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Abstract Conventional fusion joining techniques pervasive in the automotive industry are unable to effectively join aluminum and steel. To solve this problem, a technique termed friction element welding (FEW) has been developed, which is able to join any nonferrous top sheet material to a base steel layer, independent of the base layer strength. FEW works on the same principles as friction welding, as a steel element is pushed and rotated against a nonferrous top sheet to create frictional energy which softens and flows the material around the fastener shaft and under the fastener head, exposing the steel below. The element then contacts the steel and bonds through traditional friction welding. FEW is a four-step process (penetration, cleaning, welding, compression), with two to four parameters (endload, spindle speed, displacement transition, time transition) controlling each step.

Abstract Surface roughness is one of the important aspects in producing quality die. Wire Electrical Discharge Machine (WEDM) is commonly used in tool and die fabrication, since the die material is usually difficult to cut using traditional metal removal processes. Selection of optimal WEDM cutting parameters is crucial to obtain quality die finish. In this study, 2379 steel which equivalent to SKD 11 is selected as the die material. Four main WEDM cutting parameters, namely, pulse duration (A), pulse interval (B), servo voltage (C), ignition pulse current (D), were experimentally evaluated for both main cut and multiple trim cuts using Taguchi Method. Taguchi’s L9 orthogonal array is employed for experimental design and analysis of variance (ANOVA) was used in recognizing levels of significance of WEDM cutting parameters.

Abstract Nylon polymer with an optimal blend of Kevlar, fiberglass, and high-speed, high temperature (HSHT) Fiberglass offers improved characteristics such as flexural strength, wear resistance, electrical insulation, shock absorption, and a low friction coefficient. For this reason, the polymer composite manufactured by combining HSHT, Kevlar, and fiberglass with nylon as base material will expand the uses of nylon in the aerospace, automotive, and other industrial applications related to ergonomic tools, assembly trays, and so forth. The proposed work was carried out to investigate the continuous fiber reinforcement (CFR) in nylon polymer using a dual extrusion system. Twenty experimental runs were designed using a face-centered central composite design (FCCD) approach to analyze the influence of significant factors such as reinforcement material, infill pattern, and fiber angle on the fabricated specimen as per American Society for Testing Materials (ASTM) standards.

Abstract Selective inhibition sintering (SIS) results in easy, flexible, fast, and cost-efficient fabrication of functional parts by using powder material for various applications. The functional part is important for operational examination by fabricating the part unswervingly from computer-aided design (CAD) data. However, poor surface quality is the major disadvantage in the SIS procedure. The selection procedure of optimal operating parameters plays a major role in the fabrication of end products. The present study discusses the effect of key contributing operating parameters on the surface quality of the polyamide parts fabricated by the SIS process. Parameters like heater power (HP), layer thickness (LT), heater feed rate (HFR), machine feed rate (MFR), and bed temperature (BT) were considered in this study.

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%).

Abstract The high quality of the machined parts in a short time is a research challenge for enhancing these parts’ operating performance. Optimizing the machining operations and adequately selecting the cutting parameters can solve this challenge. Thus, this work proposes an optimization approach of the machining process parameters of epoxy hybrid nanocomposites reinforced by multiwall carbon nanotubes (MWCNTs) and aluminum oxide (Al2O3). Cutting speed (V), feed rate (F), insert nose radius, and depth of cut (D) were the machining parameters. The roundness error and surface roughness (Ra) were selected as process response control parameters. The optimization techniques such as response surface method (RSM) and grey relation analysis (GRA) with the variance of analysis (ANOVA) were involved. Forty experimental runs were performed. The RSM optimization and ANOVA results showed that the insert nose radius and F are the most significant factors that affect the Ra.

Abstract Crash simulation is targeted mainly carried out by the collision regulations FMVSS simulation to identify problems in vehicle structures. A modern car structure consist of several thousand weld-type connections, and failure in these connections plays an important role for the crashworthiness of the vehicle. Therefore accurate modeling of these connections is important for the automotive industry in order to improve Vehicle collision characteristics. In pursuit of this key requirement, we introduced a proper methodology for the development detailed weld model to study structural response of the weld when the applied load range is beyond the yield strength. Three-dimensional finite element (FE) models of spot welded joints are developed using the LS-Dyna FE code. In this process the force estimation model of spot welds is explained. The results from this paper shows good agreement between the simulations and the tests.

Abstract Increasing the strength of materials is effective in reducing weight and boosting structural part performance, but there are cases where the residual strain generated during the process of manufacturing of high-strength materials results in a decline of durability. It is therefore important to understand how the residual strain in a manufactured component changes due to processing conditions. In the case of a connecting rod, because the strain load on the connecting rod rib sections is high, it is necessary to clearly understand the distribution of strain in the ribs. However, because residual strain is generally measured by using X-ray diffractometers or strain gauges, measurements are limited to the surface layer of the parts. Neutron beams, however, have a higher penetration depth than X-rays, allowing for strain measurement in the bulk material.

Abstract New Editor-in-Chief: Dr. Mark Barkey

Abstract This article addresses the issue of a design to provide remote vehicle communication services sustainably. These services include new features such as remote repair of Electronic Control Unit (ECU)’s software errors and feature on demand, to mention just a few key objectives. With the usual implementations of the Modular Vehicle Communication Interface (MVCI) runtime server [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] many difficulties remain [15]. They are not sustainable and require dedicated hardware. The Dictionary Server discussed here provides necessary data to diagnostic applications in general, without putting at risk Original Equipment Manufacturer (OEM)’s expertise. It also provides data to the road infrastructure for V2V- and Vehicle-to-Infrastructure (V2X)-based services. This crucial diagnostic data contains ECUs’ communication parameters, memory programming data, and other available functions. They are kept confidentially by OEMs.

Abstract Joining titanium (Ti) alloys with conventional processes is difficult due to their complex structural properties and ability of phase transformation. Concerning all the difficulties, diffusion bonding is considered as an appropriate process for joining Ti alloys. Ti6Al4V, which is an α+β alloy widely used for aero engine component manufacturing, is diffusion bonded in this investigation. The diffusion bonding process parameters such as bonding temperature, bonding pressure, and holding time were optimized to achieve desired bonding characteristics such as shear strength, bonding strength, bonding ratio, and thickness ratio using response surface methodology (RSM). Empirical relationships were developed for the prediction of the bond characteristics, and sensitivity analysis was performed to determine the increment and decrement tendency of the shear strength with respect to the bonding parameters.

Abstract In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN).

Abstract Halogen detector is an important halogen gas leakage detection instrument. In order to ensure that the upper and lower shells have the same quality, it is necessary to use one mold and two pieces in production. Compared with the conventional one-mold two-cavity process, it is easier to produce warpage and volume shrinkage. To solve this problem, a multi-objective injection molding process optimization method based on deep neural network (DNN) model based on stochastic weight average (SWA) method and multi-objective evolutionary algorithm based on decomposition (MOEA/D) was proposed. Melt temperature, mold temperature, injection pressure, holding pressure, holding time, and cooling time are the six parameters and important structure parameters (gate diameter) as design variables, warpage, and volume shrinkage rate as the optimization goal. The neural network model between variable and goal was established, and the MOEA/D algorithm was used for global optimization.

Abstract In a context of increasing globalization, sustainability is a crucial topic for automotive companies, and carmakers are subject to a strong regulatory pressure intended to make light-duty vehicles more environmentally sustainable. Such a scenario imposes that designers and product developers bind design performances with greater environmental commitment. Therefore, this article proposes an innovative eco-design methodology to provide the automotive designer with the concept development phase of single mono-material components. Starting from the geometry and load case of the specific application, the method generates different design alternatives (both in terms of materials and manufacturing processes applied), which are evaluated under both structural integrity and environmental point of view. In particular, the assessment and selection steps are carried out through a single-score indicator based on a multi-objective approach.

Abstract In motion planning of autonomous vehicles, non-signalized intersections pose challenges due to a variety of traffic flows. Common motion planning algorithms use the current environmental information to find an optimal path that satisfies traffic safety and efficiency. Because of the non-signalized intersection dynamics, the algorithms need to iteratively generate a path while avoiding collision with other obstacles. Traditional grid-based planning algorithms present an enormous computational burden for real-time implementation. Meanwhile, sample-based algorithms like Rapidly exploring Random Trees (RRT) could be used for local motion planning to determine possible safe paths and quickly reselect alternative paths towards the goal. However, near an intersection, estimating another vehicle’s dynamic state and avoiding collision through standard RRT can be cumbersome.

Abstract As electric vehicles (EVs) begin to increase their market share in the transport sector, the efficiency of battery packs becomes critical to their performance. Within large battery packs, cell variations occur due to manufacturing processes but can also become prominent during operation due to ineffective thermal management and accelerated degradation of some cells. A battery management system (BMS) will generally account for variations in state of charge (SOC) for cells in series through balancing, but conventional BMSs do not tend to consider the imbalances of cells in parallel as their SOCs should eventually converge themselves. This can, however, lead to cells experiencing higher currents and therefore increased degradation compared to other cells within the pack.

Abstract Industry 4.0 is expected to revolutionize product development and, in particular, manufacturing systems. Cyber-physical production systems and digital twins of the product and process already provide the means to predict possible future states of the final product given the current production parameters. With the advent of further data integration coupled with the need for autonomous decision-making, methods are needed to make decisions in real time and in an environment of uncertainty in both the possible outcomes and in the stakeholders’ preferences over them. This article proposes a method of autonomous decision-making in data-intensive environments, such as a cyber-physical assembly system. Theoretical results in group decision-making and utility maximization using mixture distributions are presented. This allows us to perform calculations on expected utility accurately and efficiently through closed-form expressions, which are also provided.

Abstract Super duplex stainless steel (SDSS) is a type of stainless steel made of chromium (Cr), nickel (Ni), and iron (Fe). In the present work, a 1.6 mm wide thin sheet of SDSS is joined using gas tungsten arc welding (GTAW). The ideal parameter for a bead-on-plate trial is found, and 0.216 kJ/mm of heat input is used for welding. As an outcome of the welding heating cycle and subsequent cooling, a microstructural study revealed coarse microstructure in the heat-affected zone and weld zone. The corrosion rate for welded joints is 9.3% higher than the base metal rate. Following the corrosion test, scanning electron microscope (SEM) analysis revealed that the welded joint’s oxide development generated a larger corrosive attack on the weld surface than the base metal surface. The percentages of chromium (12.5%) and molybdenum (24%) in the welded joints are less than those in the base metal of SDSS, as per energy dispersive X-ray (EDX) analysis.

Abstract Reducing the weight of automotive components is one of the most achievable solutions for lowering the transport carbon footprint. This is the reason for the rapid increase over the last few years in the replacement of conventional alloys (i.e., steel and cast iron) with low-density materials (i.e., aluminum alloys, composites) and in the redesign of components shape in order to remove the unnecessary material (e.g., related to the introduction of additive manufacturing or high-strength materials). Despite this general trend, the use of higher-density metals and massive geometries is still predominant in the production of structural components, especially for heavy vehicles and safety-relevant parts. Aim of the present review is to summarize how this current situation can be overcome. The analysis started with an investigation about the materials that can be used for the production of structural parts, the potential reduction of the component weight and its costs.

Abstract The increasing use of aluminum alloy extrusion in automotive vehicle chassis as structural members has necessitated the need to investigate their crushing behaviors. This article experimentally examines in detail, for the first time with respect to strength, ductility, and microstructure, AA6063-T7 (overaged) condition and the standard T6 temper and their capacity to meet crashworthiness requirements. Both tempers were assessed based on their mechanical properties (strength, ductility, true stress/strain behavior to necking, plastic anisotropy, strain rate sensitivity, and post-instability ductility to fracture) and microstructure, which were determined using basic tensile testing methods and metallographic approach.