance degradation that poses safety hazards. When driving in rain, raindrops impact the LiDAR sensor assembly and cause attenuation of signals when the light beams undergo reflections and refractions. Consequently, signal detectability, accuracy, and intensity are significantly affected. To date, limited studies were able to perform objective evaluations of LiDAR performance, most of which faced limitations that hindered realistic, controllable, and repeatable
Pao, Wing Yi, Li, Long, Agelin-chaab, Martin, Roy, Langis, Knutzen, Julian, Baltazar, Alex, Muenker, Klaus, Chakraborty, Anirban, Komar, John
Technical Paper
High-Speed Optical Diagnostics of Misfire Limits in a Spark-Ignited Heavy-Duty Hydrogen Engine2025-01-8401To be published on 04/01/2025
This study investigates the ignitability of hydrogen in an optical heavy-duty SI engine. While the ignition energy of hydrogen is exceptionally low, the high load and lean mixtures used in heavy-duty hydrogen engines lead to a high gas density, resulting in a much higher breakthrough voltage than in light-duty SI engines. Spark plug wear is a real concern, so there is a need to minimize the spark energy while maintaining combustion stability. The first part of this study was performed on an optical engine fitted with a metal piston. In this configuration, we mapped the combustion stability and frequency of misfires with two different ignition systems: a common inductive system and an actively controlled capacitive system. The equivalence ratio and dwell time were varied for the inductive system while the capacitive system instead varied spark duration and spark current in addition to equivalence ratio. A key finding was that leaner mixtures require longer dwell time or longer spark
Hallstadius, Peter, Saha, Anupam, Sridhara, Aravind, Andersson, Öivind
Technical Paper
Lane-Keeping for SAE Level 3 Autonomous Vehicles: A Behavior-Tree Approach Using Lidar Data for Enhanced Safety and Control2025-01-8025To be published on 04/01/2025
Lane-keeping is a critical function for SAE Level 3 autonomous vehicles, requiring rigorous validation and end-to-end interpretability. All recently U.S. approved level 3 vehicles are equipped with lidar, likely meant to accelerate active safety regulation compliance. Lidar offers precise and reliable surface distance and intensity measurements, enabling more effective implementation of rule-based algorithms compared to camera-based methods, which tend to rely on machine learning based methods for state-of-the-art performance. This paper presents a behavior-tree algorithm for lane-keeping using data from an Ouster OS0 lidar sensor. The algorithm evaluates factors such as road curvature, speed limits, road types (rural, urban, interstate), and the proximity of objects or humans to lane markings. It also accounts for the behavior and type of adjacent and opposing vehicles, lane occlusion, and weather conditions. The algorithm outputs safe lane keeping control instructions. The algorithm
Soloiu, Valentin, Mehrzed, Shaen, Kroeger, Luke, Pierce, Kody, Sutton, Timothy, Lange, Robin
Technical Paper
A Study on Reconstructing In-Cylinder Combustion Images Based on Local Images2025-01-8381To be published on 04/01/2025
The current leading experimental platform for engine visualization research is the optical engine, which features transparent window components classified into two types: partially visible windows and fully visible windows. Due to structural limitations, fully visible windows cannot be employed under certain complex or extreme operating conditions, leading to the acquisition of only local in-cylinder combustion images and resulting in information loss. This study introduces a method for reconstructing in-cylinder combustion images from local images using deep learning techniques. The experiments were conducted using an optical engine specifically designed for spark-ignition combustion modes, capturing in-cylinder flame images under various conditions with high-speed cameras. The primary focus was on reconstructing the flame edge, with in-cylinder combustion images categorized into three types: images where the flame edge is fully within the partially visible window, partly within the
Wang, Mianheng, Zhang, Yixiao, Du, Haoyu, Xiao, Ma, Mao, Jianshu, Fang, Yuwen
Technical Paper
Hierarchical Feature-based Localization using Scene Graphs in Off-road Navigation2025-01-8046To be published on 04/01/2025
While numerous advancements have been made in autonomous navigation for structured indoor and outdoor environments, these solutions often do not generalize well to off-road settings. There are unique challenges in such settings such as unreliable GPS, limited computational and memory resources, and sparse environmental features, making navigation particularly difficult. In our work, we propose a novel data structure called Hierarchical Dynamic Scene Graphs (HDSG) to address these challenges. HDSG captures environmental information at different resolutions, integrating both geometric and semantic features. It enables various navigation tasks such as localization, loop closure, and human interaction through the visualization of environmental features for remote operators. We evaluated the performance of localizing a robot's position within the world frame by comparing compact spatial descriptors extracted from semi-consecutive scene graphs, derived from 3D LiDAR point clouds. Compared to
Alam, Fardifa Fathmiul, Iuricich, Federico, Li, Nianyi, Jia, Yunyi, Li, Bing
Technical Paper
A Motion-Aware Enhanced LiDAR ICP SLAM Framework for High-Speed Autonomy2025-01-8044To be published on 04/01/2025
Towards the goal of real-time navigation of autonomous robots, the Iterative Closest Point (ICP) based LiDAR odometry methods are a favorable class of Simultaneous Localization and Mapping (SLAM) algorithms for their high performance and efficiency. In the context of autonomous vehicles, which tend to be high motion scenarios, ICP-based SLAM algorithms provide a relatively robust and real-time localization and mapping framework, paving the way for full autonomy. However even with the recent methods, the traditional SLAM challenges persist, where odometry drifts under adversarial conditions such as featureless or dynamic environments, and under high motion of the robot. In this paper we present a motion aware, enhanced LiDAR ICP SLAM framework. We have three contributions: a novel ICP cost function that yields faster and more robust convergence compared to the state of the art methods, a sophisticated motion constraint that maintain robot localization under sudden motion changes, and
Kokenoz, Cigdem, Shaik, Toukheer, Li, Bing
Technical Paper
Solving Vehicle Speed and Acceleration from Video Evidence Using Optimization2025-01-8683To be published on 04/01/2025
This paper introduces a method to solve vehicle dynamics from one or more sources of video evidence by using optimization to determine the best-fit speed profile that tracks the measured path of a vehicle through a scene. Mathematical optimization is the process of seeking the variables that drive an objective function to some optimal value, usually a minimum, subject to constraints on the variables. In the video analysis problem, the analyst is seeking a speed profile that tracks measured vehicle positions over time. Measured positions and observations in the video constrain the vehicle’s motion and can be used to determine the vehicle dynamics. The variables are the vehicle’s initial speed and an unknown number of acceleration sequences. Optimization can be used to determine the speed profile that minimizes the total error between the vehicle’s calculated travel distance at each measured position, subject to various constraints. A test was designed to demonstrate the proposed method
Snyder, Sean, Callahan, Michael, Wilhelm, Christopher, Johnk, Chris, Lowi, Alvin, Bretting, Gerald
Technical Paper
A Comparison of UAV LiDAR and Terrestrial Laser Scanner Accuracies2025-01-8694To be published on 04/01/2025
Airborne Laser Scanning (ALS) and specifically LiDAR collected from Unmanned Aerial Vehicles (UAVs) has become an important tool for forensic site documentation. This is especially true for large, outdoor incident sites. Terrestrial Laser Scanners (TLS) are commonly used to document these large incident sites, however in comparison to TLS LiDAR, UAV LiDAR is a more efficient method for forensic service providers to document these sites. ALS is well suited for the vehicle accident reconstruction community as most roadway accident sites are accessible by UAVs. Safety may be an additional benefit of ALS as it typically requires less documentation time at the incident site and the LiDAR data is recorded from a more remote location. This research focuses on comparing the accuracy of UAV LiDAR to TLS LiDAR and evaluates efficiency of data capture. For the purposes of this study, three site locations with differing roadway geometry were chosen and documented with both TLS and UAV LiDAR. The
Foltz, Steve, Terpstra, Toby, Clarson, Julia
Technical Paper
Accuracy of Mobile of Phone LiDAR on Crashed Accident Vehicles2025-01-8692To be published on 04/01/2025
Apple’s mobile phone LiDAR capabilities can be used with multiple software applications to capture the geometry of vehicles and smaller objects. The results from differing software have been previously researched and compared to traditional ground-based LiDAR. However, results were inconsistent across software applications, with some software being more accurate and others being less accurate. (Technical Paper 2023-01-0614. Miller, Hashemian, Gillihan, Benes.) This paper builds upon existing research by utilizing the updated LiDAR hardware that Apple has added to its iPhone 15 smartphone lineup. This new hardware, in combination with the software application PolyCam, was used to scan a variety of crashed vehicles. These crashed vehicles were also scanned using a FARO 3d scanners, which have been researched extensively for their accuracy. The PolyCam scans were compared to FARO scans to determine accuracy for point location and scaling. Previous research has shown that user variability
Miller, Seth Higgins, Stogsdill, Michael, McWhirter, Seth
Technical Paper
Evaluating Consumer-Grade Technology for Accident Reconstruction: A Study of Recon3D, Luma AI, and Leica RTC3602025-01-8689To be published on 04/01/2025
This research paper investigates the effectiveness of various tools for generating 3D point clouds in accident reconstruction. Specifically, it compares the Recon3D app on the iPhone 14 and 15 Pro Max, Luma AI’s capability of generating 3D point clouds from 360-degree vehicle videos, and the industry-standard Leica RTC360 laser scanner. The study aims to capture detailed spatial data of vehicles involved in accidents, assessing the accuracy, efficiency, and usability of these tools. Controlled experiments were conducted to evaluate the performance of each tool, comparing the generated 3D models to those produced by the Leica RTC360 laser scanner. This comparative analysis highlights the potential for consumer-grade and AI-powered technologies to democratize access to advanced accident reconstruction tools, providing significant benefits to law enforcement, insurance investigators, and accident analysts
Desai, Elvis, Stemrich, Robert
Technical Paper
The Effects of Collision-Related Power Loss on Toyota Safety Sense 2.5+ Event Data2025-01-8713To be published on 04/01/2025
Toyota vehicles equipped with Toyota Safety Sense (TSS) can record detailed information surrounding various driving events, including crashes. Often, this data is employed in accident reconstruction. TSS data is comprised of three main categories: Vehicle Control History (VCH), Freeze Frame Data (FFD), and images. Because the TSS data resides in multiple ECUs, the data recording is susceptible to catastrophic power loss. In this paper, the effects of a sudden power loss on the VCH, FFD, and images are studied. Events are triggered on a TSS 2.5+ equipped vehicle by driving toward a stationary target. After system activation, a total power loss is induced at various delays after activation. Results show that there is a minimum time required after system initiation in order to obtain full VCH, FFD, and image records. Power losses occurring within this time frame produce incomplete records. Data accuracy is unaffected, even in partial records
Getz, Charles, DiSogra, Matthew, Spivey, Heath, Johnson, Taylor, Patel, Amit
Technical Paper
Evaluation of Several Tesla Dashcam Angles for Model 3 and Y Via Reverse Project Photogrammetry2025-01-8700To be published on 04/01/2025
Tesla Model 3 and Model Y vehicles come equipped with a standard dash-camera feature with the ability to record video in four directions called TeslaCam. Additional views have been known to be recorded under specific circumstances, often triggered in impacts, and available to some owners through data requests to Tesla. Prior research into TeslaCam either did not fully explore all known camera views across multiple vehicles or the metadata associated with genuine copies of TeslaCam video. Testing and analysis of real-world crashes revealed a reliable methodology to conducting photogrammetry analysis utilizing the front, rear, and side cameras found on Tesla Model 3 and Model Y vehicles, how to verify the likelihood of any copy of TeslaCam being a genuine original video discovered during the course of an investigation, and how to examine ever changing video properties as new software becomes available for the vehicles. Moving and stationary Tesla vehicles captured frames of GPS
Jorgensen, Michael, Swinford, Scott, Imada, Kevin, Farhat, Ali
Technical Paper
Decentralized perception system with multiple viewpoints2025-01-8097To be published on 04/01/2025
Vehicle-to-Infrastructure (V2I) cooperation has emerged as a fundamental technology to overcome the limitations of the individual ego-vehicle perception. Onboard perception is limited by the lack of information for understanding the environment, the lack of anticipation, the drop of performance due to occlusions and the physical limitations of embedded sensors. V2I perception in a cooperative manner improves the ego-vehicle perception range by receiving information from the infrastructure that has another point of view, mounted with sensors, such as camera and LiDAR. This technical paper presents a perception pipeline developed for the infrastructure, based on images with multiple viewpoints. It is designed to be scalable and has five main components: the image acquisition for the modification of camera settings and to get the pixel data, the object detection for fast and accurate detection of four wheels, two wheels and pedestrians, the data fusion module for robust fusion of the 2D
Picard, Quentin, Fadili, Maryem, Morice, Malo, Pechberti PhD, Steve
Technical Paper
Validating the Sun System in 3ds Max for Recreating Shadows2025-01-8685To be published on 04/01/2025
Shadow positions can be useful in determining the time of day that a photograph was taken and determining the position, size, and orientation of an object casting a shadow in a scene. Aastronomical equations can predict the location of the sun relative to the earth, and therefore the position of shadows cast by objects, based on the location’s latitude and longitude as well as the date and time. 3D computer software includes these calculations as a part of their built-in sun systems. In this paper, the authors examine the sun system in the 3D modeling software 3ds Max to determine its accuracy for use in accident reconstruction. A parking lot was scanned using FARO LiDAR scanner to create a point cloud of the environment. A camera was then set up on a tripod at the environment and photographs were taken at various times throughout the day from the same location in the environment. This environment was then 3D modeled in 3ds Max based on the point cloud, and the sun system in 3ds Max
Barreiro, Evan, Erickson, Michael, Smith, Connor, Carter, Neal, Hashemian, Alireza
Technical Paper
Testing Complex Circuits with Radio-frequency Reflectometry2025-01-8251To be published on 04/01/2025
Driving automation systems rely heavily on sophisticated electronics to function effectively, and economic pressure poses new challenges in manufacturing. Tightly integrated sensors, processors, and communication modules monitor and control the vehicle's operation at any time. Size, weight, power, and cost constraints put pressure on manufactures to reduce stack electronics, miniaturize boards, and innovate over the traditional sequential assemble/test cycle. Consequently, designers and manufacturers reduce access to boards, remove test points, co-locate RF with other components, and break the sequential SMT line. Radiofrequency (RF) reflectometry is a mature and reliable technology essential for characterizing materials, components, and analog circuits. It provides precise insights into electromagnetic properties like impedance and permittivity, crucial for optimizing RF and microwave designs. Widely used in fields from material science to quantum computing, RF reflectometry is key to
Fischmeister, Sebastian, Moreno, Carlos
Technical Paper
Adaptive Sliding Mode Control for Active Hydro-Pneumatic Suspension Based on Extended State Observer2025-01-8272To be published on 04/01/2025
Hydro-pneumatic suspension is widely used due to its favorable nonlinear stiffness and damping characteristics. However, with the presence of parameter uncertainties and high nonlinearities in the hydro-pneumatic suspension system, the effectiveness of the controller is often suboptimal in practical applications. To mitigate the influence of these issues on the control performance, an adaptive sliding mode control method with an expanded state observer (ESO) is proposed. Firstly, a nonlinear mathematical model of hydro-pneumatic suspension, considering seal friction, is established based on the hydraulic principle and the knowledge of fluid mechanics. Secondly, the ESO is designed to estimate the total disturbance caused by the nonlinearities and uncertainties, and it is incorporated into the sliding mode control law, allowing the control law to adapt to the operating state of the suspension system in real time, which solves the effect of uncertainties and nonlinearities on the system
changsheng, Niu, Liu, Xiao-Ang, Jia, Xing, Gong, Bo, Xu, Bo
Technical Paper
The Rain Test Domain: An Outdoor Testbed for Automotive Sensor Evaluation in Adverse Weather Conditions2025-01-8058To be published on 04/01/2025
The rapid development of autonomous vehicles necessitates rigorous testing under diverse environmental conditions to ensure their reliability and safety. One of the most challenging scenarios for both human and machine vision is navigating through rain. This study introduces the Digitrans Rain Testbed, an innovative outdoor rain facility specifically designed to test and evaluate automotive sensors under realistic and controlled rain conditions. The rain testbed features a wetted area of 600 square meters and a rain volume of 600 cubic meters, providing a comprehensive environment to rigorously assess the performance of autonomous vehicle sensors. Rain poses a significant challenge due to the complex interaction of light with raindrops, leading to phenomena such as scattering, absorption, and reflection, which can severely impair sensor performance. Our facility replicates various rain intensities and conditions, enabling comprehensive testing of radar, lidar, and camera sensors. By
Feichtinger, Christoph Simon
Technical Paper
Evaluation of the Effect of Image Processing Methodology on Vapor Length and Spray Angle in a Constant Volume Combustion Chamber2025-01-8465To be published on 04/01/2025
In direct injected engines the spray formation is important for both combustion performance and emission formation. Thus, being able to compare how the spray formation is affected by changes in nozzle design, injection pressure or fuel formulation is an important area of research for all engine sizes. This becomes especially important for the introduction of new sustainable fuels, or for fuel injection optimization to increase efficiencies and minimize the formation of emissions such as particles. High-speed imaging of the fuel spray using the schlieren technique is well established for this purpose, and the Engine Combustion Network (ECN) has developed multiple guidelines to ensure that a similar experimental approach is used in different laboratories around the world. For the initial image processing, the ECN provides a procedure based on an image-temporal-derivative approach. Many researchers however rely on intensity-based thresholding, preceded by contrast adjustment, background
Sileghem, Victor, Larsson, Tara, Dejaegere, Quinten, Verhelst, Sebastian
Technical Paper
Discrepancies Between Metadata and Actual Camera Field of View: A Photogrammetric Approach2025-01-8686To be published on 04/01/2025
Camera matching photogrammetry is widely used in the field of accident reconstruction for mapping accident scenes, modeling vehicle damage from post collision photographs, analyzing sight lines, and video tracking. A critical aspect of camera-matching photogrammetry is determining the Field of View (FOV) of the photograph being analyzed. FOV is a function of the focal length of the camera and the size of the camera sensor and is recorded in the metadata of the photograph. The intent of this research is to analyze the accuracy of the metadata reported Field of View (FOV) and Focal Length. The FOV from photographs captured by over 20 different cameras of various makes, models, and focal lengths will be determined using a controlled and repeatable measurement methodology. The difference in measured FOV versus reported FOV will be presented and analyzed. This research will provide analysts with a quantifiable and acceptable range of FOVs when camera matching and utilizing the metadata from
Smith, Connor A., Erickson, Michael, Hashemian, Alireza
Technical Paper
LiDAR Contamination Recognition based on optimized PointNet2025-01-8013To be published on 04/01/2025
LiDAR sensors have become an integral component in the realm of autonomous driving, widely utilized in environmental perception and vehicle navigation. However, in real-world road environments, contaminants such as dust and dirt can severely hamper the cleanliness of LiDAR optical windows, thereby degrading operational performance and affecting the overall environmental perception capabilities of intelligent driving systems. Consequently, maintaining the cleanliness of LiDAR optical windows is crucial for sustaining device performance. Unfortunately, the scarcity of publicly available LiDAR contamination datasets poses a challenge to the research and development of contamination identification algorithms. This paper first introduces a method for acquiring LiDAR-pollution datasets. The dataset modeles mud and leaves as two different pollution states by acquiring data from a LiDAR device on an urban open road. The constructed dataset meticulously differentiates among the three states
wei, ziyu, guo, binyun, Lujia, Ran, Li, Liguang
Technical Paper
Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety2025-01-8017To be published on 04/01/2025
Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using
Ponnuru, Venkata Naga Rithika, Das, Sushanta, Grant, Joseph, Naber, Jeffrey, Bahramgiri, Mojtaba
Technical Paper
RGB2BEV-Net: A PyTorch-based End-to-End Pipeline for RGB to BEV Segmentation Using an Extended Dataset for Autonomous Driving2025-01-8023To be published on 04/01/2025
In this work, we introduce RGB2BEV-Net, an end-to-end pipeline that extends traditional BEV segmentation models by utilizing raw RGB images with Bird’s Eye View (BEV) generation. While previous work primarily focused on pre-segmented images to generate corresponding BEV maps, our approach expands this by collecting RGB images alongside their affiliated segmentation masks and BEV representations. This enables direct input of RGB camera sensors into the pipeline, reflecting real-world autonomous driving scenarios where RGB cameras are commonly used as sensors, rather than relying on pre-segmented images. Our model processes four RGB images through a segmentation layer before converting them into a segmented BEV, implemented in the PyTorch framework after being adapted from an original implementation that utilized a different framework. This adaptation was necessary to improve compatibility, optimize performance and ensure better integration of the entire system within autonomous vehicle
Hossain, Sabir, Lin, Xianke
Technical Paper
Sparse BEV 3D Object Detection with Perspective Supervision2025-01-8019To be published on 04/01/2025
Object detection is a fundamental task in autonomous driving perception systems. This paper discusses this task and proposes a novel sparse BEV object detector with perspective supervision. Existing BEV object detectors typically use 2D modern image backbones. We further explore pre-trained backbones to adapt general 2D image backbones for BEV detectors. By using modern image backbone networks in BEV models, inference accuracy can rival certain depth-pretrained backbones, thereby unleashing the full potential of modern image backbones. Many advanced dense BEV detectors have limitations in model flexibility. For instance, temporal feature fusion requires stacking historical features, consuming substantial computational resources, and modeling moving objects necessitates a large receptive field. Some sparse BEV detectors suffer from slower convergence due to sparsity. We aim for BEV detectors to model more flexibly in both temporal and spatial domains and achieve faster convergence
Yan, Yixiong
Technical Paper
Experimental Characterization of Fabricated (310) and (210) Symmetrical Tilt High-Angle Grain Boundaries in Bicrystalline Copper Thin Films Using NaCl Substrates2025-28-0034To be published on 02/07/2025
A novel sintering method of bridging the two mechanically polished and oriented single-crystals together face-to-face in a non-environmental controlled atmosphere to fabricate the bicrystal substrate of NaCl of macroscopic thickness, with a common zone axis and having planarity over large areas, has been developed. Epitaxial [001] bicrystalline thin face-centered cubic (fcc) metal film of surface-reactive metal-containing tilt grain boundary across the interface is first grown in high vacuum directly by flash deposition on initially fabricated [001] oriented bicrystalline substrate of NaCl. The [001] tilt boundary, thus produced, is examined by electron microscopy to characterize grain boundary morphology and structure. The findings of some preliminary investigations are then presented. A distinct atomic structure is observed for {310} and {210} inclination. Both HAADF-STEM and Diffraction images reveal that such fabricated high-angle grain boundary accommodates minor deviations from
Dish, Nilabh, Gautam, Abhay, Behera, Rakesh, Banka, Hemasunder, Chavan, Pradeep
Technical Paper
Design of Smart Assistive Headgear For The Blind2025-28-0096To be published on 02/07/2025
This paper presents the development of a cost-effective assistive headgear designed to address the navigation challenges faced by millions of visually impaired individuals. Existing solutions are often prohibitively expensive, leaving a significant portion of this population underserved. To address this gap, we propose a novel human-machine interface that utilizes a synergistic combination of computer vision, stereo imaging, and haptic feedback technologies. The focus of this project lies in the creation of a practical and affordable headgear that empowers visually impaired users with real-time obstacle detection and navigation capabilities. The solution leverages computer vision for environmental analysis and integrates haptic feedback for intuitive user guidance. This paper details the design intricacies of the headgear, along with the implementation methodologies employed. We present comprehensive testing results and discuss the project's potential to significantly enhance
Manu, Rohith, S Nair, Sreeram, Biju, Mariya, KM, Devika, Sadique, Anwar
Technical Paper
Autonomous Drone Solution for Human-Wildlife Conflict Management2025-28-0198To be published on 02/07/2025
Human-wildlife conflicts pose significant challenges to both conservation efforts and community well-being. As these conflicts escalate globally, innovative technologies become imperative for effective and humane management strategies. This paper presents an integrated autonomous drone solution designed to mitigate human-wildlife conflicts by leveraging technologies in drone surveillance and artificial intelligence. The proposed system consists of stationary IR cameras that are setup within the conflict prone areas, which utilizes machine learning to identify the presence of wild animals and to send the corresponding location to a drone docking station. An autonomous drone equipped with high resolution IR cameras and sensors is deployed from the docking station to the provided location. The drone camera utilizes object detection technology to scan the specified zone to detect the animal and emit animal repelling ultrasonic sound from a device integrated to the drone to achieve non
Sadanandan, Vaishnav, Sadique, Anwar, George, Angeo Pradeep, Vinod, Vishal, Raveendran, Darshan Unni
Technical Paper
Investigation on Heat Transfer Properties of Graphene Nanoplatelets Blended Distilled Water-Ethylene Glycol Mixtures in Battery thermal management system2025-28-0089To be published on 02/07/2025
This study investigates the heat transfer properties of graphene nanoplatelets (GnPs) blended with distilled water-ethylene glycol (DW-EG) mixtures, focusing on their potential application in battery thermal management systems (BTMS). Compared to other nanoparticles, carbon nanostructures exhibit higher thermal conductivity due to their low density and integrated thermal conductivity. The experimental findings are relevant in that compared with the base fluid, nanofluid samples had heat transfer capability. The physicochemical characteristics of investigated GNP were characterized using a Scanning Electron Microscope (SEM), pH and UV–Vis spectrophotometry. The thermal conductivity and physical properties of graphene platelets having the specific surface area of 500 m2/g in the base fluid of Distilled Water-Ethylene Glycol (DW-EG 70:30) and 100 % vol. of Ethylene Glycol (EG 100) were determined after 120 minutes of sonication time. The graphene nanofluids with the platelet
S, Palanisamy, Selvan, Arul Mozhi
Technical Paper
Influence of High-Temperature Annealing Parameters on Wear and Friction Mechanisms in Polypropylene Carbon Fiber Composites2025-01-500001/15/2025
Fused deposition modeling (FDM) is a rapidly growing additive manufacturing method employed for printing fiber-reinforced polymer composites. Nonetheless, the performance of printed parts is often constrained by inherent defects. This study investigates how the varying annealing parameter affects the tribological properties of FDM-produced polypropylene carbon fiber composites. The composite pin specimens were created in a standard size of 35 mm height and 12 mm diameter, based on the specifications of the tribometer pin holder. The impact of high-temperature annealing process parameters are explored, specifically annealing temperature and duration, while maintaining a fixed cooling rate. Two set of printed samples were taken for post-annealing at temperature of 85°C for 60 and 90 min, respectively. The tribological properties were evaluated using a dry pin-on-disc setup and examined both pre- (as-built) and post-annealing at temperature of 85°C for 60 and 90 min printed samples
Nallasivam, J.D., Sundararaj, S., Kandavalli, Sumanth Ratna, Pradab, R.
Technical Paper
SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer1349501/10/2025
In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in
Hineman, Marcie
Podcast
Exploring Shock Wave–Boundary Layer Interaction Using Rainbow Schlieren Deflectometry01-18-01-000212/24/2024
The flow structure and unsteadiness of shock wave–boundary layer interaction (SWBLI) has been studied using rainbow schlieren deflectometry (RSD), ensemble averaging, fast Fourier transform (FFT), and snapshot proper orthogonal decomposition (POD) techniques. Shockwaves were generated in a test section by subjecting a Mach = 3.1 free-stream flow to a 12° isosceles triangular prism. The RSD pictures captured with a high-speed camera at 5000 frames/s rate were used to determine the transverse ray deflections at each pixel of the pictures. The interaction region structure is described statistically with the ensemble average and root mean square deflections. The FFT technique was used to determine the frequency content of the flow field. Results indicate that dominant frequencies were in the range of 400 Hz–900 Hz. The Strouhal numbers calculated using the RSD data were in the range of 0.025–0.07. The snapshot POD technique was employed to analyze flow structures and their associated
Datta, Narendra, Olcmen, Semih, Kolhe, Pankaj
Journal Article
Laser Welding Applied to Complex Phase Steel2024-36-015912/20/2024
The development of advanced high-strength steels has become essential in the production of lightweight, safe, and more economical vehicles within the context of the automotive industry. Among the advanced high-strength steels, complex phase steels stand out, characterized by their high formability and high energy absorption and deformation capacity. Laser welding is a technique that applies laser using high energy density as a heat source. It has the advantages that the high welding speed and low heat input compared to other welding methods cause a decrease in deformation, and the narrow width of the weld bead and heat-affected zone allows for the welding of complex parts that would be difficult for other welding methods. Based on a study of a complex phase steel, an analysis was made of the microstructures observed by optical microscopy, the grain boundaries and certain phases contained in this microstructure, as well as the microstructures of each area in the laser welding region
Dias, Erica Ximenes, Reis de Faria Neto, Antonio, Castro, Thais Santos, Martins, Marcelo Sampaio, Santos Pereira, Marcelo
Technical Paper
Dynamic SLAM with Real-Time Semantic Segmentation and Inter-Frame and Multi-Frame Motion Feature Detection2024-01-704212/13/2024
Traditional Simultaneous Localization and Mapping (SLAM) methods often assume static environments. This limitation can lead to inaccurate localization or even the loss of tracking in dynamic scenes. To address this issue, we propose a novel SLAM approach specifically designed for dynamic environments. Our method integrates the real-time image semantic segmentation network BisenetV2 with inter-frame and continuous multi-frame motion feature detection. Firstly, semantic segmentation is applied to render the semantic mask, which is then used by the inter-frame motion detection module to identify potential motion features. Subsequently, these suspected motion features are evaluated by a likelihood probability model across consecutive frames. Finally, points with a high probability of motion are monitored in real-time by the Luenberger observer, which filters out motion features and re-adds static ones. Our experiments demonstrate that semantic segmentation can meet real-time requirements
Qin, Xiaohui, Gao, Chengyu, Huang, Shengjie, Zeng, Conglei, Zhou, Yunshui
Technical Paper
Hierarchical-Level Rain Image Generative Model Based on GAN2024-01-703412/13/2024
Visual perception systems for autonomous vehicles are exposed to a wide variety of complex weather conditions, among which rainfall is one of the weather conditions with high exposure. Therefore, it is necessary to construct a model that can efficiently generate a large number of images with different rainfall intensities to help test the visual perception system under rainfall conditions. However, the existing datasets either do not contain multilevel rainfall or are synthetic images. It is difficult to support the construction of the model. In this paper, the natural rainfall images of different rainfall intensities were first collected and produced a natural multilevel rain dataset. The dataset includes no rain and three levels (light, medium and heavy) of rainfall with the number of 629, 210, 248 and 193 respectively, totaling 1280 images. The dataset is open source and available online via: https://github.com/raydison/natural-multilevel-rain-dataset-NMRD. Subsequently, a
Liu, Zhenyuan, Jia, Tong, Xing, Xingyu, Wu, Jianfeng, Chen, Junyi
Technical Paper
Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems2024-01-702812/13/2024
Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructure to address the occlusion and sensing range limitation issues. To date, existing research is mainly focused on prototyping cooperative perception using only one type of sensor such as LiDAR and camera. In such cases, the performance of cooperative perception is constrained by individual sensor limitations. To exploit the multi-modality of sensors to further improve distant object detection accuracy, in this paper, we propose a unified multi-modal multi-agent cooperative perception framework that integrates camera and LiDAR data to enhance perception performance in intelligent transportation systems. By leveraging the complementary strengths of LiDAR and camera sensors, our framework utilizes the geometry information from LiDAR and the semantic information from cameras to achieve an accurate cooperative perception
Meng, Zonglin, Xia, Xin, Zheng, Zhaoliang, Gao, Letian, Liu, Wei, Zhu, Jiaqi, Ma, Jiaqi
Technical Paper
Research on a Detection Algorithm of Contaminant on Automotive Cameras Optical Surface Based on SEFaster-YOLOv82024-01-703212/13/2024
Cameras are crucial sensors in intelligent driving systems. Due to the optical windows of these cameras generally being exposed, they are highly susceptible to contaminant from external dust, mud, and other contaminants. These contaminants can degrade the vehicle’s perception capabilities, posing safety risks. Therefore, research on the identification and automatic cleaning of optical window surface contamination for automotive cameras is essential. This paper constructs a dataset of contaminated images of automotive cameras using a method based on shooting and image fusion. By introducing the SE attention mechanism and replacing the YOLOv8 backbone network with FasterNet, this paper proposed the SEFaster-YOLOv8 model. Experimental results show that the SEFaster-YOLOv8 model reduces the parameter count by 37.6% compared to the original YOLOv8 model. The mAP@0.5 and mAP@0.5:0.95 reach 95.7% and 66.9%, respectively, representing improvements of 1.8% and 1.1% over the original YOLOv8
Ran, Lujia, Hu, Zongjie, Lu, Xiangxiang, Wu, Zhijun
Technical Paper
Low-Cost Fusion Odometry Algorithm Based on 4D Radar and Pseudo-LiDAR: Bridging the Gap between 4D Radar and Images in 3D Space2024-01-703112/13/2024
Recently, four-dimensional (4D) radar has shown unique advantages in the field of odometry estimation due to its low cost, all-weather use, and dynamic and static recognition. These features complement the performance of monocular cameras, which provide rich information but are easily affected by lighting. However, the construction of deep radar visual odometry faces the following challenges: (1) the 4D radar point cloud is very sparse; (2) due to the penetration ability of 4D radar, it will produce mismatches with pixels when projected onto the image plane. In order to enrich the point cloud information and improve the accuracy of modal correspondence, this paper proposes a low-cost fusion odometry method based on 4D radar and pseudo-LiDAR, 4DRPLO-Net. This method proposes a new framework that uses 4D radar points and pseudo-LiDAR points generated by images to construct odometry, bridging the gap between 4D radar and images in three-dimensional (3D) space. Specifically, the pseudo
Huang, Minqing, Lu, Shouyi, Zhuo, Guirong
Technical Paper
Effect of Nano Silica on the Mechanical Behaviour of High Impact Polypropylene/Nano Clay Composites2024-01-521112/10/2024
This study investigated the effect of nano silica on the mechanical behaviour of blends containing high impact polypropylene (hiPP) and nano clay. This study used nano silica from rice husk ash with an average particle size of 26 nm. The hiPP composites were mixed with 3 wt. % nano clay and different weight percentages (1%, 2%, and 3%) of nano silica were also added. The blending process used twin-screw extrusion, and composite samples were subsequently produced by injection moulding. Various parameters including tensile, compressive, and impact strengths were analyzed. In particular, the hiPP composite containing 3 wt. % nano clay and 2 wt. % nano silica had significantly improved mechanical properties, showing a 37.5% increase in tensile strength, a 56.8% increase in flexural strength, and a 51.4% increase in impact strength. It exhibited the highest tensile (53.51 MPa), flexural (67.19 MPa), and impact strength (5.17 KJ/m2) among all tested composites, demonstrating superior
Thangavel, Anand, Ragupathy, K., Manivannan, S., Murali, M.
Technical Paper
Effect on Ageing and Cerium Addition on Magnesium Alloy2024-01-520912/10/2024
Magnesium (Mg) alloys are becoming ever more ubiquitous as the need for lighter and stronger alloys has increased significantly in the past decades. Mg alloy grade AZ91D is embedded in 0.5 of cerium have a high strength-to-weight ratio and lower specific density, which is useful in the case of automobile applications. An inconclusive study by Lagowski has shown that interrupted age hardening of AZ magnesium alloy increases the yield strength by around 10%. An investigation on the developed AZ91D+0.5Ce alloy subjected to various ageing treatments was carried out in this present study. The various aged samples were investigated by optical microscopy and scanning electron microscopy analysis. The yield strength was also evaluated quantitatively as a function of ageing parameters. A significant increase in yield strength and hardness values was observed in the artificially aged samples due to the precipitation of Mg17Al12 phases
Venkatesh, R., Manivannan, S., Das, A. Daniel, Mohanavel, Vinayagam, Soudagar, Manzoore Elahi Mohammad
Technical Paper
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