Browse Topic: Statistical analysis

Items (2,267)
Both automotive aftermarket vehicle modifications and Advanced Driver Assistance Systems (ADAS) are growing. However, there is very little information available in the public domain about the effect of aftermarket modifications on ADAS functionality. To address this deficiency, a research study was previously performed in which a 2022 Chevrolet Silverado 1500 light truck was tested in four different hardware configurations. These included stock as well as three typical aftermarket configurations comprised of increased tire diameters, a suspension level kit, and two different suspension lift kits. Physical tests were carried out to investigate ADAS performance of lane keeping, crash imminent braking, traffic jam assist, blind spot detection, and rear cross traffic alert systems. The results of the Silverado study showed that the ADAS functionality of that vehicle was not significantly altered by aftermarket modifications. To determine if the results of the Silverado study were
Bastiaan, JenniferMuller, MikeMorales, Luis
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 RithikaDas, SushantaGrant, JosephNaber, JeffreyBahramgiri, Mojtaba
Automated driving is an important development direction of the current automotive industry. Level 3 automated driving allows the driver to perform non-driving related tasks (NDRTs) during automated driving, however, once the operating conditions exceed the designed operating domain, the driver is still required to take over. Therefore, it is important to rationally design takeover requests (TORs) in Level 3 conditional automated driving. This paper investigates the effect of directional tactile guidance on driver takeover performance in emergency obstacle avoidance scenarios during the transfer of control from automated driving mode to manual driving. 18 participants drove a Level 3 conditional automated driving vehicle in a driving simulator on a two-way four-lane urban road, performed a takeover, and avoided obstacles while performing non-driving related tasks. The driver's takeover performance during the takeover process was measured and subjective driver evaluation data was
Liang, XinyingLiang, YunhanMa, XiaoyuanWang, LuyaoChen, GuoyingHu, Hongyu
To provide an affordable and practical platform for evaluating driving safety, this project developed and assessed 2 enhancements to an Unreal-based driving simulator to improve realism. The current setup uses a 6x6 military truck from the Epic Games store, driving through a pre-designed virtual world. To improve auditory realism, sound cues such as engine RPM, braking, and collision sounds were implemented through Unreal Engine's Blueprint system. Engine sounds were dynamically created by blending 3 distinct RPM-based sound clips, which increased in volume and complexity as vehicle speed rose. For haptic feedback, the road surface beneath each tire was detected, and Unreal Engine Blueprints generated steering wheel feedback signals proportional to road roughness. These modifications were straightforward to implement. They are described in detail so that others can implement them readily. A pilot study was conducted with 3 subjects, each driving a specific route composed of a straight
Duan, LingboXu, BoyuGreen, Paul
In future spark-ignition internal combustion engines, characterized by high compression ratios, issues such as knocking and super-knocking have increasingly emerged as major factors limiting thermal efficiency improvements. Ion current detection technology, with its advantages of not altering engine structure, low cost, and maintenance-free operation, is considered as one of the most promising methods for in-cylinder combustion detection. However, the mechanism of ion current formation under end gas auto-ignition conditions remains unclear, and the matching law between the ion current signal and the combustion state can only be obtained by experimental and statistical methods so far, posing challenges for abnormal combustion diagnostics and control based on ion current detection technology. To analyze the signal characteristics of ion current under abnormal combustion from a more intrinsic perspective, this paper develops a one-dimensional flame ionization model using MATLAB. The model
Zhou, YanxiongDong, GuangyuNi, XiaociXu, JieLi, XianLi, Liguang
Parts in automotive exhaust assembly are joined to each other using welding process. When the exhaust is subjected to dynamic loads, most of these weld joints experience high stresses. Hence it should be ensured that the exhaust assembly is designed to meet the requirements of exhaust durability for the estimated life of the vehicle. We also know that all parts used in manufacturing of exhaust system have inherent variations with respect to sheet metal thickness, dimensions and shape. Some parts like flex coupling and isolators have high variations in their stiffness based on their material and manufacturing processes. This all leads to a big challenge to ensure that the exhaust system meets the durability targets on a vehicle manufactured with all these variations. This works aims to evaluate the statistical spread in weld life of an exhaust with respect to inherent variations of its components. For the purpose of variational analysis, a Design of Experiments (DOE) is done where
Ramamoorthy, RajapandianBazzi, Ramzi
Wind tunnel calibration is necessary for repeatable and reproducible data for all industries interested in their output. Quantities such as wind speed, pressure gradients, static operating conditions, ground effects, force and moment measurements, as well as flow uniformity and angularity are all integral in an automotive wind tunnel’s data quality and can be controlled through appropriate calibration, maintenance, and statistical process control programs. The purpose of this technical paper is to (1) provide a basis of commonality for automotive wind tunnel calibration, (2) help customers and operators to determine the calibration standards best suited for their unique automotive wind tunnel and, (3) complement the American Institute of Aeronautics and Astronautics recommended practice R-093-2003(2018) Calibration of Subsonic and Transonic Wind Tunnels as specifically applied to the automotive industry. This document compiles information from various automotive wind tunnel customers
Bringhurst, KatlynnBest, ScottNasr Esfahani, VahidSenft, VictorStevenson, StuartWittmeier, Felix
Real-world data show that abdominal loading due to a poor pelvis-belt restraint interaction is one of the primary causes of injury in belted rear-seat occupants, highlighting the importance of being able to assess it in crash tests. This study analyzes the phenomenon of submarining using video, time histories, and statistical analysis of data from a Hybrid III 5th female dummy seated in the rear seat of passenger vehicles in moderate overlap frontal crash tests. This study also proposes different metrics that can be used for detecting submarining in full-scale crash tests. The results show that apart from the high-speed videos, when comparing time-series graphs of various metrics, using a combination of iliac and lap belt loads was the most reliable method for detecting submarining. Five metrics from the dynamic sensors (the maximum iliac moment, maximum iliac force drop in 1 ms, time for 80% drop from peak iliac force, maximum pelvis rotation, and lumbar shear force) were all
Jagtap, Sushant RJermakian, Jessica SEdwards, Marcy A
This paper is a continuation of a previous effort to evaluate the post-impact motion of vehicles with high rotational velocity within various vehicle dynamic simulation softwares. To complete this goal, this paper utilizes a design of experiments (DOE) method. The previous papers analyzed four vehicle dynamic simulation software programs; HVE (SIMON and EDSMAC4), PC-Crash and VCRware, and applied the DOE to determine the most sensitive factors present in each simulation software. This paper will include Virtual Crash into this methodology to better understand the significant variables present within this simulation model. This paper will follow a similar DOE to that which was conducted in the previous paper. A total of 32 trials were conducted which analyzed ten factors. Aerodynamics, a factor included in the previous DOE, was not included within this DOE because it does not exist within Virtual Crash. The same three response variables from the previous DOE were measured to determine
Roberts, JuliusCivitanova, NicholasStegemann, JacobBuzdygon, DavidThobe, Keith
Growth in the EV market is resulting in an unprecedented increase of electrical load from EV charging at the household level. This has led to concern about electric utilities’ ability to upgrade electrical distribution infrastructure at an affordable cost and sufficient speed to keep up with EV sales. Adoption of EVs in the California market has outpaced the national average and offers early insight for other regions of the United States. The Sacramento Municipal Utility District (SMUD) partnered with two grid-edge Distributed Energy Resource Management System (DERMS) providers, the OVGIP (recently incorporated as ChargeScape, a joint venture of Ford, BMW, Honda, and Nissan) and Optiwatt, to deliver a vehicle telematics-based active managed charging pilot. The pilot program, launched in Summer 2022 enrolled approximately 1,200 EVs over two years including Tesla, Ford, BMW, and GM vehicles. The goal of this pilot program was to evaluate the business case for managed charging to mitigate
Liddell, ChelseaSchaefer, WalterDreffs, KoraMoul, JacobKay, CarolAswani, Deepak
Gray cast iron (GJL) is one of the oldest cast iron materials and is still in use in many applications in the automotive industry due to its good characteristics, in relation to lubrication, heat conductivity and damping. Engine parts particularly benefit from these parameters. Nevertheless, the design of these components has always been challenging, in terms of maximizing material utilization for lightweight designs for components under cyclic loading. In particular, with regard to the influence of the statistical (component size), geometrical (notches) and technological (microstructural) size effects, the existing guidelines and literature lack the necessary information to provide a comprehensive understanding of the cyclic material behavior of GJL materials. Within a comprehensive study, different GJL materials have been investigated at Fraunhofer LBF to provide more detailed information regarding the influence of size effects on fatigue strength. Accordingly, a variety of specimen
Bleicher, ChristophKansy, Axel
This SAE Aerospace Standard (AS) establishes the minimum performance standards for equipment used as secondary alternating current (AC) electrical power sources in aerospace electric power systems.
AE-7B Power Management, Distribution and Storage
To meet the requirements of high-precision and stable positioning for autonomous driving vehicles in complex urban environments, this paper designs and develops a multi-sensor fusion intelligent driving hardware and software system based on BDS, IMU, and LiDAR. This system aims to fill the current gap in hardware platform construction and practical verification within multi-sensor fusion technology. Although multi-sensor fusion positioning algorithms have made significant progress in recent years, their application and validation on real hardware platforms remain limited. To address this issue, the system integrates BDS dual antennas, IMU, and LiDAR sensors, enhancing signal reception stability through an optimized layout design and improving hardware structure to accommodate real-time data acquisition and processing in complex environments. The system’s software design is based on factor graph optimization algorithms, which use the global positioning data provided by BDS to constrain
Zhan, KaiDiGao, ChengfaXu, DaweiLan, MinyiDing, Rongjing
Abrasive water jet (AWJ) machining is the most effective technology for processing various engineering materials particularly difficult-to-cut materials such as aluminum alloys, steels, brass, ceramics, composites, and the like. The present study focuses on the experimental study on surface roughness and kerf taper is carried out during AWJ machining of Al 6061-T6 alloy with 40 mm thickness, and the influence of process parameters includes water jet pressure, standoff distance, and abrasive flow rate on the kerf taper and surface roughness is analyzed. The number of experiments is designed using Taguchi’s L9 orthogonal array. Experimental results are statistically analyzed using ANOVA. Also gray relational analysis (GRA) coupled with principal component analysis (PCA) hybrid approach was implemented to optimize the performance parameters. From the results it is found that standoff distance and hydraulic jet pressure are the most influencing parameters on surface roughness and kerf
Kolluri, Siva PrasadSrikanth, V.Ismail, Sk.Bhanu, C.H.
The generation of data plays a vital role in machine learning (ML) techniques by providing the foundation for training and improvement of forecast models. As one application area for these models, in-vehicle systems, like vehicle diagnostics, have the potential to enhance the reliability and durability of vehicles by utilizing ML models in the testing phases. However, acquiring a high volume of quality onboard diagnostics (OBD) data is time-consuming and poses challenges like the risk of exposing sensitive information. To address this issue, synthetic data generation offers a promising alternative that is already in use in other domains. Thereby, synthetic data allows the exploitation of knowledge found in original data, ensuring the privacy of sensitive data, with less time costs of data acquisition. The application of such synthetically generated data could be found in predictive maintenance, predictive diagnostics, anomaly detection, and others. For this purpose, the research
Vučinić, VeljkoHantschel, FrankKotschenreuther, Thomas
This study tackles the issue of order delays in logistics using XGBoost for feature analysis and reinforcement learning for intelligent courier scheduling. Pickup order data from May 1 to October 31, 2023, in Chongqing is analyzed using spatio-temporal statistical methods. Key findings include that order placement peaks at 9:00 a.m., delays peak at 10:00 a.m., and the delay rate is 8.6%. A significant imbalance exists between the regional daily average of dispatchable couriers and order volumes.XGBoost is employed to predict order delays, revealing that pickup location is the most influential factor (27%), followed by courier pickup location (22%). These factors and their relationships are identified as key drivers of delays.To address these issues, a reinforcement learning-based courier scheduling optimization model is developed. The model defines courier location, current time, and pending orders as state variables and adopts an epsilon-greedy strategy for action selection
Wang, ManjunYu, Xinlian
Fused Deposition Modeling (FDM) is a widely recognized additive manufacturing method that is highly regarded for its ability to create complex structures using thermoplastic materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability. TPU has several applications, including automobile instrument panels, caster wheels, power tools, sports goods, medical equipment, drive belts, footwear, inflatable rafts, fire hoses, buffer weight tips, and a wide range of extruded film, sheet, and profile applications.. The primary objective of this study is to enhance the FDM parameters for TPU material and construct regression models that can accurately forecast printing performance. The study involved conducting experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses, including dimensional accuracy, surface quality, and mechanical
Pasupuleti, ThejasreeNatarajan, ManikandanSagaya Raj, GnanaSilambarasan, RKiruthika, Jothi
The world is moving towards a green transportation system. Governments are also pushing for green mobility, especially electric vehicles. Electric vehicles are becoming more popular in Europe, China, India, and developing countries. In EVs, the customer's range anxiety and the perceived real-world range are major challenges for the OEMs. The OEMs are moving towards a higher power-to-weight ratio. Energy density plays a crucial role in the battery pack architecture to increase the vehicle range. Higher capacity battery packs are needed to improve the vehicle's range. The battery pack architecture is vital in defining the gravimetric and volumetric energy densities. The cell-to-pack battery technique aims to achieve a higher power-to-weight ratio by eliminating unnecessary weight in the battery architecture. The design of battery architecture depends on the cell features such as the cell shape & size, cell terminal positions, vent valve position, battery housing strength requirements
K, Barathi Raja
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, independent of their level of hardness. Due to the increasing demand for superior products and the necessity for quick design modifications, decision-making in the manufacturing sector has become progressively more difficult. This study primarily examines the use of Haste alloy in vehicle applications and suggests creating regression models to predict performance parameters in ECM. The experiments are formulated based on Taguchi's ideas, and mathematical equations are derived using multiple regression models. The Taguchi approach is employed for single-objective optimization to ascertain the ideal combination of process parameters for optimizing the material removal rate. ANOVA is employed to evaluate the statistical significance of process parameters that impact performance indicators. The proposed regression models for Haste alloy are more versatile
Natarajan, ManikandanPasupuleti, ThejasreeD, PalanisamySilambarasan, RKrishnamachary, PC
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has emerged as a revolutionary method for fabricating complex geometries using a variety of materials. Polyethylene terephthalate glycol (PETG) is a thermoplastic material that is biodegradable and environmentally friendly, making it a preferred choice in additive manufacturing (AM) due to its affordability and ease of use. This study aims to optimize the FDM settings for PETG material and investigate the impact of key process parameters on printing performance. An experimental study was conducted to evaluate the influence of crucial factors in FDM, including layer thickness, infill density, printing speed, and nozzle temperature, on significant outcomes such as dimensional accuracy, surface quality, and mechanical properties. The use of the Grey Relational Analysis (GRA) approach enabled a systematic assessment of multi-performance characteristics, facilitating the optimization of the FDM process. The findings
Pasupuleti, ThejasreeNatarajan, ManikandanKumar, VKiruthika, JothiKatta, Lakshmi NarasimhamuSilambarasan, R
In response to rising emissions and pollutants, an alternative and environmentally friendly synthesis is gaining prominence on the energy sources. The leather industries generate substantial amount of waste and fleshing oil extracted from fleshing which is rich in lipids and presents a viable feedstock for biodiesel production. In this research work, Response Surface Methodology (RSM) is used to optimize the conversion of leather fleshing oil into biodiesel using three parameters such as operating temperature, reaction time, and molar ratio. Experiments were carried out to determine the most optimal conditions and the response on yield (%) and viscosity (mm2/s) based on a 17-run Box–Behnken Design matrix. Stochastic model parameters such as R2 (0.9715 and 0.9793), adjusted R2 (0.9349 and 0.9527), predicted R2 (0.8327 and 0.7656), and high F-values (26.52 and 36.78) of both responses (yield and viscosity) were found to be statistically significant and warranted model adequacy. ANOVA and
P, KanthasamySelvan, Arul MozhiP, Shanmugam
Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal
Pasupuleti, ThejasreeNatarajan, ManikandanRaju, DhanasekarKrishnamachary, PCSilambarasan, R
In recent years, Additive Manufacturing (AM), more especially Fused Deposition Modeling (FDM), has emerged as a very promising technique for the production of complicated forms while using a variety of materials. Polyethylene Terephthalate Glycol, sometimes known as PETG, is a thermoplastic material that is widely used and is renowned for its remarkable strength, resilience to chemicals, and ease of processing. Through the use of Taguchi Grey Relational Analysis (GRA), the purpose of this investigation is to improve the process parameters of the FDM technology for PETG material. In order to investigate the influence that several FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, have on significant outcome variables, such as dimensional accuracy, surface quality, and mechanical qualities, an empirical research was conducted. For the purpose of constructing the regression prediction model, the obtained dataset is used to make
Natarajan, ManikandanPasupuleti, ThejasreeShanmugam, LoganayaganKatta, Lakshmi NarasimhamuSilambarasan, RKiruthika, Jothi
Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several
Natarajan, ManikandanPasupuleti, ThejasreeC, NavyaKiruthika, JothiSilambarasan, R
Closed-loop combustion control is highly beneficial for improving the efficiency and reducing the emissions of spark ignition internal combustion engines. In this paper, the key parameter (CA50) of closed-loop combustion control and its effect on the combustion and emissions were explored experimentally in a six-cylinder hydrogen enriched compressed natural gas (HCNG) engine. Moreover, the particle swarm optimization (PSO) back propagation neural network (BPNN) algorithm improved by various hybrid strategies was employed for CA50 prediction. The experimental results reveal that CA50 has a significant impact on the combustion characteristics and emissions of the HCNG engine. Meanwhile, statistical analysis illustrates that CA50 follows a normal distribution and has no self-correlation. Considering the one-to-one correspondence between CA50 and the spark timing, it is suitable to select CA50 as the feedback parameter. The simulation results indicate that the CA50 prediction model
Duan, HaoYan, YuRen, XianfengYin, XiaojunWang, JinhuaZeng, Ke
This article provides a comprehensive review of existing literature on AI-based functions and verification methods within vehicular systems. Initially, the introduction of these AI-based functions in these systems is outlined. Subsequently, the focus shifts to synthetic environments and their pivotal role in the verification process of AI-based vehicle functions. The algorithms used within the AI-based functions focus primarily on the paradigm of deep learning. We investigate the constituent components of these synthetic environments and the intricate relationships with vehicle systems in the verification and validation domain of the system. In the following, alternative approaches are discussed, serving as complementary methods for verification without direct involvement in synthetic environment development. These approaches include data-oriented methodologies employing statistical techniques and AI-centric strategies focusing solely on the core deep learning algorithm.
Aslandere, TurgayDurak, Umut
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, NarendraOlcmen, SemihKolhe, Pankaj
In the automotive industry, a good vehicle is one that not only provides comfort and adequate on-road performance but also ensures safety for its users. Therefore, various standards have been created to qualify and ensure that cars meet minimum requirements. Assays include frontal and side impact tests. However, physical tests end up being costly if performed frequently, and thus, increasing the correlation between these and computational simulations has been explored in recent years. Within the computational scope, given the nonlinear nature of the functions involved in such studies, the use of metaheuristics (MH) with constraint handling techniques (CHT) has been employed to obtain better results for such scenarios. In this work, three MH algorithms are used: Archimedean Optimization (AOA), Sine-Cosine Algorithm (SCA), and Dung Beetle Optimization (DBO). They are coupled with CHTs of the penalty methods (PM) type in their most basic character, such as Static Penalty Method (SPM
Souza Silva, PauloDezan, Daniel JonasFerreira, Wallace Gusmão
During the early phase of vehicle development, one of the key design attributes to consider is the trunk. Trunk is the pillar that is responsible for user’s accommodate their baggage and make into customer needs in engineer metrics. Therefore, it is one of the key requirements to be considered during the vehicle design. Certain internal vehicle trunk characteristics such as the trunk height and length are engineer metrics that influence the occupants’ perception for trunk. One specific characteristic influencing satisfaction is the rear opening width lower for notch back segment, which is the subject of this paper. The objective of this project is to analyze the relationship between the rear opening width lower with the occupant’s satisfaction under real world driving conditions, based on research, statistical data analysis and dynamic clinics.
Santos, Alex CardosoSilva, GustavoGenaro, PieroTerra, RafaelPádua, AntônioBenevente, RodrigoLourenço, Sergio
Thoracic injuries, most frequently rib fractures, commonly occur in motor vehicle crashes. With an increased reliance on human body models (HBMs) for injury prediction in various crash scenarios, all thoracic tissues and structures require more comprehensive evaluation for improvement of HBMs. The objective of this study was to quantify the contribution of costal cartilage to whole rib bending properties in physical experiments. Fifteen bilateral pairs of 5th human ribs were included in this study. One rib within each pair was tested without costal cartilage while the other rib was tested with costal cartilage. All ribs were subjected to simplified A-P loading at 2 m/s until failure to simulate a frontal thoracic impact. Results indicated a statistically significant difference in force, structural stiffness, and yield strain between ribs with and without costal cartilage. On average, ribs with costal cartilage experienced a lower force but greater displacement with a longer time to
Schaffer, RoseKang, Yun-SeokMarcallini, AngeloPipkorn, BengtBolte, John HAgnew, Amanda M
In recent years, the automotive industry has been undergoing constant evolution, and to keep up with market trends, it’s necessary to seek better performance in the shortest time possible. Therefore, CAE (Computer Aided Engineering) becomes one of the most efficient tools for this purpose, as it can predict failures and/or improvements virtually before the start of tooling manufacturing. Thus, optimization, a process in which the best value of a parameter is obtained, becomes essential in the CAE field. In this work, Design of Experiments (DOE) will be applied, a methodology that, using applied statistics, plans, conducts, analyzes, and interprets controlled tests, evaluating predefined parameters from different areas. A light vehicle skid plate will be the case study, impacting disciplines such as durability, NVH (Noise, Vibration and Harshness), and aerodynamics, in virtual analyses such as stiffness, vibration modes, and water fording. Using the resources provided by Renault, this
dos Santos Magalhães, Daniella FernandaMoura, Vitor LoicBraga Junior, Francisco EstevanatoAndrade Barbosa, Samuel França MouraTheulen Mueller, André Marcelo
Wire Electrical Discharge Machining (WEDM) is a widely used manufacturing method that is employed to shape complex geometries in conductive materials such as cupronickel, which is highly regarded for its resistance to corrosion and ability to conduct heat. The aspiration of this investigation is to improve the effectiveness and accuracy of Wire Electrical Discharge Machining (WEDM) for cupronickel material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study analyzes the impact of WEDM parameters, specifically pulse-on time, pulse-off time, and discharge current, on important machining outcomes such as surface roughness, material removal rate. Experimental trials are performed to collect data on these parameters and their corresponding machining characteristics. The TOPSIS optimization method is utilized to determine the most favourable parameter settings by evaluating each parameter combination against the ideal and
Pasupuleti, ThejasreeNatarajan, ManikandanKiruthika, JothiC, NavyaSilambarasan, R
Wire Electrical Discharge Machining (WEDM) is an essential manufacturing process used to shape complex geometries in conductive materials such as cupronickel, which is valued for its corrosion resistance and electrical conductivity. The aim of this explorative study is to enhance the efficiency and precision of machining by creating a specialized predictive model using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for cupronickel material. The study examines the intricate correlation between process variables of the WEDM (Wire Electrical Discharge Machining) technique, such as pulse-on time (Ton), pulse-off time (Toff), and discharge current, and crucial machining responses, including surface roughness, material removal rate. Data is collected through systematic experimentation in order to train and validate the ANFIS predictive model. The ANFIS model utilizes the collective learning capabilities of neural networks and fuzzy logic systems to precisely forecast machining responses by
Pasupuleti, ThejasreeNatarajan, ManikandanKiruthika, JothiKatta, Lakshmi NarasimhamuSilambarasan, R.
Additive Manufacturing (AM), specifically Fusion Deposition Modeling (FDM), has transformed the manufacturing industry by allowing the creation of complex structures using a wide range of materials. The objective of this study is to enhance the FDM process for Thermoplastic Polyurethane (TPU) material by utilizing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) optimization method. The study examines the influence of FDM parameters, such as layer height, nozzle temperature, and infill density, on important characteristics of the printing process, such as tensile strength, flexibility, and surface finish. The collection of experimental data is achieved by conducting systematic FDM printing trials that cover a variety of parameter combinations. The TOPSIS optimization method is utilized to determine the optimal parameter settings by evaluating each parameter combination against the ideal and anti-ideal solutions. This method determines the optimal parameter
Pasupuleti, ThejasreeNatarajan, ManikandanKiruthika, JothiRamesh Naik, MudeSilambarasan, R
Looking at the current scenario in transportation industry, in majority of the conventional powertrains, internal combustion (IC) engines fueled by diesel serve as the powerhouse. In all locomotives driven by IC engine, it is essential to monitor critical engine parameters to ensure good engine health and performance. Exhaust temperature of engine is a very critical parameter which gives the information about in-cylinder combustion. In traditional diesel engine layouts, exhaust temperature measurement relies on physical temperature sensor. The proposed methodology is focused on applying the data driven models for providing an estimated value of the exhaust temperature. Based on the estimated value of exhaust temperature, this technique can be used to monitor the IC engine. This methodology uses an advanced Artificial Intelligence technique for providing an accurate estimate of exhaust gas temperature. Real world vehicle data was used for training, validating, and testing the data
Jagtap, Virendra ShashikantGanguly, GouravMitra, Partha
Extreme out-of-position pre-crash postures may need high-force pre-pretensioner (PPT) for effective repositioning (Mishra et al., 2023). To avoid applying a high force on the chest, we hypothesized that in case of these extreme postures the PPT may be activated in the absence of a pre-crash motion as a cautionary measure. Therefore, the aims of this study were: (1) to understand the effect of the PPT in repositioning a forward-leaning occupant in static conditions and (2) to characterize occupants’ kinematic variability during repositioning. Sixteen healthy volunteers (8 males, 8 females, 23.8 ± 4.2 years old) were seated with a 40° forward posture on a vehicle seat and restrained with a 3-point seat belt equipped with a PPT. Two PPT seatbelt conditions were examined: low PPT (100 N) and high PPT (300 N). Head and trunk rearward displacements relative to the initial forward-leaning position at 350 ms from PPT onset were collected with a 3D motion-capture system and compared between
Witmer, MaitlandGriffith, MadelineGraci, Valentina
The Reactivity Control Compression Ignition (RCCI) engine, with its dual fuel system and coordinated injection strategy, offers superior emission control and fuel efficiency compared to conventional diesel engines. However, cyclic variations leading to engine combustion instability poses a significant challenge to their development and commercialization. In this study, statistical (COV and Histogram) and nonlinear dynamic (Recurrence Plot and its Quantification) analysis techniques are applied on the time-series data obtained from a single-cylinder diesel engine modified to operate in CNG-Diesel RCCI mode. The engine, while advancing the main injection timing (SOI-2), is tested under various operating conditions, including different engine loads, direct injection mass ratios (DIMR) and port fuel injection (PFI) masses, to help identify the configurations with better temporal correlations and deterministic traits. Such configurations hold potential for control strategy implementation
Prashar, RajatKumar, Kamal S.Yadav, Ratnesh KumarMaurya, Rakesh Kumar
The American Petroleum Institute’s (API) Single Technology Matrix (STM) is a data-based, Virtual Testing process and protocol (utilizes test data, characteristics and features of base stocks and blends coupled with statistical methods and analysis) used to predict the performance capability of a specific engine oil additive technology in a single specified base oil, in a given engine test. The concept was first introduced in 2002, codified and implemented by API in 2007, and updated in 2022. The previously published advantages of STM in the proof-of-performance of engine oils, remain relevant. These advantages include a data space focused on interpolation, documented statistical analysis protocol, limitation to a specific formulation, flexibility in understanding complicated, interactive, or non-linear technology and base oil relationships, and timeliness. There have been numerous changes to, and in, the engine oil industry since the introduction of STM in 2007. These include advances
Zielinski, ChristineScinto, PhilipChen, MinGibbons, GreerBaker, Charles
The healthcare industry is evolving and facing two major challenges. First, the rise of chronic diseases. By 2050, chronic diseases such as cardiovascular diseases, cancer, diabetes, and respiratory illnesses could account for 86 percent of the 90 million deaths each year, according to the World Health Organization (WHO) in its 2023 World Health Statistics report. This increase is due to factors such as an aging population, lifestyle changes, and risk factors like high blood pressure, high blood sugar, and air pollution. Consequently, this creates a second challenge: added strain on healthcare resources. To address this, WHO recommends tackling the root causes of chronic diseases, promoting healthier behaviors, and ensuring universal access to healthcare resources.
Within the heavy commercial vehicle sector, fleet availability stands as a crucial factor impacting the productivity and competitiveness of companies. Despite this, the core element of maintenance strategies applied in the sector still relies solely on mileage or component usage time. On the other hand, the evolution of the industry, particularly the advancement of Industry 4.0 enabling technologies such as sensorization embedded in components, now provides a vast amount of operational data. The severity levels of application, driving style influence, and vehicle operating conditions can be indicated through the treatment of these data. However, there is still little practical application of using this data for effective decision-making regarding maintenance strategy in the sector, correlating the severity level with component failure possibility. Seeking a disruptive approach to this scenario where data analysis supports decisions related to component maintenance strategy, a
de Moraes Seixas, Ricardo
The present study explores the performance of high-density polyethylene (HDPE) pyrooil and ethanol blends with gasoline in SI engine using statistical modeling and analysis using response surface methodology (RSM) and the Anderson–Darling (AD) residual test. The pyrooil was extracted from HDPE through pyrolysis at 450°C and then distilled to separate the liquid fraction. Two blends were prepared by combining pyrooil and gasoline, and pyrooil–ethanol mixture (volume ratio of 9:1) and gasoline, both at volumetric concentrations ranging from 2% to 8% to evaluate brake thermal efficiency (BTE) and specific fuel consumption (SFC) in a SI engine. An experimental matrix containing speed, torque, and blend ratio as independent variables for both blends were designed, analyzed, and optimized using the RSM. The results show that a 4% blend of pyrooil with gasoline (P4) and a 6% blend of pyrooil–ethanol mixture with gasoline (P6E) were optimum for an SI engine. Also, the experimental findings
Manickavelan, K.Sivaganesan, S.Sivamani, S.Kulkarni, Mithun V.
Typically, machine learning techniques are used to realise autonomous driving. Be it as part of environment recognition or ultimately when making driving decisions. Machine learning generally involves the use of stochastic methods to provide statistical inference. Failures and wrong decisions are unavoidable due to the statistical nature of machine learning and are often directly related to root causes that cannot be easily eliminated. The quality of these systems is normally indicated by statistical indicators such as accuracy and precision. Providing evidence that accuracy and precision of these systems are sufficient to guarantee a safe operation is key for the acceptance of autonomous driving. Usually, tests and simulations are extensively used to provide this kind of evidence. However, the basis of all descriptive statistics is a random selection from a probability space. A major challenge in testing or constructing the training and test data set is that this probability space is
Wiesbrock, Hans WernerGrossmann, Jürgen
In the realm of transportation science, the advent of deep learning has propelled advancements in predicting longitudinal driving behavior. This study explores the application of deep neural network architectures, specifically long–short-term memory (LSTM) and convolutional neural networks (CNNs), recognized for their effectiveness in handling sequential data. Using a 3-s temporal window that includes past vehicle progress, speed, and acceleration, the proposed model, a hybrid LSTM–CNN architecture, predicts the vehicle’s speed and progress for the next 6 s. The approach achieves state-of-the-art performance, particularly within a 4 s horizon, but remains competitive even for longer-term predictions. This is achieved despite the simplicity of its input space, which does not include information about vehicles other than the target vehicle. As a result, while its performance may decrease slightly for longer-term predictions due to the lack of environmental information, it still offers
Lucente, GiovanniMaarssoe, Mikkel SkovKahl, IrisSchindler, Julian
In 2023, the European Union set more ambitious targets for reducing greenhouse gas emissions from passenger cars: the new fleet-wide average targets became 93.6 g/km for 2025, 49.5 g/km in 2030, going to 0 in 2035. One year away from the 2025 target, this study evaluates what contribution to CO2 reduction was achieved from new conventional vehicles and how to interpret forecasts for future efficiency gains. The European Commission’s vehicle efficiency cost-curves suggest that optimal technology adoption can guarantee up to 50% CO2 reduction by 2025 for conventional vehicles. Official registration data between 2013 and 2022, however, reveal only an average 14% increase in fuel efficiency in standard combustion vehicles, although reaching almost 23% for standard hybrids. The smallest gap between certified emissions and best-case scenarios is of 14 g/km, suggesting that some manufacturers’ declared values are approaching the optimum. Yet, the majority of vehicles do not appear to fully
Komnos, DimitriosNur, JamilTansini, AlessandroKtistakis, Markos AlexandrosSuarez, JaimeKrause, JetteFontaras, Georgios
The present study discusses the determination of the Seal drag force in the application where an elastomeric seal is used with a metallic interface in the presence of different fluids. An analytical model was constructed to predict the seal drag force and an experimental test was performed to check the fidelity of the analytical model. A Design of Experiment (DoE) was utilized to perform an experimental test considering different factors affecting the Seal drag force. Statistical tools such as the Test for Equal Variances and One Way Analysis of Variance (ANOVA) were used to draw inferences for the population based on samples tested in the DoE test. It was observed that Glycol fluids lead to lubricant wash-off resulting in increased seal drag force. Additionally, non-lubricated seals tend to show higher seal drag force as compared to lubricated seals.
Yarolkar, MakrandTelore, MilindPatil, Sandip
A structural load estimation methodology was developed for RLV-TD HEX-01 hypersonic experimental mission, the maiden winged body technology demonstrator vehicle of ISRO. Primarily the method evaluates time history of station loads considering effects of vehicle dynamics and structural flexibility. Station loads of critical structures are determined by superposition of quasi-static aerodynamic loads, dynamic inertia loads, control surface loads and propulsion loads based on actual physics of the system, improving upon statistical load combination approaches. The technique characterizes atmospheric regime of flight from vehicle loads perspective and ensures adequate structural margin considering atmospheric variations and system level perturbations. Features to estimate change in loads due to wind variability and atmospheric turbulence are incorporated into the load estimation methodology. Augmentation in loads due to structural flexibility is assessed along the trajectory using vehicle
Jayan, MahindPavanasam, Ashok GandhiDaniel, Sajan
Aluminum and its alloys entered a main role in the engineering sectors because of their applicable characteristics for indispensable applications. To enhance requisite belongings for the components, the composition of variant metal/nonmetal with light metal alloys is essential in the manufacturing industries. To enhance the wear resistance with significant strength property of the aluminum alloy 2024, the reinforcement SiC and fly ash (FA) were added with the designation Al2024 + 10% SiC; Al2024 + 5% SiC + 5% FA; and Al2024 + 10% FA via stir-casting technique. The wear resistance property of the composites was tested in pin-on-disc with a dry-sliding wear test procedure. The experiment trials were designed in Box–Behnken design (BBD) by differing the wear test parameters like % of reinforcement, sliding distance (m), and load (N). The wear tests on casted samples were carried out at the constant velocity of 2 m/sec, such that the corresponding wear rate for the experiment trials was
Sivakumar, N.Sireesha, S. C.Raja, S.Ravichandran, P.Sivanesh, A. R.Aravind Kumar, R.
The objectives of this study were to provide insights on how injury risk is influenced by occupant demographics such as sex, age, and size; and to quantify differences within the context of commonly-occurring real-world crashes. The analyses were confined to either single-event collisions or collisions that were judged to be well-defined based on the absence of any significant secondary impacts. These analyses, including both logistic regression and descriptive statistics, were conducted using the Crash Investigation Sampling System for calendar years 2017 to 2021. In the case of occupant sex, the findings agree with those of many recent investigations that have attempted to quantify the circumstances in which females show elevated rates of injury relative to their male counterparts given the same level bodily insult. This study, like others, provides evidence of certain female-specific injuries. The most problematic of these are AIS 2+ and AIS 3+ upper-extremity and lower-extremity
Dalmotas, DainiusChouinard, AlineComeau, Jean-LouisGerman, AlanRobbins, GlennPrasad, Priya
For taking counter measures in advance to prevent accidental risks, it is of significance to explore the causes and evolutionary mechanism of ship collisions. This article collects 70 ship collision accidents in Zhejiang coastal waters, where 60 cases are used for modeling while 10 cases are used for verification (testing). By analyzing influencing factors (IFs) and causal chains of accidents, a Bayesian network (BN) model with 19 causal nodes and 1 consequential node is constructed. Parameters of the BN model, namely the conditional probability tables (CPTs), are determined by mathematical statistics methods and Bayesian formulas. Regarding each testing case, the BN model’s prediction on probability of occurrence is above 80% (approaching 100% indicates the certainty of occurrence), which verifies the availability of the model. Causal analysis based on the backward reasoning process shows that H (Human error) is the main IF resulting in ship collisions. The causal chain that maximizes
Tian, YanfeiQiao, HuiHua, LinAi, Wanzheng
The proposed Euro 7 regulation includes On Board Monitoring, or OBM, to continuously monitor vehicles for emission exceedances. OBM relies on feedback from existing or additional sensors to identify high emitting vehicles, which poses many challenges. Currently, sensors are not commercially available for all emissions constituents, and the accuracy of available sensors is not capable enough for in use compliance determination. On board emissions models do not offer enough fidelity to determine in use compliance and require new complex model innovation development which will be extremely complicated to implement on board the vehicle. The stack up of multi-component deterioration leading to an emissions exceedance is infeasible to detect using available sensors and models. An assessment of limitations and measurement accuracy for sensors or models including oxides of nitrogen (NOx) sensors under a variety of operating conditions, ammonia (NH3) sensing, and particulate matter (PM) sensing
Funk, SarahPotter, JaneanPruski, Erika
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