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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.

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.

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.

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.

Design for Manufacturing and Assembly (DFM+A), pioneered by Boothroyd and Dewhurst, has been used by many companies around the world to develop creative product designs that use optimal manufacturing and assembly processes. Correctly applied, DFM+A analysis leads to significant reductions in production cost, without compromising product time-to-market goals, functionality, quality, serviceability, or other attributes. In this two-day course, you will not only learn the Boothroyd Dewhurst Method, you will actually apply it to your own product design!

This course is verified by Probitas as meeting the AS9104/3A requirements for Continuing Professional Development. This course provides both a functional understanding of the principles involved in conducting a Design for Manufacture/Design for Assembly (DFM/DFA) study and the process for implementing a DFM/DFA culture into the organization.

The paramount importance of titanium alloy in implant materials stems from its exceptional qualities, yet the optimization of bone integration and mitigation of wear and corrosion necessitate advanced technologies. Consequently, there has been a surge in research efforts focusing on surface modification of biomaterials to meet these challenges. This project is dedicated to enhancing the surface of titanium alloys by employing shot peening and powder coatings of titanium oxide and zinc oxide. Comparative analyses were meticulously conducted on the mechanical and wear properties of both treated and untreated specimens, ensuring uniformity in pressure, distance, and time parameters across all experiments. The outcomes underscore the efficacy of both methods in modifying the surface of the titanium alloy, leading to substantial alterations in surface properties.

Abstract In this article, we investigated the effects of material parameters on the clinching joint geometry using finite element model (FEM) simulation and machine learning-based metamodels. The FEM described in this study was first developed to reproduce the shape of clinching joints between two AA5052 aluminum alloy sheets. Neural network metamodels were then used to investigate the relation between material parameters and joint geometry as predicted by FEM. By interpreting the data-driven metamodels using explainable machine learning techniques, the effects of the hard-to-measure material parameters during the clinching are studied. It is demonstrated that the friction between the two metal sheets and the flow stress of the material at high (up to 100%) plastic strain are the most influential factors on the interlock and the neck thickness of the clinching joints. However, their dependence on the material parameters is found to be opposite.

This standard establishes the recommended requirements for application of AMS3144 anodic electrodeposition primer to aerospace components. Adherence to these requirements will help facilitate satisfactory performance of the applied primer.

Abstract This research looks into how abrasive water jet machining (AWJM) can be used on carbon fiber-reinforced polymer (CFRP) materials, specifically how the kerf characteristics change with respect to change in process parameters. We carefully looked into four important process parameters: stand-off distance (SOD), water pressure (WP), traverse rate (TR), and abrasive mass flow rate (AMFR). The results showed that as SOD goes up, the kerf taper angle goes up because of jet dispersion, but as WP goes up, the angle goes down because jet kinetic energy goes up. The TR was directly related to the kerf taper angle, but it made the process less stable. The kerf drop angle was not greatly changed by AMFR. When it came to kerf top width, SOD made it wider, WP made it narrower, TR made it narrower, and AMFR made it a little wider. When the settings (SOD: 1 mm, WP: 210 MPa, TR: 150 mm/min, AMFR: 200 g/min) were optimized, the kerf taper angle and kerf top width were lowered.

The machining process is employed to transform a workpiece into a predefined geometry with the assistance of a cutting tool. Throughout this process, the cutting tool undergoes various adverse effects, including deformation, stress, thermal gradient, and more, all of which impact tool sharpness, surface finish, and tool life. These outcomes are also influenced by cutting parameters, specifically cutting speed, feed rate, and depth of cut. The present investigation aims to demonstrate the application of ANSYS analysis software in predicting stress, deformation, thermal gradient, and other factors on the tool insert tip for various machining parameters. To achieve this, an experimental setup was arranged to collect cutting force and temperature data using a dynamometer and thermocouples during the machining process of maraging steel with a tungsten carbide tool insert. Experiments were conducted with different combinations of machining parameters using design of experiments (DoE).

Abstract This article presents a strategy for the virtual calibration of a large-scale model representing a self-piercing rivet (SPR) connection. The connection is formed between a stack of three AA6016-T4 aluminum sheets and one SPR. The calibration process involves material characterization, a detailed riveting process simulation, virtual joint unit tests, and the final large-scale model calibration. The virtual tests were simulated by detailed solid element FE models of the joint unit. These detailed models were validated using experimental tests, namely peeling, single-lap joint, and cross-tests. The virtual parameter calibration was compared to the experimental calibration and finally applied to component test simulations. The article contains both experiments and numerical models to characterize the mechanical behavior of the SPR connection under large deformation and failure.

Abstract This study focused on the synthesis and characterization of monodisperse spherical TiO2 nanoparticles doped on the surface with Se (IV) in order to increase the mechanical properties of the bonded joint reinforcing. Work will begin with the synthesis of monodisperse quasi-spherical TiO2 nanoparticles with a modal diameter of less than 20 nm, using the sol-gel technique. Se (IV) selenium surface doping changed the specimen’s chemistry and physics. Different initial concentrations of the doping element will be tested. Next, a physicochemical characterization of the different solid systems will be carried out in order to determine the effect of the doping element on the properties of titanium dioxide. Their morphology and size will be studied through transmission electron microscope observations; volume chemical composition by X-ray diffraction analysis, EDX (energy-dispersive X-ray), and XRF (X-ray fluorescence).

Abstract 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.

Abstract In recent years, the use of cutting fluids has become crucial in hard metal machining. Traditional non-biodegradable cutting fluids have long dominated various industries for machining. This research presents an innovative approach by suggesting a sustainable alternative: a cutting fluid made from a blend of glycerol (GOL) and distilled water (DW). We conducted a thorough investigation, creating 11 different GOL and DW mixtures in 10% weight increments. These mixtures were rigorously tested through 176 experiments with varying loads and rotational speeds. Using Design-Expert software (DES), we identified the optimal composition to be 70% GOL and 30% DW, with the lowest coefficient of friction (CFN). Building on this promising fluid, we explored further improvements by adding three nanoscale additives: Nano-graphite (GHT), zinc oxide (ZnO), and reduced graphene oxide (RGRO) at different weight percentages (0.06%, 0.08%, 0.1%, and 0.3%).

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.