Browse Topic: Fatigue
The rear swing arm, a crucial motorcycle component, connects the frame and wheel, absorbing the vehicle’s load and various road impacts. Over time, these forces can damage the swing arm, highlighting the need for robust design to ensure safety. Identifying potential vulnerabilities through simulation reduces the risk of failure during the design phase. This study performs a detailed fatigue analysis of the swing arm across different road conditions. Data for this research were collected from real-vehicle experiments and simulation analyses, ensuring accuracy by comparing against actual performance. Following CNS 15819-5 standards, road surfaces such as poorly maintained, bumpy, and uneven roads were tested. Using Motion View, a comprehensive multi-body dynamic model was created for thorough fatigue analysis. The results identified the most stress-prone areas on the swing arm, with maximum stress recorded at 109.6N on poorly maintained roads, 218.3N on bumpy surfaces, and 104.8N on
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
Continuing prior work, which established a simulation workflow for fatigue performance of elastomeric suspension bushings operating under a schedule of 6-channel (3 forces + 3 moments) road load histories, the present work validates Endurica-predicted fatigue performance against test bench results for a set of multi-channel, time-domain loading histories. The experimental fatigue testing program was conducted on a servo-hydraulic 3 axis test rig. The rig provided radial (cross-car), axial (for-aft), and torsional load inputs controlled via remote parameter control (rpc) playback of road load data acquisition signals from 11 different test track events. Bushings were tested and removed for inspection at intervals ranging from 1x to 5x of the test-equivalent vehicle life. Parts were sectioned and checked for cracks, for point of initiation and for crack length. No failure was observed for bushings operated to 1 nominal bushing lifetime. After 3 nominal bushing lifetimes, cracks were
Fatigue design is invariably of prior concern for the automotive industry, no matter of the evolution of the mobility market: at first because carmakers must stay compliant with general structural integrity requirements for reliability, notably applicable to the chassis system, then due to the endless competition for lightweighting in order to mitigate product costs and/or enhance vehicle efficiency. In the past, this key performance was often tackled by basic reference load cases, making use of the simplest signal content, e.g. sinus functions, to practice constant amplitude loads on test rigs and for computations, respectively. Nowadays, full time series coming from proving ground measurements, or any corresponding virtual road load data computations, may be applied to feed complex vehicle computations for virtual assessment and complex test facilities for final approval, under variable amplitude loads. In between, the concept of load spectra (i.e. distribution of amplitudes with
Gray cast iron is a cost-effective engineering material widely used for heavy duty engine blocks and brake rotor discs in vehicles. Thermomechanical fatigue (TMF) frequently occurs during vehicle operation due to temperature fluctuations in brake rotors. To speed up the design of the component, design structurally sounding brake rotors, and prevent premature thermally induced cracking, it is critical to investigate TMF behavior of the gray cast iron. This study presents a series of fatigue tests, including isothermal low cycle fatigue (LCF) tests at temperatures up to 700°C, as well as in-phase (IP) and out-of-phase (OP) TMF tests across various temperature ranges. Because of the asymmetric behavior in tension and compression, creep behaviors in both tension and compression and oxidation are also studied. These behaviors are the key to enable simulation of thermally induced cracks in rotors.
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
Any vehicle traveling on roads interacts with various profiles of surface roughness, which can be best characterized by randomness. The resulting random vibrations not only expose passengers to unpleasant physical shakes and noises, but also impart fatigue damage to nearly everything installed on the vehicle. In today’s robust design process, it is highly desirable to predict fatigue damage in the early design phase, in order to prevent any durability problems in the future, especially for electric vehicles. Historically, the conventional approach to tackling the problem of fatigue damage has involved cycle-counting stress or strain responses, obtained through step-by-step numeric solutions in the time-domain. However, the most effective method of predicting fatigue in random vibration lies in the frequency domain. Such a spectrum-based approach is greatly advantageous because it does not have to deal with expensive and tedious simulations involving millions of time instants of
Given the strategic importance of aluminum cast materials in producing lightweight, high-performance products across industries, it is fundamental to assess their mechanical and cyclic fatigue properties thoroughly. This investigation is primarily for optimizing material utilization and enhancing the efficiency and reliability of aluminum cast components, contributing to significant conservation of raw materials and energy throughout both the manufacturing process and the product's lifecycle. In this study, a systematic material investigation was conducted to establish a reliable estimation of the fatigue behavior of different aluminum cast materials under different loading ratios and elevated temperatures. This paper presents an analysis of the statistical and geometrical influences on various aluminum alloys, including AlSi10MnMg, AlSi7Mg0.3, and AlSi8Cu3Fe, produced via pressure die casting and gravity die casting (permanent mold casting), and subjected to different heat treatment
Engineers have been dealing with either random excitation or swept sinusoidal excitation quite often in the past, in order to estimate the fatigue damage in an automotive system. Efficient numerical methods in frequency domain for the fatigue due to either form of excitation (not both) have become more mature in the past decades. However, a greater demand for a fatigue estimation under a more complicated form of excitation has risen as electric vehicles are being developed in the automotive industry. In particular, delicate rotating components such as electric motors and gearboxes can be simultaneously subject to a random excitation due to any possible rough surfaces on the road as well as sinusoidal excitation due to its own rotation. Hence, this combined or mixed excitation, also known as swept sine-on-random (SSOR), has posed a challenge to the fatigue simulation community when using a frequency-domain method is desired.. The very challenge is due to the fact that when either random
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
The tensile and low-cycle fatigue (LCF) properties of Ti6Al4V specimens, manufactured using the selective laser melting (SLM) additive manufacturing (AM) process and subsequently heat-treated in argon, were investigated at elevated temperatures. Specifically, fully reversed strain-controlled tests were performed at 400°C to determine the strain-life response of the material over a range of strain amplitudes of industrial interest. Fatigue test results from this work are compared to those found in the literature for both AM and wrought Ti6Al4V. The LCF response of the material tested here is in-family with the AM data found in the literature. Scanning electron microscopy performed on the fracture surfaces indicate a marked increase in secondary cracking (crack branching) as a function of increased plastic deformation and demonstrating equivalent performance when compared to the wrought Ti6AL4V at RT (room temperature) at 1.4% strain amplitude and better performance when compared to the
This SAE Aerospace Standard (AS) establishes vibration and transmissibility test procedures which compare the relative strengths of various loop and saddle type support clamps. This procedure is intended for conducting fatigue testing which is standard throughout the aerospace industry thereby establishing a clamp strength comparison that can be used in an evaluation process. The testing required by this document ensures that clamps will meet adequate fatigue requirements only. It does not infer qualification of the clamp installation techniques or its ability to meet in-service environments or operating conditions. Separate qualification testing should be performed to ensure satisfactory service of the installed clamp.
Intelligent Structural Health Monitoring (SHM) of bridge is a technology that utilizes advanced sensor technology along with professional bridge engineering knowledge, coupled with machine vision and other intelligent methods for continuously monitoring and evaluating the status of bridge structures. One application of SHM technology for bridges by way of machine learning is in the use of damage detection and quantification. In this way, changes in bridge conditions can be analyzed efficiently and accurately, ensuring stable operational performance throughout the lifecycle of the bridge. However, in the field of damage detection, although machine vision can effectively identify and quantify existing damages, it still lacks accuracy for predicting future damage trends based on real-time data. Such shortfall l may lead to late addressing of potential safety hazards, causing accelerated damage development and threatening structural safety. To tackle this problem, this study designs a deep
In some IC engines, fuel injection pump is driven by camshaft; thus, these camshafts are designed for bending and torsional loads. Conventionally, camshafts are built-to-specification. Typically, durability assessment of camshaft happens at engine level, this calls for proto or calibration engine to be made and available for testing. As there are limited number of engine level proto testing, the overall scatter in camshafts due to manufacturing/process variations is not possible to be covered. This poses a risk of camshaft failures in the final stages of product development. To mitigate this risk, a component level standard test method is needed for quickly validating design and manufacturing process of camshafts for second source suppliers. The current paper discusses the process followed for arriving at a standard test setup and overcoming the challenges in terms of capturing the appropriate physics for camshaft failure during the engine level testing. Camshaft rear end experiences
Tractors, as agricultural machines consisting of various interconnected assemblies, work in unison to perform specific functions and achieve desired outputs. Among these assemblies, the Hood Assembly, Firewall Assembly, Scuttle Assembly, Fuel Tank Assembly, Fender Assembly, Floor Panel Assembly, and Footstep Assembly are all produced through sheet metal fabrication. The components of these assembly are made from sheet metal and are joined together using various techniques, such as bolts, welds, and others. The inherent characteristics of welding processes generally results in welded joints having lower fatigue strength compared to the individual parts being joined. Moreover, welds are commonly applied at geometric features or areas where the section changes within a structure. As a result, even in a structurally sound design, welded joints are often more vulnerable to fatigue failure. Hence, a comprehensive assessment of the durability of a welded structure requires placing
Surface roughness is a key factor in different machining processes and plays an important role in ergonomics, assembly process, wear and fatigue life of components. Other factors like functionality, performance and durability of parts are also affected by surface roughness. Although maintaining an optimum surface roughness is a major challenge in many manufacturing industries. Surface roughness during machining depends upon machining parameters such as tool geometry, feed rate, depth of cut, rotational speed, lubrication, tool wear, etc. Tool vibrations during machining also have significant influence in surface roughness. In this work an attempt is made to predict the surface roughness of machined components made by the turning process by using machine learning of tool vibration signals. By varying different machining parameters and keeping other tooling and material properties same, a range of surface roughness values can be obtained. For each condition, corresponding tool vibration
As a part of an automobile suspension structure, fatigue durability performance of the automotive stabilizer bar linkage is crucial to the safety and reliability of the suspension system. In this study, the modeling and simulation analysis methods of the stabilizer bar linkage were described in detail, especially for the welded positions between the connecting rod and the spherical shells (or sleeves). Based on the equivalent structural stress method and the theory of critical distances, damage values of welded positions in the stabilizer bar linkage were solved. For the spherical shell end, the simulation reproduced the bench test; and for the sleeve end, the analysis approach was determined by comparing in several different modeling ways. Mooney-Rivlin model was adopted to fit the constitutive relationship of rubber material in the bushing. The above methods were applied to predict the fatigue durability performance of the stabilizer bar linkage product, and the effectiveness was
A temperature dependent cohesive zone model considering the thermo-mechanical fatigue loadings are used to simulate and predict the failure process of solder joint interface in power electronics modules. Cohesive Zone Models (CZMs) are gaining popularity for modeling the fracture and fatigue behavior in various class of materials such as metals, polymers, ceramics, and their composite materials. Unlike the traditional fracture mechanics which considers concept of infinitesimal crack, CZMs assume a fracture process zone in which external energy is distributed in vicinity to propagating crack. In order to predict the fatigue-fracture process under thermo-mechanical cyclic loading, a damage accumulation variable is utilized. The calculation of damage is performed using a progressive mechanism, and the cohesive zone model is updated to reflect the present level of damage. The existing cohesive forces are influenced by both the current damage status and the extent of separation
This paper presents additive Weibull reliability model using customer complaints data and finite element fatigue (FEA) analysis data. Warranty data provides insight into the underlying customer issues. Reliability engineers prepare a prediction model based on this data to forecast the failure rate of components. However, warranty data has certain limitations with respect to prediction modeling. The warranty period covers only the infant mortality and useful life zone of a bathtub curve. Thus, predicting with solely warranty data generally cannot provide results with desired accuracy. The failure rate of wear-out components is driven by random issues initially and wear-out or usage-related issues at the end of the lifetime. For accurate prediction of failure rate, data need to be explored at wear-out zone of a bathtub curve. Higher cost always limits the testing of components until failure, but FEA fatigue analysis can provide the failure rate behavior of a part much beyond the warranty
Most of the heavy commercial vehicles are installed with Pneumatic brake system where the medium is a pressurized pneumatic air generated with the reciprocating air compressor. Heating is an undesirable effect of the compression process during loading cycles as reciprocating air compressors are concerned. Therefore it is necessary to reduce the delivery air temperature of compressor for safer operation of downstream products. The present investigation deals with the measurement of the delivery air temperature of a typical 318 cc water cooled compressor. A through steady state conjugate heat transfer analysis is conducted for the given speed and with the specification cooling water flow rate to predict the delivery air temperature. Pressure drop across the cooling water flow path has been measured and optimum flow rate is arrived to meet the design requirement. The results of characteristic analysis and comparative research show that the cooling system can obviously reduce the cylinder
Arrays of radial cracks often appear at the bore of pressurized cylinders, posing potential safety risks and leading to possible structural failures. This article presents an analytical approach to evaluate the stress field arising from single or multiple uniform radial cracks in thick-walled pressurized cylinders within the context of linear elastic fracture mechanics (LEFM) under mode-I loading. This formulation is based on the fundamental equations of elasticity and approximations of stress intensity factors (SIF) reported in the literature. Hence, the SIF were revisited and their range of validity was highlighted. The study considers two types of internal pressure loading: one applied only to the cylinder’s inner surface with no pressure on the crack faces and another applied to both the inner surface and the crack faces. The influence of the number and length of cracks relative to cylinder thickness on the stress field is analyzed. A finite element model of the pressurized vessel
Reducing vehicle weight is a key task for automotive engineers to meet future emission, fuel consumption, and performance requirements. Weight reduction of cylinder head and crankcase can make a decisive contribution to achieving these objectives, as they are among the heaviest components of a passenger car powertrain. Modern passenger car cylinder heads and crankcases have greatly been optimized in terms of cost and weight in all-aluminum design using the latest conventional production techniques. However, it is becoming apparent that further significant weight reduction cannot be expected, as processes such as casting have reached their limits for further lightweighting due to manufacturing restrictions. Here, recent developments in the additive manufacturing (AM) of metallic structures is offering a new degree of freedom. As part of the government-funded research project LeiMot [Lightweight Engine (Eng.)] borderline lightweight design potential of a passenger car cylinder head with
Rolling bearings play a critical role in rotating machinery, with their fatigue life directly impacting equipment’s operational reliability. This underscores the significant engineering application value of “fault diagnosis” (FD) technology for rolling bearings in mechanical, automation, and aerospace domains. Literature reviews highlight that a substantial portion of failures in machinery such as jet turbine engines, wind turbines, gear reducers, and induction machines are attributable to bearing issues. Early fault detection and preventive maintenance are therefore imperative for ensuring the smooth operation of rotating machinery. This paper focuses on rolling bearings, delving deep into FD technology using machine learning principles. It analyses the structure and common failure modes of rolling bearings, discussing an FD method based on machine learning. Specifically, the SE-DRN (“squeeze-exclusion deep residual network”) approach is employed, leveraging “variational modal
This study emphasizes the importance of computer-aided engineering (CAE) approach in optimizing exhaust gas recirculation (EGR) tube under thermal load. With exhaust gases generating high temperatures, the EGR tube experiences increased stress and strain, posing challenges to its structural integrity. Moreover, the cyclic heating and cooling cycles of the engine imposes thermal fatigue, further compromising the tube’s performance over time. To address these concerns, the paper introduces a comprehensive CAE methodology for conducting factor of safety analysis. The nonlinear thermal analysis is performed on the assembly as due to high temperatures the stresses cross the yield limit. The strain-based approach is used to calculate the factor of safety. Moreover, a comprehensive case study is presented, illustrating how design modifications can enhance the thermal fatigue factor of safety. By adjusting parameters such as thickness and routing, engineers can mitigate thermal stresses and
This study aims to present a virtual numerical validation procedure for durability in brake system components, using artificial neural networks and based on experimental bench tests. The study focus was concentrated on the drum brake spider component, responsible for mechanically connecting the brake system subassemblies. To develop the validation procedure, engineering software such as ABAQUS, Fe-Safe, Minitab, and MATLAB was used. These were crucial for carrying out stress analyses, statistical data validation, and construction of an Artificial Neural Network (ANN) capable of predicting finite element responses, fatigue life, and supporting real-time decision-making for structural validation of mechanical components. The results obtained from these tools allowed the calibration of a numerical virtual model using the Finite Element Method (FEM) based on mechanical theories and results obtained in bench tests with the brake system, thus, a finite element database was generated for the
The demand for enhanced safety and extended lifespan of brake systems prompts the investigation to increase the static mechanical properties and fatigue resistance of commercial vehicle brake spiders through the incorporation of niobium nanoparticles into a cast iron alloy. This study aims to improve the material structure as well as the static and dynamic mechanical properties of the component. Chemical, microscopic, and mechanical analyses were conducted in samples of the nanostructured alloy and in the spider. A durability test was performed using a structural bench called “Chuker” to assess the potential increase in fatigue life. The Chuker is capable of simulating a real-world brake system condition, including torque magnitudes up to 17.5 kNm, which are the highest to be withstand by the designed brake power. This torque replicates the brake system activation during a vehicle emergency braking. The spiders manufactured with the nanostructured alloy exhibited most uniform
For the vibration durability bench test of commercial vehicle batteries, it is essential to have accurate test specifications that exhibit high robustness and reasonable acceleration characteristics. This study evaluates the impact of different battery frame systems on the vibration response of the battery body, as determined by road load spectrum test results of a commercial vehicle battery system. It also confirms the variations in the external environmental load. Utilizing the response spectrum theory, a comprehensive calculation method for the fatigue damage spectrum (FDS) of batteries is developed. The time domain direct accumulation method, frequency domain direct accumulation method, and frequency domain envelope accumulation method are all compared. Analysis of kurtosis and skewness reveals that when the load follows the super-Gaussian distribution characteristics, the time domain direct accumulation method should be used to calculate the fatigue damage spectrum to minimize
Automotive closure slam is the most crucial attribute affecting the closure structure and its mountings on BIW due to its high occurrence in real-world usage. Thus, virtual simulation of closure slam becomes necessary and is generally carried out using explicit codes with associated technical hitches like all-requisite inputs availability, FE modeling and analysis techniques, substantial human effort, high solution time, human and computational resource competence, or even access to suitable expensive explicit FE solver. Hence it becomes challenging to virtually analyze the design at every design phase of product development cycle under strict timelines leading to possibilities of both over- and under-designed parts, sometimes resulting in physical testing or even field failures. So, the need for an alternative simplified representation of closure slam, addressing the typical issues faced during explicit dynamic simulation and producing acceptable analysis outputs, gains significance
This article investigates the deformation mechanics of cast iron and its implications for notch analysis, particularly in the automotive industry. Cast iron’s extensive use stems from its cost-effectiveness, durability, and adaptability to various mechanical demands. Gray, nodular, and compacted graphite cast irons are the primary types, each offering unique advantages in different applications. The presence of graphite, microcracks, and internal porosity significantly influences cast iron’s stress–strain behavior. Gray and compacted cast iron display an asymmetrical curve, emphasizing low tensile strength and superior compression performance due to graphite flakes and crack closures. Nodular cast iron exhibits a symmetrical curve, indicating balanced mechanical properties under tension and compression. The proposed simplified macrostructural approach, based on monotonic stress–strain, aims to efficiently capture graphite and crack closure effects, enhancing compressive strength and
The aerospace industry heavily relies on NASGRO as a standard method for crack propagation analysis, despite encountering challenges due to variations in stress gradients across flight missions. In response to this issue, this paper introduces a pioneering methodology that integrates stress gradients at each time point throughout a mission, computed cycle by cycle using NASGRO. The study meticulously evaluates the feasibility and efficacy of this approach against established industry-standard procedures, focusing on the critical topic of low cycle fatigue (LCF) and underscoring the significance of damage-tolerant design principles. The methodology encompasses the design of an H-sector in Ansys Workbench, the execution of stress analysis for a typical flight mission profile, and the systematic extraction of stress gradients for each cycle at the pivotal crack nucleation point. Subsequently, NASGRO is employed to estimate life cycles using both industry-standard baseline methodologies
This specification covers a carbon steel in the form of wire supplied as coils, spools, or cut lengths (see 8.2).
Hydraulic systems in aircraft largely comprise of metallic components with high strength to weight ratios. Some examples of such material include Aluminum and Titanium alloys which are typically chosen for low and high-pressure applications respectively. For aircraft fluid conveyance products, hydraulic conduits are fabricated by axisymmetric turning to support flow conditions. The hydraulic conduits can have grooved interfaced design within for placement of elastomeric sealing components. This article presents a systematic study carried out on common loads experienced by fluid carrying conduits and the failure modes induced. Firstly, a static structural analysis was carried out on each of the geometries of the test articles to identify the locations having areas of high stress concentration. Test articles of various wall thicknesses and internal diameters were pressure impulse tested at different conditions of side loads to identify cycle numbers till failure and failure locations. On
High-cycle fatigue damage causing micro-crack initiation is a critical concern in aerospace structural components and alloys due to intense thermo-mechanical stress and vibration. Vibration or overload/impact can initiate small cracks near the stress concentration zones. These cracks may expand erratically before being detectable in subsequent inspections, emphasizing the need to predict the effects of usage and aging on components. This predictive ability would significantly aid material refinement, design enhancements, and inspection planning. Prediction of fatigue damage leading to the formation of cracks is a great challenge for many reasons, including microstructure anisotropy and uncertainties in complex stress states compared to design stress used in testing and qualifying a component. These uncertainties undermine inspection reliability and effectiveness. The elastic moduli of the material are considered isotropic and homogeneous at the macroscopic level of continuum plasticity
Thermo-mechanical fatigue and natural aging due to environmental conditions are challenging to simulate in an actual test with advanced fiber-reinforced composites, where their fatigue and aging behavior are little understood. Predictive modeling of these processes is challenging. Thermal cyclic tests take a prohibitively long time, although the strain rate effect can be scaled well for accelerating the mechanical stress cycles. Glass fabric composites have important applications in pipes, aircraft, and spacecraft structures, including microwave transparent structures, impact-resistant parts of the wing, fuselage deck and many other load-bearing structures. Often additional additively manufactured features and coatings on glass fabric composites are employed for thermal and anti-corrosion insulations. In this paper, we employ a thermo-mechanical fatigue model based on an accelerated fatigue test and life prediction under hot-to-cold cycles. Thermo-mechanical strain-controlled stress
AISI H13 hot work tool steel is commonly used for applications such as hot forging and hot extrusion in mechanical working operations that face thermal and mechanical stress fluctuations, leading to premature failures. Cryogenic treatment was applied for AISI H13 steel to improve the surface hardness and thereby fatigue resistance. This work involves failure analysis of H13 steel specimens subjected to cryogenic treatment and gas nitriding. The specimens were heated to 1020°C, oil quenched followed by double tempering at 550°C for 2 h, and subsequently, deep cryogenically treated at −185°C in the cryochamber. Gas nitriding was carried out for 24 h at 500°C for 200 μm case depth in NH3 surroundings. The specimens were subjected to rotating bending fatigue at constant amplitude loading at room temperature. Measurement of surface roughness, hardness, and microstructural analysis indicated improved fatigue life for cryogenically treated specimens as compared to gas nitride, which could be
The qualification requirements of automakers derive from track testing in which road load and moment inputs to a part in x, y and z directions are recorded over a set of driving conditions selected to represent typical operation. Because recorded histories are lengthy, often comprising many millions of time steps, past industry practice has been to specify simplified block cycle schedules for purposes of durability testing or analysis. Simplification, however, depends on imprecise human judgement, and risks fidelity of the inferred life and failure mode relative to actual. Fortunately, virtual methods for fatigue life prediction are available that are capable of processing full, real-time, multiaxial road load histories. Two examples of filled natural rubber ride bushings are considered here to demonstrate. Each bushing is subject to a schedule of 11 distinct recorded track events. Endurica EIETM map building procedures are first used together with a finite element solution to map the
This paper presents deep learning-based prognostics and health management (PHM) for predicting fractures of an electric propulsion (eP) drivetrain system using real-time CAN signals. The deep learning algorithm, based on autoencoders, resamples time-series signals and converts them into 2D images using recurrence plots (RP). Subsequently, through unsupervised learning of DeepSVDD, it detects anomalies in the converted 2D images and predicts the failure of the system in real-time. Also, reliability analysis based on fracture mechanics was performed using the detected signals and big data. In particular, the severity of the eP drivetrain system is proportional to the maximum shear stress (τmax) in terms of linear elastic fracture mechanics (LEFM) and can be calculated by summarizing the relationship between cracks (a) and the stress intensity factor (KIII). During this process, the system status can be checked by comparing the stress intensity factor and fracture toughness (KIIIc), and
Vibration from a mechanical system not only produces unwanted noises annoying to people around, but also runs a risk of fatigue failure that would actually hinder its functionality. There are several forms of vibration depending on the sources of excitation forms. Mechanical systems with rotating components can be subjected to sinusoidal excitation due to the fact the center of mass is not perfectly aligned with the rotating axis. If the rotating speed is strictly ramping up or ramping down, this can create an excitation whose frequency is changing with time in a frequency range corresponding to the speeds swept. Compared with a single sinusoidal excitation, the issue with fatigue at swept sinusoidal excitation, is that as it sweeps through a wide frequency range, some swept frequencies will definitely coincide with the natural frequencies of the system. Certainly, the stress response exactly at the resonant frequency becomes the highest and could account for a lot of fatigue damage
Lightweight design is a key factor in general engineering design practice, however, it often conflicts with fatigue durability. This paper presents a way for improving the effectiveness of fatigue performance dominated optimization, demonstrated through a case study on suspension brackets for heavy-duty vehicles. This case study is based on random load data collected from fatigue durability tests in proving grounds, and fatigue failures of the heavy-duty vehicle suspension brackets were observed and recorded during the tests. Multi-objective fatigue optimization was introduced by employing multiaxial time-domain fatigue analysis under random loads combined with the non-dominated sorting genetic algorithm II with archives. While evaluating fatigue life within optimization loops, particularly for multiaxial random load fatigue in the time domain, is time-intensive, this study is to improve computational efficiency in two strategies: 1) the dynamic adjustment of target nodes from the
High cycle fatigue (HCF) S-N curves of steels are applied by OEMs for direct evaluation of the products' durability or as an input to their CAE for design purpose. It has been found that the existing models for S-N data resulting HCF test might have difficulties in properly depicting the entire spectrum of fatigue lives. To overcome these difficulties, a new equation has been developed based on the relationship between the behaviors of short and long fatigue lives. The new equation was applied to model S-N data resulting from recent HCF testing of several steels and was compared with the 3 existing popular models. The comparison in the preliminary validations indicated that the new equation has high potential for application in more accurate S-N data modeling and fatigue limit prediction.
A special spot weld element (SWE) is presented for simplified representation of spot joints in complex structures for structural durability evaluation using the mesh-insensitive structural stress method. The SWE is formulated using rigorous linear four-node Mindlin shell elements with consideration of weld region kinematic constraints and force/moments equilibrium conditions. The SWEs are capable of capturing all major deformation modes around weld region such that rather coarse finite element mesh can be used in durability modeling of complex vehicle structures without losing any accuracy. With the SWEs, all relevant traction structural stress components around a spot weld nugget can be fully captured in a mesh-insensitive manner for evaluation of multiaxial fatigue failure. For validation purposes, a set of spot-welded lab test specimens has been analyzed for demonstrating the mesh-sensitivity of structural stress computation and fatigue test data transferability, e.g., from lap
The flight mechanisms of birds have long inspired efforts to develop bioinspired aerial vehicles. This study presents a computational framework to analyze a flapping mechanism's structural behavior and performance based on the Scotch yoke principle. A three-dimensional CAD model is developed and meshed for finite element analysis in ANSYS. Structural steel is chosen as the material. Static analysis is performed under simulated flapping loads to predict deformation, stresses, fatigue life, and failure points. Preliminary results identify regions of high-stress concentration requiring optimization. Topology optimization is conducted to determine an optimal material layout within defined constraints. Additional shape and compliance optimizations are employed. Comparison of initial and optimized designs significantly reduces maximum deformation and stresses throughout the structure. Fatigue life and safety factors are markedly improved. This study enhances understanding of Scotch yoke
Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization. An adapted design for a
The ball joint with cross groove offers both angular and plunging motion. When transmitting the same torque, the cross groove ball joint is lighter than other plunging Constant Velocity Joints (CVJs). It is crucial for the design of the joint and enhancing the contact fatigue life of the raceway to accurately estimate component loads of the ball joints with cross groove. In this study, the transmission efficiency of the joint and the peak value of contact force between ball and the track are used as evaluation indexes for characterizing dynamic performance of the joint. A multibody dynamic model of the joint is established to calculate its dynamic performance. In the model, the contact properties and friction characteristics of the internal structures were modeled, and a nonlinear equivalent spring and damping model was adopted for estimating the contact force. The transmission efficiency loss of the cross groove joint was measured and compared with the calculated values. Taking
The fatigue prediction model of an air spring based on the crack initiation method is established in this study. Taking a rolling lobe air spring with an aluminum casing as the studying example, a finite element model for analyzing force versus displacement is developed. The static stiffness and dimensional parameters of limit positions are calculated and analyzed. The influence of different modeling methods of air springs bellow are compared and analyzed. Static stiffness measurement of an air spring is conducted, and the calculation results and the measured results of the static stiffness are compared. It is shown that the relative error of the measured stiffness and calculated stiffness is within 1%. The Abaqus post-processing stage is redeveloped in Python language. The damage parameters including the maximum principal nominal strain, maximum Green-Lagrange strain, and effective stress of air spring bellows are extracted and calculated to find out the critical points, where the
This study deals with the fatigue life prediction methodology of welding simulation components involving arc welding. First, a method for deriving the cyclic deformation and fatigue properties of the weld metal (that is also called ER70S-3 in AWS, American Welding Standard) is explained using solid bar specimens. Then, welded tube specimens were used with two symmetric welds and subjected to axial, torsion, and combined in-phase and out-of-phase axial-torsion loads. In most previous studies the weld bead’s start/stop were arbitrarily removed by overlapping the starting and stop point. Because it can reduce fatigue data scatter. However, in this study make the two symmetric weld’s start/stops exposed to applying load. Because the shape of the weld bead generated after the welding process can act as a notch (Ex. root notch at weld start / Crater at weld stop) to an applied stress. Accordingly, they were intentionally designed to cause stress concentrations on start/stops. A geometric 3D
Cast austenitic stainless steels, such as 1.4837Nb, are widely used for turbo housing and exhaust manifolds which are subjected to elevated temperatures. Due to assembly constraints, geometry limitation, and particularly high temperatures, thermomechanical fatigue (TMF) issue is commonly seen in the service of those components. Therefore, it is critical to understand the TMF behavior of the cast steels. In the present study, a series of fatigue tests including isothermal low cycle fatigue tests at elevated temperatures up to 1100°C, in-phase and out-of-phase TMF tests in the temperature ranges 100-800°C and 100-1000°C have been conducted. Both creep and oxidation are active in these conditions, and their contributions to the damage of the steel are discussed.
Rubber isolators are widely used under random vibrations. In order to predict their fatigue life, a study on the fatigue analysis methodology for rubber isolators is carried out in this paper. Firstly, taking a mount used for isolating air conditioning compressor vibrations as studying example, accelerations versus time of rubber isolator at both sides are acquired for a car under different running conditions. The acceleration in time domain is transformed to frequency domain using the Fourier transform, and the acceleration power spectral density (PSD) is the obtained. Using the PSD as input, fatigue test is carried for the rubber isolator in different temperature and constant humidity conditions. A finite element model of the rubber isolator using ABAQUS is established for estimating fatigue life, and model validity is verified through static characteristic testing. Dynamic responses of the rubber isolator at frequency domain are calculated if a unit load is applied. The estimated
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