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The production of electric vehicles (EVs) has a significant environmental impact, with up to 50 % of their lifetime greenhouse gas potential attributed to manufacturing processes. The use of sustainable materials in EV design is therefore crucial for reducing their overall carbon footprint. Wood laminates have emerged as a promising alternative due to their renewable nature. Additionally, wood-based materials offer unique damping properties that can contribute to improved Noise, Vibration, and Harshness (NVH) characteristics. In comparison to conventional materials such as aluminum, ply wood structures exhibit beneficial damping properties. The loss factor of plywood structures with a thickness below 20 mm ranges from 0.013 to 0.032. Comparable aluminum structures however exhibit only a fraction of this loss factor with a range between 0.002 and 0.005.

Composite materials, pioneered by aerospace engineering due to their lightweight, strength, and durability properties, are increasingly adopted in the high-performance automotive sector. Besides the acknowledged composite components’ performance, enabled lightweighting is becoming even more crucial for energy efficiency, and therefore emissions along vehicle use phase from a decarbonization perspective. However, their use entails energy-intensive and polluting processes involved in raw material production, in manufacturing processes, and, in particular, in end-of-life disposal. Carbon footprint is the established indicator to assess the environmental impact of climate-changing factors on products or services. Research on different carbon footprint sources reduction is increasing, and even the European Composites Industry Association is demanding the development of specific Design for Sustainability approaches.

Passengers perceive annoying noise significantly stronger for electric vehicle, as an e-motor is in the absence of background noise, such as traditional powertrain noise. Automotive body coating squeak noise is becoming the most annoying noise for customers. It is a high-frequency noise that is attributed to the viscoelastic property of electrophoretic painting. The reason behind of this complex phenomenon lies in several aspects, so the solving solutions are also varying. Previous studies often focused to analyze the root cause of body squeaks through finite element analysis of body-in-white (BIW) opening diagonal distortion, as well as welding spots distribution. This paper first attempts to address it with the Maxwell viscoelastic model to describe the physical phenomenon. The model is composed of Maxwell element, a linear spring and a nonlinear dashpot to simulate the dynamic mechanical behavior of vehicle body coating when encountering vibration.

Centrifugal fans are applied in many industrial and civil applications, such as manufacturing processes and building HVAC systems. They can also be found in automotive applications. Noise-reduction mea- sures for centrifugal fans are often challenging to establish, as acous- tic performance may be considered a tertiary purchase criterion after energetic efficiency and price. Nonetheless, their versatile application raises the demand for noise control. In a low-Mach-number centrifugal fan, acoustic waves are predominantly excited by aerodynamic fluctu- ations in the flow field and transmit to the exterior via the housing and duct walls. The scientific literature documents numerous mech- anisms that cause flow-induced sound generation, even though only some are considered well-understood. Numerical simulation methods are widely used to gather spatially high-resolved insights into physical fields.

Although structural intensity was introduced in the 80's, this concept never found practical applications, neither for numerical nor experimental approaches. Quickly, it has been pointed out that only the irrotational component of the intensity offers an easy interpretation of the dynamic behavior of structures by visualizing the vibration energy flow. This is especially valuable at mid and high frequency where the structure response understanding can be challenging. A new methodolodgy is proposed in order to extract this irrotational intensity field from the Finite Element Model of assembled structures such as Bodies In White. This methodology is hybrid in the sense that it employs two distinct solvers: a dynamic solver to compute the structural dynamic response and a thermal solver to address a diffusion equation analogous to the thermal conduction built from the previous dynamic response.

Water management in PEMFC power generation systems is a key point to guarantee optimal performances and durability. It is known that a poor water management has a direct impact on PEMFC voltage, both in drying and flooding conditions: furthermore, water management entails phenomena from micro-scale, i.e., formation and water transport within membrane, to meso-scale, i.e., water capillary transport inside the GDL, up to the macro-scale, i.e., water droplet formation and removal from the GFC. Water transport mechanisms through the membrane are well known in literature, but typically a high computational burden is requested for their proper simulation. To deal with this issue, the authors have developed an analytical model for the water membrane content simulation as function of stack temperature and current density, for fast on-board monitoring and control purposes, with good fit with literature data.

The benefits introduced by the replacement of conventional centrifugal pumps with volumetric machines for Internal Combustion Engines (ICEs) cooling were experimentally and theoretically proven in literature. In particular, Sliding Rotary Vane Pumps (SVRPs) ensure to achieve an interesting reduction of ICEs fuel consumption and CO2 emissions. Despite volumetric pumps are a reference technology for ICE lubrication oil circuits, the application in ICE cooling systems still not represent a ready-to-market solution. Particularly challenging is the case of Heavy-Duty ICE due to the wide operating range the pump covers in terms of flow rate delivered. Generally, SVRPs are designed to operate at high speeds to reduce machine dimensions and, consequently, the weight. Nevertheless, speed increase could lead to a severe penalization of pump performance since the growth of the friction losses.

In applications demanding high performance under extreme conditions of pressure and temperature, a range of Mechanically Attached Fittings (MAFs) is offered by various Multinational Corporations (MNCs). These engineered fittings have been innovatively designed to meet the rigorous requirements of the aerospace industry, offering a cost-effective and lightweight alternative to traditional methods such as brazing, welding, or other mechanically attached tube joints. One prominent method employed for attaching these fittings to tubing is through Internal Swaging, a mechanical technique. This process involves the outward formation of rigid tubing into grooves within the fitting. One of the methods with which this intricate operation is achieved is by using a drawbolt - expander assembly within an elastomeric swaging machine.

Selective Laser Melting (SLM) has gained widespread usage in aviation, aerospace, and die manufacturing due to its exceptional capacity for producing intricate metal components of highly complex geometries. Nevertheless, the instability inherent in the SLM process frequently results in irregularities in the quality of the fabricated components. As a result, this hinders the continuous progress and wider acceptance of SLM technology. Addressing these challenges, in-process quality control strategies during SLM operations have emerged as effective remedies for mitigating the quality inconsistencies found in the final components. This study focuses on utilizing optical emission spectroscopy and IR thermography to continuously monitor and analyze the SLM process within the powder bed, with the aim of strengthening process control and minimizing defects.

Bio-composites have gained significant attention within the aerospace industry due to their potential as a sustainable solution that addresses the demand for lightweight materials with reduced environmental impact. These materials blend natural fibers sourced from renewable origins, such as plant-based fibers, with polymer matrices to fabricate composite materials that exhibit desirable mechanical properties and environmental friendliness. The aerospace sector's growing interest in bio-composites originates from those composites’ capacity to mitigate the industry's carbon footprint and decrease dependence on finite resources. This study aims to investigate the suitability of utilizing plant derived flax fabric/PLA (polylactic acid) matrix-based bio-composites in aerospace applications, as well as the recyclability potential of these composites in the circular manufacturing economy.

For intelligent vehicles, a robust perception system relies on training datasets with a large variety of scenes. The architecture of federated learning allows for efficient collaborative model iteration while ensuring privacy and security by leveraging data from multiple parties. However, the local data from different participants is often not independent and identically distributed, significantly affecting the training effectiveness of autonomous driving perception models in the context of federated learning. Unlike the well-studied issues of label distribution discrepancies in previous work, we focus on the challenges posed by scene heterogeneity in the context of federated learning for intelligent vehicles and the inadequacy of a single scene for training multi-task perception models. In this paper, we propose a federated learning-based perception model training system.

Fracture characterization of automotive metals under simple shear deformation is critical for the calibration of advanced fracture models employed in forming and crash simulations. In-plane shear fracture tests of high ductility materials have proved challenging since the sample edge fails first in uniaxial tension before the fracture limit in shear is reached at the center of the gage region. Although through-thickness machining is undesirable, it appears required to promote higher strains within the shear zone. The present study seeks to adapt existing in-plane shear geometries, which have otherwise been successful for many automotive materials, to have a local shear zone with a reduced thickness. It is demonstrated that a novel shear zone with a pocket resembling a “peanut” can promote shear fracture within the shear zone while reducing the risk for edge fracture. An emphasis was placed upon machinability and surface quality for the design of the pocket in the shear zone.

Fracturing in a tight radius during bending is one of the major manufacturing issues in forming Advanced High Strength Steels (AHSS). The study investigated the bendability of AHSS under two forming conditions: bending with and without stretched over the die radius. The bendability was evaluated by conducting modified Bending Under Tension (BUT) test for stretch bending and 90o v bend test for bending without stretch. The study also examined the effect of material properties on the limiting bend ratio. Various strength high strength steels, range from 420 MPa to 1700 MPa tensile strength, were selected in the study. Results indicated that critical radius-to-thickness ratios between the two tests are different but correlated in a relationship which was depicted in the bendability diagram.

Multiple hybrid bead designs were investigated in this study to control the springback on DP780 samples using post-stretching technique. The performance of the four different hybrid bead designs was evaluated by measuring the minimum blank-lock tonnage required to control the springback during a U-channel stamping process. A finite element (FE) model of the U-channel stamping process was developed to simulate the process and predict the minimum blank-lock tonnage required for springback control using each of the hybrid bead designs. It is shown that the developed FE model predicts both the required minimum blank-lock tonnage for post-stretching, and the springback profile, with good accuracy.

During the vehicle lifecycle, customers are able to directly perceive the outer panel stiffness of vehicles in various environmental conditions. The outer panel stiffness is an important factor for customers to perceive the robustness of the vehicle. In the real test of outer panel stiffness after prototype production, evaluators manually press the outer panel in advance to identify vulnerable areas to be tested and evaluate the performance only in those area. However, when developing the outer panel stiffness performance using FEA (Finite Element Analysis) before releasing the drawing, it is not possible to filter out these areas, so the entire outer panel must be evaluated. This requires a significant amount of computing resources and manpower. In this study, an approach utilizing artificial intelligence was proposed to streamline the outer panel stiffness analysis and improve development reliability.

Heavy Vehicle Event Data Recorders (HVEDRs) have the ability to capture important data surrounding an event such as a crash or near crash. Efforts by many researchers to analyze the capabilities and performance of these complex systems can be problematic, in part, due to the challenges of obtaining a heavy truck, the necessary space to safely test systems, the inherent unpredictability in testing, and the costs associated with this research. In this paper, a method for simulating vehicle speed sensor (VSS) inputs to HVEDRs to trigger events is introduced and validated. Full-scale instrumented testing is conducted to capture raw VSS signals during steady state and braking conditions. The recorded steady state VSS signals are injected into the HVEDR along with synthesized signals to evaluate the response of the HVEDR. Brake testing VSS signals are similarly captured and injected into the HVEDR to trigger an event record.

Multiple experimental studies were performed on galling intiation for variety of tooling materials, coatings and surface treatments, sheet materials with various surface textures and lubrication. Majority of studies were performed for small number of samples in laboratory conditions. In this paper, the methodology of screening experiment using different combinations of tooling configurations and sheet material in the lab followed by the high volume small scale U-bend performed in the progressive die on the mechanical press is discussed. The experimental study was performed to understand the effect of the interface between the sheet metal and the die surface on sheet metal flow during stamping operations. Aluminum sheet AA5754 2.5mm thick was used in this experimentation. The sheet was tested in laboratory conditions by pulling between two flat insert with controllable clamping force and through the drawbead system with variable radii of the female bead.

Roller offsetting is an incremental forming technique used to generate offset stiffening or mating features in sheet metal parts. Compared to die forming, roller offsetting utilizes generic tooling to create versatile designs at a relatively lower forming speed, making it well-suited for low volume productions in automotive and other industries. However, more significant distortion can be generated from roller offset forming process resulting from springback after forming. In this work, we use particle swarm optimization to identify the tool path and resulting feature geometry that minimizes distortion. In our approach, time-dependent finite element simulations are adopted to predict the distortion of each candidate tool path using a quarter symmetry model of the part. A multi-objective fitness function is used to both minimize the distortion measure while constraining the minimal radius of curvature in the tool path.

Over the past decade, significant progress has been made in developing algorithms and improving hardware for automated driving. However, conducting research and deploying advanced algorithms on automated vehicles for testing and validation remains costly, especially for academia. This paper presents the efforts of our research team to integrate the newest version of the open-source Autoware software with the commercially available DataSpeed Drive-by-Wire (DBW) system, resulting in the creation of a versatile and robust automated vehicle research platform. Autoware, an open-source software stack based on the 2nd generation Robot Operating System (ROS2), has gained prominence in the automated vehicle research community for its comprehensive suite of perception, planning, and control modules. The DataSpeed DBW system directly communicates with the vehicle's CAN bus and provides precise vehicle control capabilities.

Autonomous driving technology is more and more important nowadays, it has been changing the living style of our society. As for autonomous driving planning and control, vehicle dynamics has strong nonlinearity and uncertainty, so vehicle dynamics and control is one of the most challenging parts. At present, many kinds of specific vehicle dynamics models have been proposed, this review attempts to give an overview of the state of the art of vehicle dynamics models for autonomous driving. Firstly, this review starts from the simple geometric model, vehicle kinematics model, dynamic bicycle model, double-track vehicle model and multi degree of freedom (DOF) dynamics model, and discusses the specific use of these classical models for autonomous driving state estimation, trajectory prediction, motion planning, motion control and so on.