Browse Topic: Quality, Reliability, and Durability
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
In the ongoing Road Load Data Acquisition (RLDA) for engine mounts, a load cell arrangement is being utilized, where the load cell must be placed between the mount arm and an engine mount bracket or an additional tower bracket. This configuration required the design of a custom mount arm with a crank in the Z direction, secured with a single bolt to accommodate the load cell. However, this method has revealed significant load coupling in the X and Z directions, resulting in incorrect load prediction for engine mount testing. This happens due to the architectural packaging of the engine mount on the long member to meet NVH requirements. To mitigate these issues, an alternative strain gauge-based RLDA approach was investigated. The optimal locations for strain gauge placement were determined using the inverse matrix method with the assistance of Computer-Aided Engineering (CAE) analysis. Strain gauges were then installed at these identified locations on the mount arm. The engine mount
In a three-phase voltage source inverter, in order to prevent the direct short circuit of the upper and lower tubes of the bridge arm and ensure the normal operation of the inverter, microsecond-level dead time needs to be added when the power devices are turned on and off. However, due to the dead-time effect, slight distortion may occur in the inverter within the modulation period, and this distortion will eventually lead to harmonic components in the output current after accumulation, thereby generating torque ripple. Against the above background, implementing dead-time compensation strategies is very important. To compensate for the voltage error caused by the dead-time effect, current polarity determination is required first. Then, the dead time is compensated, thereby indirectly compensating for the voltage error caused by the dead-time effect. Regarding the dead-time compensation time, without changing the hardware, this paper proposes a solution to turn off the dead-time
The half vehicle spindle-coupled multi-axial input durability test has been broadly used in the laboratory to evaluate the fatigue performance of the vehicle chassis systems by automotive suppliers and OEMs. In the lab, the front or rear axle assembly is usually held by fixtures at the interfaces where it originally connects to the vehicle body. The fixture stiffness is vital for the laboratory test to best replicate the durability test in the field at a full vehicle level especially when the subframe of the front or rear axle is hard mounted to the vehicle body. In this work, a multi-flexible body dynamics (MFBD) model in Adams/Car was utilized to simulate a full vehicle field test over various road events (rough road, braking, steering). The wheel center loads were then used as inputs for the spindle coupled simulations of the front axle with a non-isolated subframe. Three types of fixtures including trimmed vehicle body, a rigid fixture with softer connections and a rigid fixture
This paper reviews the current situation in the terms and definitions that influence the development of testing and prediction in automotive, aerospace and other areas of engineering. The accuracy of these terms and definitions is very important for correct simulation, testing and prediction. This paper aims to define accurate terms and definitions. It also includes the author’s recommendations for improving this situation and preparing new standards.
Nowadays, more than in the recent decades, the design process for the body in white for passenger cars is driven by efficiency. This results in the enhanced usage of large-scale cast components made of aluminum, for the battery compartment, the front or rear body and other components. While the automotive industry is striving towards even larger structures made with so-called “Giga-Casting”, challenges in the casting and supply chain processes, but also maintenance and repair processes of these large structures, arise. Other tasks to solve might follow from controlling local microstructures, and thus the strength of the parts, when the flow length of the molten metal increases with component size, especially in relation to an increased fraction of recycled aluminum. Within the Fraunhofer-internal project “FutureCarProduction”, focus is directed towards understanding what drives efficiency, availability and sustainability of modern processes for the production of a car body. Moreover
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
In Automobile manufacturing, maintaining the Quality of parts supplied by vendor is crucial & challenging. This paper introduces a digital tool designed to monitor trends for critical parameters of these parts in real-time. Utilizing Statistical Process Control (SPC) graphs, the tool continuously tracks Quality trend for critical parts and process parameters, predicting potential issues for proactive improvements even before parts are supplied. The tool integrates data from all Supplier partners across value chain into a single ecosystem, providing a comprehensive view of their performance and the parts they supply. Suppliers input data into a digital application, which is then analyzed in the cloud using SPC techniques to generate potential alerts for improvement. These alerts are automatically sent to both Suppliers and relevant personnel at the OEM, enabling proactive measures to address any Quality deviations. 100% data is visualized in an integrated dashboard which acts as a
At present, electric head restraints have been developed locally, so overseas mechanisms are used. In this study, two concept mechanisms were developed, and in addition, one patent for a wing-out head restraint mechanism was additionally applied. The new mechanism has had an excellent effect on cost reduction and improvement of operating noise compared to the current one.
High-efficiency manufacturing involves the transmission of copious amounts of data, exemplified both by trends in the automotive industry and advances in technology. In the automotive industry, products have been growing increasingly complex, owing to multiple SKUs, global supply chains and the involvement of many tier 2 / Just-In Time (JIT) suppliers. On top of that, recalls and incidents in recent years have made it important for OEMs to be able to track down affected vehicles based on their components. All of this has increased the need for OEMs to be able to collect and analyze component data. The advent of Industry 4.0 and IoT has provided manufacturing with the ability to efficiently collect and store large amounts of data, lining up with the needs of manufacturing-based industries. However, while the needs to collect data have been met, corporations now find themselves facing the need to make sense of the data to provide the insights they need, and the data is often unstructured
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
Simulated distillation (SimDis) uses wide bore capillary gas chromatography (GC) to provide a detailed volatility profile of blended gasoline. The boiling point distribution from SimDis analysis is correlated to the hydrocarbon contents of spark ignition fuels and provide the resolution necessary to characterize the compositions of the fuel. Recent publications on simulated distillation applied to spark ignition fuel reveal the merits of indexing a gasoline fuel so that it can be correlated to the tendency of particulate emissions from vehicles. With this in mind, SimDis can be a useful and quick tool in assessing the PM-formation potential of market gasolines. Heavy aromatic compounds are compounds identified as having at least 10 Carbons and 1 aromatic ring. These compounds that are present in spark ignition fuels are major contributors to vehicle particulate emissions. These compounds can be found in the higher boiling portion (T70+) of the distillation profiles. As demonstrated in
The trends of intelligence and connectivity are continuously driving innovation in automotive technology. With the deployment of more safety-critical applications, the demand for communication reliability in in-vehicle networks (IVNs) has increased significantly. As a result, Time-Sensitive Networking (TSN) standards have been adopted in the automotive domain to ensure highly reliable and real-time data transmission. IEEE 802.1CB is one of the TSN standards that proposes a Frame Replication and Elimination for Reliability (FRER) mechanism. With FRER, streams requiring reliable transmission are duplicated and sent over disjoint paths in the network. FRER enhances reliability without sacrificing real-time data transmission through redundancy in both temporal and spatial dimensions, in contrast to the acknowledgment and retransmission mechanisms used in traditional Ethernet. However, previous studies have demonstrated that, under specific conditions, FRER can lead to traffic bursts and
Cam gear is a critical component of the timing system in an internal combustion engine, ensuring the synchronized opening of the engine valves, pistons, and rotating parts, but their unavailability may result in long-term downtime or expensive replacement. Reverse engineering (RE) systems also play an important role in promoting sustainable practices projects in automotive technologies. The study focuses on presenting a proposed method for redesigning damaged parts in engines using image processing technology by creating an-accurate CAD model. In addition to clarifying of the expected causes that led to cam gear damage. The proposed method involves taking a high-resolution image of the damaged part, then applying advanced image processing algorithms to analyze and reconstruct the geometry of the part. The data is then converted into a high-resolution 3D CAD model. This approach aims to address the challenges of replicating worn or broken parts, providing a cost-effective maintenance
This paper examines the challenges and mechanisms for ensuring Freedom from Interference in Adaptive AUTOSAR-based platforms, with a focus on managing Memory, Timing, and Execution challenges. It explores the robust safety mechanisms in Classic AUTOSAR that ensure Freedom from Interference and the significant challenges in achieving interference-free operation in Adaptive AUTOSAR environments while adhering to ISO26262 standards. The study emphasizes strategies for managing complexities and outlines the multifaceted landscape of achieving interference-free operation. Additionally, it discusses ASIL-compliant Hypervisor, memory partitioning, and Platform Health Management as mechanisms for ensuring safety execution. The paper also raises open questions regarding real-time problems in live projects that are not solved with existing safety mechanisms. Adaptive AUTOSAR plays a crucial role in the development of autonomous and connected vehicles, where functional safety is of utmost
In automotive engineering, seam welds are frequently used to join or connect various parts of structures, frames, cradles, chassis, suspension components, and body. These welds usually form the weaker material link for durability and impact loads, which are measured by lab-controlled durability and crash tests, as well as real-world vehicle longevity. Consequently, designing robust welded components while optimizing for material performance is often prioritized as engineering challenge. The position, dimensions, material, manufacturing variation, and defects all affect the weld quality, stiffness, durability, impact, and crash performance. In this paper, the authors present best practices based on studies over many years, a rapid approach for optimizing welds, especially seam welds, by adopting Design For Six Sigma (DFSS) IDDOV (Identify, Define, Develop, Optimization, and Verification) discrete optimization approach. We will present the case testimony to show the approach throughout
Airline passenger satisfaction is important for airline operation service quality management. When airline companies carry out advertisement campaigns or plan a marketing strategy, the resources and budgets are not unlimited. Thus, an airline can only focus on improving a few factors that drive passenger satisfaction. To understand the key satisfies for the young and the old adults, respectively, we leverage five airline passenger satisfaction methods to identify the key factors that explain the airline service satisfaction of different passengers. In particular, we investigate and compare the ridge and the Lasso regularization in terms of the resulting model’s sparsity and computational efficiency. The top three important factors that influence the old’s satisfaction are departure and arrival time convenience, legroom service, and baggage handling. Our findings indicate that the young people place a higher value on entertainment, while the old adults place a higher value on usefulness
The advancement of autonomous driving perception frequently necessitates the aggregation of data, its subsequent annotation, the implementation of training procedures, and other related activities. In contrast, the utilisation of synthetic data obviates the necessity for data collection, annotation, and the generation of accurate and reliable labels. Its incorporation into the development process is anticipated to streamline the entire algorithmic development process. In this study, we propose a novel approach utilising the Blender software to create a virtual representation of an underground car park and develop an automated parking dataset. The utilisation of virtual simulation technology enables the generation of diverse and high-quality training data, thereby addressing the challenge of acquiring data in the actual scene. The experimental results demonstrate that the model trained based on the synthetic dataset exhibits superior performance in the automatic parking task, thereby
Monitoring changes in pavement material compaction degree and analyzing the interaction mechanism between particles are essential for improving compaction quality. In this paper, an on-site intelligent compaction test was carried out using intelligent sensor, the correlation between the in-situ test results and the intelligent compaction measurement value (ICMV) was written, and the influences of moisture content on the correlations were discussed. Further, the gyratory compaction tests were carried out using smart aggregate (SA) sensors to investigate the characteristics of the sensing results during the gyratory compaction of mixtures with different moisture contents, revealing the interaction mechanism between particles. Finally, the compaction characteristic indexes CEI, CDI and CSI were proposed using the SA sensing results, which were used to characterize the flow, compaction degree and stability characteristics of the mixtures, respectively. The conclusions of the study are of
This study introduces a probabilistic analysis approach to evaluate the gear tooth strength for the hypocycloid engines, which are particularly significant in internal combustion (IC) engine applications due to their unique design and critical requirements for both efficiency and durability. The research utilizes the stress–strength interference (SSI) theory within a “design for reliability” framework to develop a robust methodology for designing the internal gear mechanism required for the hypocycloid gear mechanism (HGM) engine, in accordance with American Gear Manufacturers Association (AGMA) standard gear rating practices. This approach incorporates probabilistic factors to address variations in HGM component parameters, gear material properties, and engine operational conditions. To validate the design and ensure accuracy, a finite element method (FEM)-based verification is employed, to identify potential failure points and enhance the overall reliability of the HGM engine. The
The sound generated by electric propulsion systems differs compared to the prevalent sound generated by combustion engines. By exposing listeners to various sound situations, the manufacturer can start understanding which direction to take to achieve compelling battery electric vehicle trucks from a sound perspective. The main objective of this study is to understand what underlying aspects decide the experience and perception of heavy vehicle–related sounds in the context of electrified propulsion. Using a thematic analysis of data collected at a listening experiment conducted in 2020, factors affecting the perception of novel sounds generated by a first-generation electric truck are investigated. A hypothesis is that the experience of driving or being a passenger in electric trucks will affect the rating and response differently compared to listeners not yet experienced with this sound. The results show that the combination of individual preference and experience, hearing function
To improve the accuracy and reliability of short-term prediction of highway visibility level in key scenarios such as short duration and fast changing speed, this paper proposes a short-term prediction method for highway visibility level based on attention mechanism LSTM. Firstly, XGBoost and SHAP methods are used to analyze the factors affecting highway visibility, determine the importance ranking of different influencing factors, and select the factors that have a greater impact on visibility as inputs for the visibility level prediction model. Secondly, based on LSTM as the model foundation network and innovative coupling attention mechanism, a visibility level prediction model based on attention mechanism LSTM is constructed, which can dynamically update the correlation between meteorological feature information at each historical time point and the visibility level at the current prediction time, thereby dividing the importance of information and flexibly capturing important
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
Monitoring the safety and structural condition of tunnels is crucial for maintaining critical infrastructure. Traditional inspection methods are inefficient, labor-intensive, and pose safety risks. With its non-contact, high-precision, and high-efficiency features, mobile laser scanning technology has emerged as a vital tool for tunnel monitoring. This paper presents a mobile laser scanning system for tunnel measurement and examines techniques for calculating geometric parameters and processing high-resolution imaging data. Empirical evidence demonstrates that mobile laser scanning offers a reliable solution for evaluating and maintaining tunnel safety.
Temperature segregation significantly affects the compaction of asphalt mixtures and the durability of the asphalt pavement layer. Uneven cooling of the mixture during transportation is a key factor contributing to temperature segregation. This study uses finite element simulations to analyze the temporal and spatial temperature evolution during the transportation of asphalt mixtures. A temperature segregation evaluation index (TSIv) is proposed to assess the significance of various factors affecting segregation. Support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGBoost) models are employed to predict temperature changes during transportation and optimize the predictive models. The results indicate that the proportion of areas with a temperature difference of less than 10°C is consistently the highest, followed by areas with a temperature difference greater than 25°C, and then those with temperature differences in the ranges of 10-16°C and
This paper presents advanced intelligent monitoring methods aimed at enhancing the quality and durability of asphalt pavement construction. The study focuses on two critical tasks: foreign object detection and the uniform application of tack coat oil. For object recognition, the YOLOv5 algorithm is employed, which provides real-time detection capabilities essential for construction environments where timely decisions are crucial. A meticulously annotated dataset comprising 4,108 images, created with the LabelImg tool, ensures the accurate detection of foreign objects such as leaves and cigarette butts. By utilizing pre-trained weights during model training, the research achieved significant improvements in key performance metrics, including precision and recall rates. In addition to object detection, the study explores color space analysis through the HSV (Hue, Saturation, Value) model to effectively differentiate between coated and uncoated pavement areas following the application of
Since the rapid development of the shipping and port industries in the second half of the twentieth century, the introduction of container technology has transformed cargo management systems, while simultaneously increasing the vulnerability of global shipping networks to natural disasters and international conflicts. To address this challenge, the study leverages AIS data sourced from the Vessel Traffic Data website to extract ship stop trajectories and construct a shipping network. The constructed network exhibits small-world characteristics, with most port nodes having low degree values, while a few ports possess extremely high degree values. Furthermore, the study improved the PageRank algorithm to assess the importance of port nodes and introduced reliability theory and risk assessment theory to analyze the failure risks of port nodes, providing new methods and perspectives for analyzing the reliability of the shipping network.
Electrochemical machining (ECM) is a highly efficient method for creating intricate structures in materials that conduct electricity, irrespective of their level of hardness. With the rising demand for superior products and the necessity for quick design modifications, decision-making in the industrial sector becomes increasingly complex. This study specifically examines Titanium Grade 7 and suggests creating prediction models through regression analysis to estimate performance measurements in ECM. The experiments are formulated based on Taguchi's ideas, utilizing a multiple regression approach to deduce mathematical equations. The Taguchi method is utilized for single-objective optimization in order to determine the ideal combination of process parameters that will maximize the material removal rate. ANOVA is a statistical method used to determine the relevance of process factors that affect performance measures. The suggested prediction technique for Titanium Grade 7 exhibits
The objective of this research is to develop an optimization strategy for the Electrochemical Drilling process on Nimonic alloy material, taking into account various performance factors. The optimization strategy relies on the integration of the Taguchi method with Grey Relational Analysis (GRA). Nimonic is extensively utilized in aerospace, nuclear, and marine industries, specifically in situations that are prone to corrosion. The experimental trials are structured based on Taguchi's principle and encompass three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This inquiry examines performance indicators like the rate of material removal, surface roughness, as well as geometric parameters such as overcut, shape, and orientation tolerance. Based on the investigation, it is determined that the feed rate is the primary factor that directly affects the intended performance criteria. In order to enhance the accuracy of predictions, multiple regression
India has seen a significant boost in automotive research and development, specific to Vehicle Dynamics active safety systems and ADAS. To develop these systems, without excessive reliance on full working prototypes, vehicle manufacturers are relying on virtual models to better fine tune the design parameters. For this, there is a real requirement of digital twins of the proving grounds. This virtual testing surfaces will help in reducing test costs, test times and increase iteration counts, leading to fine-tuned prototype vehicle and finally a market leading product. National Automotive Test Tracks (NATRAX) is already playing a crucial role in the testing and development of these technologies, on its test tracks. Recognizing the need to assist in virtual testing for Indian automotive manufacturers, NATRAX is taking steps to develop virtual proving grounds to complement physical testing and reduce the development time. This paper targets a comparative analysis of dynamic parameters
Natural fiber composites (NFC’s) have considerable promise for a wide range of technological applications due to their exceptional features, which include notable weight reduction, high strength, and affordability. The aforementioned materials are also biodegradable and sustainable, which makes them appealing for use in sustainable engineering methods. This research focuses on evaluating the mechanical features of jute fiber and Al₂O₃ particle fortified polymer composites, exploring their potential for advanced engineering uses. The Taguchi technique is used with a L9 orthogonal array, integrating three-level, three-parameter approach, to systematically examine potential combinations of process variables in the manufacturing of these polymer composites. The primary goal is to optimize the mechanical attributes of the composites, which include tensile modulus, tensile stress, and weight percentage increase. Detailed investigations are conducted to interpret the effects of these process
This study investigates the thermal buckling behavior of axially layered functionally graded material (FGM) thin beams with potential applications in automotive structures. The FGM beam is constructed from four axially stratified sections, with the proportional amount of metal and ceramic fluctuating through the thickness. The buckling analysis is carried out for three different support configurations: clamped-clamped, simply supported-simply supported, and clamped-simply supported. The primary objective is to identify the optimal thermal buckling temperature of the FGM thin beam using the Taguchi optimization method. Beam arrangements are established using a Taguchi L9 orthogonal array and analyzed using finite element software (ANSYS). Layers 1-4 of the axially layered beam are considered process parameters, while the thermal buckling temperature is the response parameter. Minitab software performs an Analysis of Variance (ANOVA) with a 95% confidence level to identify the most
The intention of this exploration is to evolve an optimization method for the Electrochemical Machining (ECM) process on Haste alloy material, taking into account various performance characteristics. The optimization relies on the amalgamation of the Taguchi method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Haste alloy is extensively utilized in the aerospace, nuclear, marine, and car sectors, specifically in situations that are prone to corrosion. The experimental trials are organized based on Taguchi's principles and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This examination examines performance indicators, including the pace at which material is removed and the roughness of the surface. It also includes geometric factors such as overcut, shape, and tolerance for orientation. The results suggest that the rate at which the feed is supplied is the most influential element affecting the necessary performance standards
The integration of carbon nanotubes (CNT) into composite materials has revolutionized various high-performance industries, including aerospace, marine, and defense, for their exceptional thermal, mechanical, and electrical properties. The critical nature of these applications demands precise control over the manufacturing process to ensure the optimal performance of the CNT-reinforced composites. This study employs the Taguchi approach to systematically investigate and determine the optimal proportion of CNT volume fraction, fiber volume fraction, and stacking sequence in composite materials to achieve the optimal fundamental frequency. The Taguchi method, known for its efficiency in optimizing design parameters with a minimal number of experiments, enables the identification of the most influential factors and their optimal levels for enhancing material properties. Our findings demonstrate that the proper arrangement and proportioning of these components significantly improve the
Fused Deposition Modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition Modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational Analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses
This study investigates the frequency response characteristics of laminated composite rectangular plates, focusing on the influence of fiber orientation. The composite plates, composed of 12 layers of glass fiber reinforced polymer composites (GFRP), were chosen for their superior mechanical properties and broad applicability in engineering fields, including the automotive sector. In automotive engineering, these composites are valued for their lightweight properties and high strength, contributing to enhanced performance and fuel efficiency. The analysis employed a combination of finite element methods and Taguchi experimental design techniques to understand how fiber orientation affects the dynamic behavior of these plates. To systematically explore the impact of fiber orientation on the frequency response, the study utilized Taguchi's orthogonal array design. Specifically, the L9 (3^3) and L16 (4^4) orthogonal arrays were employed to structure the experimental runs effectively
This study presents a comprehensive structural analysis of a two-wheeler handlebar subjected to various loading conditions, aiming to evaluate its strength, durability, and safety. During operation, two-wheelers encounter multiple forces, making the handlebar a critical component for rider control and safety. The analysis begins by investigating the different types of loads experienced during typical riding scenarios, including static loads when the bike is stationary, and dynamic loads arising from rider movements, braking, and handling. The primary objective is to understand how these loads impact the handlebar's structural integrity. To achieve this, critical load cases are identified and categorized. Braking loads, which apply force primarily in the forward direction due to deceleration, are examined. Manhandling loads are analyzed to understand the multidirectional forces acting on the handlebar during transportation and parking. Additionally, vertical loads are assessed
The aim of this study is to create an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for the Electrochemical Machining (ECM) process using Nimonic Alloy material, with a specific focus on several performance aspects. The optimization strategy utilizes the combination of the Taguchi method and ANFIS integration. Nimonic Alloy is widely employed in the aerospace, nuclear, marine, and car sectors, especially in situations that are susceptible to corrosion. The experimental trials are designed according to Taguchi's method and involve three machining variables: feed rate, electrolyte flow rate, and electrolyte concentration. This study investigates performance indicators, such as the rate at which material is removed, the roughness of the surface, and geometric characteristics, including overcut, shape, and tolerance for orientation. Based on the analysis, it has been determined that the feed rate is the main component that influences the intended performance criteria. In order to
This research examines the thermal instability of slender beams composed of functionally graded materials (FGMs), with a specific focus on their suitability for engine hood components. The FGM combines the durability of aluminum with the heat tolerance of silicon nitride. The study aims to determine the maximum temperature the beam can withstand without buckling under various support conditions, simulating the uneven heat distribution experienced by engine hoods in actual use. The FGM structure comprises four longitudinally arranged layers, where the ceramic and metallic components gradually shift across the thickness. Finite element modeling software (ANSYS) is utilized to examine the buckling response under diverse temperature conditions. To enhance the thermal performance of the engine hood panel, the Taguchi L9 orthogonal array methodology is employed utilizing Minitab 19 software. The first four layers of the FGM beam are defined as process variables, while the critical buckling
This paper presents a fault diagnosis strategy that integrates model-based and data-driven approaches for a 115 kW proton exchange membrane fuel cell used in vehicles. First, a stack subsystem model was developed in the MATLAB/Simulink platform based on the working principles and structure of PEMFC, and validated with experimental data. Subsequently, faults in the air and hydrogen inlet pipelines were simulated, and the resulting fault data were subjected to preprocessing steps, including cleaning, normalization, and feature extraction, to enhance the efficiency of subsequent data processing. Finally, a BP neural network optimized by particle swarm optimization was employed to achieve fault tree-based classification diagnosis. Experimental results indicate that the diagnosis accuracy of the BP neural network reached 96.04%, with an additional accuracy improvement of approximately 2.4% after PSO optimization.
The durability of fuel cell vehicle (FCV) has always been one of the key factors affecting its large-scale application. However, the durability test methods of FCV and its key components, fuel cell stack (FCS), are incomplete all over the world, especially the lack of vibration test method on FCV. Focused on the FCS, this paper collects the road load spectrum of different vehicle models in their typical working conditions, so as to obtain the power spectral density of FCS of different vehicle models, which is used as the input signal of durability test. Through the FCS testing and analysis of fuel cell passenger car, bus, tractor and cargo van, the results show that the vibration intensity in three directions of FCS of different models is basically less than that of power battery, and only the FCS of fuel cell bus is greater than that of power battery in the direction of vehicle travel.
Currently, the application scope of fuel cell vehicles is gradually expanding. There is currently no durability testing method for the entire vehicle level in its research and development design process. In this article, a certain fuel cell passenger car is taken as the research object. The load spectrum data of its key components is collected. A ‘user goal test field’ multi-channel multi-dimensional load correlation optimization model is established. The goal is to minimize the difference in pseudo damage of special components such as the fuel cell vehicle stack structure under the user’s full life cycle target load and the test field test load. The characteristics of the multi-dimensional load of the fuel cell components corresponding to the optimized solution in the rainflow distribution and frequency domain distribution are calculated. And a durability reliability acceleration testing specification for fuel cell vehicle test fields for special components such as the stack structure
A 20-cell self-humidifying fuel cell stack containing two types of MEAs was assembled and aged by a 1000-hour durability test. To rapidly and effectively analyze the primary degradation, the polarization change curve is introduced. As the different failure modes have a unique spectrum in the polarization change curve, it can be regarded as the fingerprint of a special degradation mode for repaid analysis. By means of this method, the main failure mode of two-type MEAs was clearly distinguished: one was attributed to the pinhole formation at the hydrogen outlet, and another was caused by catalyst degradation only, as verified by infrared imaging. The two distinct degradation phases were also classified: (i)conditioning phase, featuring with high decay rate, caused by repaid ECSA change from particle size growth of catalyst. (ii) performance phase with minor voltage loss at long test duration, but with RH cycling behind, as in MEA1. Then, an effective H2-pumping recovery is conducted
The present research explores the potential of high-performance thermoplastics, Polymethyl Methacrylate and Polyurethane, to enhance the passive safety of automotive instrument panels. The purpose is to evaluate and compare the passive safety of these two materials through the conduct of the Charpy Impact Test, Tensile Strength Test, and Crush Test —. For this, five samples were prepared in the case of each material via injection moulding, which enabled reliability, and consistency of the findings. As a result, it was found that in the case of the Charpy Impact Test, the average impact resistance varies with PMMA exhibiting a level of 15.08 kJ/m2 as opposed to the value of 12.16 kJ/m2 for PU. The Tensile Strength Test produced the average tensile strength of 50.16 for PMMA and 48.2 for PU, which implied superior structural integrity under tension for the first type of thermoplastic. Finally, the Crush Test showed that PMMA is more resistant to crushes on average than PU with the
During the development phase of any product, it is crucial to ensure functionality and durability throughout their whole lifecycle. Physical tests have been traditionally used as the main tool to evaluate the durability of a product, especially in the automotive industry. And the evolution of computational methods combined with the Engineering Fundamentals allowed Computer Aided Engineering (CAE) simulations to predict failures in considering different conditions without building a prototype to perform a test. The use of virtual product validation using CAE simulations leads to product design flexibility on early development phase and both development costs and time reduction. This paper presents a methodology for computing the operation reaction loads in an automotive fuel filler door, which is an input needed to virtually validate the subsystem in terms of durability. The methodology is based on rigid body motion assumptions and the result shows good accuracy when comparing the
Mechanical component failure often heralds superficial damage indicators such as color alteration due to overheating, texture degradation like rusting or false brinelling, spalling, and crack propagation. Conventional damage assessment relies heavily on visual inspections performed by technicians, a practice bogged down by time constraints and the subjective nature of human error. This research paper delves into the integration of deep learning methodologies to revolutionize surface damage evaluation, addressing significant bottlenecks in diagnostic precision and processing efficiency. We detail the end-to-end process of developing an intelligent inspection system: selecting appropriate deep learning architectures, annotating datasets, implementing data augmentation, optimizing hyperparameters, and deploying the model for widespread user accessibility. Specifically, the paper highlights the customization and assessment of state-of-the-art models, including EfficientNet B7 for
The path towards clean mobility points in the direction of battery electric vehicles (BEVs) as a possible transportation solution. Despite a growing market penetration worldwide, emerging countries are struggling to successfully adopt BEV with current vehicle models. The literature presents an embracing discussion about BEV barriers but lacks into suggesting practical actions into BEV design. Based on a product development methodology and value analysis, this research aims to review factors holding back the BEV adoption in developing countries and to apply these factors into BEV features and design specifications. The literature was systematically reviewed based on the Brazilian case scenario to cast customer requirements for numerical evaluation through the Mudge Method. These were later translated into design requirements and ranked according to their relative importance with the quality function deployment (QFD). The results show that vehicle safety, pricing, and range anxiety are
LM (Lean manufacturing) is the manufacturing strategy focused on continuous improvement of manufacturing operations. This study has been carried out in manufacturing industry of northern India to assess important success factors, LM strategies applied, and important benefits of both LM strategies and approach. Questionnaire survey has been performed to achieve the desired objectives. Results indicated that manufacturing organizations have great affinity for LM strategies viz. small incremental improvements (kaizen) for strategic success. Production rates are highly improved after implementing LM approach. Mediating role of every success factor have been measured using regression analysis and structural equation modeling. Moreover, correlation shows the highly significant relations between LM strategies and benefits of the LM approach.
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