Browse Topic: Suspension systems
This paper aims to explore the application of machine learning techniques to the analysis of road suspension systems, with particular emphasis on mechanical leaf spring suspensions. These systems are essential for vehicle performance, as they guarantee comfort and stability while driving, and they have an intrinsically complex and non-linear dynamic behavior. Because of this complexity, traditional approaches often prove costly and insufficient to represent operating conditions. In this context, machine learning techniques stand out for their ability to learn patterns from experimental data, allowing the modelling of non-linear phenomena that characterize road implement suspensions. One of the main contributions of this study is the demonstration that machine learning algorithms are capable of identifying complex patterns to represent the behavior of the system, as well as facilitating the detection of anomalies and potential faults in the suspension system, contributing to predictive
Advanced motion control technologies are essential to modern aerospace design, supporting a wide range of safety-critical and comfort-driven applications. In aerospace, motion control components such as gas springs, actuators, and dampers are integral to nearly every commercial aircraft, rocket, satellite, and space vehicle. These critical elements support flight safety and transport functions, from the dependable deployment of landing gear and cargo doors to the smooth, ergonomic operation of seating for pilots and passengers.
The continuous improvement of validation methodologies for mobility industry components is essential to ensure vehicle quality, safety, and performance. In the context of mechanical suspensions, leaf springs play a crucial role in vehicle dynamics, comfort, and durability. Material validation is based on steel production data, complemented by laboratory analyses such as tensile testing, hardness measurements, metallography, and residual stress analysis, ensuring that mechanical properties meet fatigue resistance requirements and expected durability. For performance evaluation, fatigue tests are conducted under vertical loads, with the possibility of including "windup" simulations when necessary. To enhance correlation accuracy, original suspension components are used during testing, allowing for a more precise validation of the entire system. Additionally, dynamic stiffness measurements provide valuable input for vehicle dynamics and suspension geometry analysis software, aiding in
The design, development, and optimization of modern suspension systems is a complex process that encompasses several different engineering domains and disciplines such as vehicle dynamics simulation, tire data analysis, 1D lap-time simulation, 3D CAD design and structural analysis including full 3D collision detection. Typically, overall vehicle design and suspension development are carried out in multiple iterative design loops by several human specialists from diverse engineering departments. Fully automating this iterative design process can minimize manual effort, eliminate routine tasks and human errors, and significantly reduce design time. This desired level of automation can be achieved through digital modeling, automated model generation, and simulation using graph-based design languages and an associated language compiler for translation and execution. Graph-based design languages ensure the digital consistency of data, the digital continuity of processes, and the digital
This paper presents an analytical approach for identifying suspension kingpin alignment parameters based on screw axis theorem and differential calculation model. The suspension kingpin caster and inclination alignment parameters can produce additional tire force, which affects vehicle handling dynamics. In wheel steering process, the multi-link suspension control arms lead to movement of the imaginary kingpin, which can cause change in suspension kingpin alignment parameters. According to the structure mechanism of commercial vehicle multi-link independent suspension, the kinematics characteristics of imaginary kingpin were analyzed based on the screw axis theorem. The angular velocity and translation velocity vectors were calculated. In order to avoid the influence of bushing deformation, the unique differential identification model was established to evaluate the suspension kingpin alignment parameters, and the identification results were compared with the ADAMS/Car data. The
Road noise caused by road excitation is a critical factor for vehicle NVH (Noise, Vibration, and Harshness) performance. However, assessing the individual contribution of components, particularly bushings, to NVH performance is generally challenging, as automobiles are composed of numerous interconnected parts. This study describes the application of Component Transfer Path Analysis (CTPA) on a full vehicle to provide insights into improving NVH performance. With the aid of Virtual Point Transformation (VPT), blocked forces are determined at the wheel hubs; afterward, a TPA is carried out. As blocked forces at the wheel hub are independent of the vehicle dynamics, these forces can be used in simulations of modified vehicle components. These results allow for the estimation of vehicle road noise. To simulate changes in vehicle components, including wheel/tire and rubber bushings, Frequency-Based Substructuring (FBS) is used to modify the vehicle setup in a simulation model. In this
In the highly competitive automotive industry, optimizing vehicle components for superior performance and customer satisfaction is paramount. Hydrobushes play an integral role within vehicle suspension systems by absorbing vibrations and improving ride comfort. However, the traditional methods for tuning these components are time-consuming and heavily reliant on extensive empirical testing. This paper explores the advancing field of artificial intelligence (AI) and machine learning (ML) in the hydrobush tuning process, utilizing algorithms such as random forest, artificial neural networks, and logistic regression to efficiently analyze large datasets, uncover patterns, and predict optimal configurations. The study focuses on comparing these three AI/ML-based approaches to assess their effectiveness in improving the tuning process. A case study is presented, evaluating their performance and validating the most effective method through physical application, highlighting the potential
The recent addition of fully electric powertrains to propulsion system options has increased the relevance of sound and vibration from electric motors and gearboxes. Electrified beam axles require different metrics from conventional beam axles for noise and vibration because they have multiple sources of vibration energy, including an electric motor and a reduction gearbox. Improved metrics are also driven by the stiff suspension connections and lack of significant isolation compared to electric drive units. Blocked force is a good candidate because it can completely characterize the vibration energy transmitted into a receiver and is especially useful because it is theoretically independent of the vehicle-side structure. While the blocked force methodology is not new, its application to beam axles is relatively unexplored in the literature. This paper demonstrates a case study of blocked force measurement of an electrified beam axle with a leaf spring suspension. The axle was tested
Electric vehicles (EVs) are particularly susceptible to high-frequency noise, with rubber eigenmodes significantly influencing these noise characteristics. Unlike internal combustion engine (ICE) vehicles, EVs experience pronounced variations in dynamic preload during torque rise, which are substantially higher. This dynamic preload variation can markedly impact the high-frequency behaviour of preloaded rubber bushings in their installed state. This study investigates the effects of preload and amplitude on the high-frequency dynamic performance of rubber bushings specifically designed for EV applications. These bushings are crucial for vibration isolation and noise reduction, with their role in noise, vibration, and harshness (NVH) management being more critical in EVs due to the absence of traditional engine noise. The experimental investigation examines how preload and excitation amplitude variations influence the dynamic stiffness, damping properties, and overall performance of
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