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The automotive lightweighting trends, being driven by sustainability, cost, and performance, that create the enormous demand for lightweight materials and design concepts, are assessed as a part of the circular economy solutions in modern mobility and transportation. The current strategies that aim beyond the basic weight reduction and cover also the structural efficiency as well as the economic and environmental impact are explained with an essence of guidelines for materials selection with an eco-friendly approach, substitution rules, and a paradigm of the multi-material design. Particular attention is paid to the metallic alloys sector and progress in global R&D activities that cover the “lightweight steel”, conventional aluminum, and magnesium alloys, together with well-established technologies of components manufacturing and future-oriented solutions, and with both adjusting to a transition from internal combustion engines to electric vehicles. Moreover, opportunities and challenges that the lightweighting creates are discussed with strategies of achieving its goals through structural engineering, including the metal-matrix composites, laminates, sandwich structures, and bionic-inspired archetypes. The profound role of the aerospace and car-racing industries is emphasized as the key drivers of lightweighting in mainstream automotive vehicles.

IntroductionWith the escalating demands for agricultural product quality in modern agriculture, peanuts, as a crucial economic crop, have their pod appearance quality directly influencing market value and consumer acceptance. Traditionally, the visual inspection of peanut pod appearance quality relies heavily on manual labor, which is not only labor-intensive and inefficient but also susceptible to subjective judgments from inspectors, thereby compromising the consistency and accuracy of inspection outcomes. Consequently, the development of a rapid, accurate, and automated inspection system holds significant importance for enhancing production efficiency and quality control in the peanut industry.MethodsThis study introduces the optimization and iteration of the YOLOv5s model, aiming to swiftly and precisely identify high-quality peanuts, peanuts with mechanical damage, moldy peanuts, and germinated peanuts. The CSPDarkNet53 network of the YOLOv5s model was substituted with the ShuffleNetv2 backbone network to reduce the model’s weight. Various attention mechanisms were explored for integration and substitution with the backbone network to enhance model performance. Furthermore, the substitution of various loss functions was investigated, with the Focal-EIoU loss function employed as the regression loss term for predicting bounding boxes, thereby improving inference accuracy.ResultsCompared to the YOLOv5s network model, SSE-YOLOv5s boasts a mere 6.7% of the original model’s parameters, 7.8% of the computation, and an FPS rate 115. 1% higher. Its weight size is a mere 7.6% of the original model’s, while the detection accuracy and mean average precision (mAP) reach 98.3% and 99.3%, respectively, representing improvements of 1.6 and 0.7 percentage points over the original YOLOv5s model.DiscussionThe results underscore the superiority of the SSE-YOLOv5s model, which achieves a maximum mAP of 99.3% with a minimal model size of 1. 1MB and a peak FPS of 192.3. This optimized network model excels in rapidly, efficiently, and accurately detecting the appearance quality of mixed multi-target peanut pods, making it suitable for deployment on embedded devices. This study provides an essential reference for multi-target appearance quality inspection of peanut pods.

Vehicle lightweighting is a promising strategy that can reduce energy consumption and GHG emissions without compromising vehicle's performance or size. The cost of lightweighting plays a critical role in determining the adoption of lightweighting technologies by consumers and manufacturers among advanced vehicle technologies. This analysis estimates the cost of lightweighting needed to achieve significant light-duty vehicle adoption to provide reductions in use-phase GHG emissions. Three different costs of lightweighting scenarios in the U.S. market including a baseline scenario, advanced technology scenario, and widespread scenario are evaluated employing Automotive Deployment Options Projection Tool (ADOPT) in conjunction with other technology improvement assumptions (e.g., advancements in fuel and battery technologies, and material price reductions) from DOE. ADOPT leverages a database of over 700 existing vehicle models and options, enabling it to provide a high degree of realism and capture the unique characteristics of popular vehicles and the endogenously evolvement of the vehicle options. For baseline scenario, the use-phase GHG emissions are reduced by more than 50% and lightweighting fraction reaches 15% by 2046 compared to 2015 levels. The widespread scenario further reduces the GHG emissions by about 4% from the additional 10% glider mass reduction compared to the baseline scenario. The benefit came largely from lightweighting being implemented in the large market segment of lower-price vehicles, due to the relatively low lightweighting cost (5/kg).

It is generally accepted that backstress/internal stress development plays an important role in the response to deformation of many metals. Techniques for quantifying such stresses, at both a macroscopic and a microscopic level, are vital to ongoing efforts to model and predict such deformation behavior. The stress dip test has been used mostly for the study of creep, including the development and calibration of related models; however, it has also been noted that the quantification of macroscopic backstress is another potential application. In this research, the stress dip test is used to assess backstress evolution during tensile deformation in AA6016-T4, including the extraction of backstress from the observed data. The results are compared with those for samples prestrained under biaxial and plane strain modes. It is found that backstress accumulates with ε 0.61 under uniaxial tension, indicating an approximately linear relationship between geometrically necessary dislocation and backstress development in this material for this strain path. Furthermore, samples prestrained using different strain paths have lower backstress than those strained entirely under uniaxial tension. A crystal plasticity finite element model, with an additional backstress law, captures the stress dip behavior well. After calibration to the stress dip tests, application to cyclical testing is also accurate. However, the model may require a more sophisticated backstress saturation constraint to accurately mirror the strain rate response during stress dip tests at higher deformation levels.

With the push towards sustainable alloy production, using recycled material in casting Al alloys has become essential. However, high recycle content (HRC) aluminum alloys typically have a high iron content, leading to the formation of Fe-bearing intermetallic particles (Fe-IMCs) that affect the mechanical performance and formability of the alloy. Historically, 2D microscopy-based characterization techniques have been used to assess the size and morphology of these Fe-IMCs. While widely used, these 2D techniques are often incapable of capturing the complex 3D interconnected morphologies of the Fe-IMCs. In this work, we present a methodology for the high-throughput compositional and 3D morphological characterization of Fe-IMCs in a primary (AA 5182) and a high recycle content (HRC alloy) in the as-cast and homogenized states, using a combination of 3D X-ray Computed Tomography (XCT) and energy-dispersive X-ray spectroscopy (EDS). To capture the differences in morphology of the Fe-IMCs in the commercial and HRC alloys, we introduce a new 3D morphological descriptor—the particle-to-convex hull volume ratio (p/h). Finally, the effect of homogenization on the Fe-IMCs morphology was tracked using p/h, and a comprehensive analysis of the Fe-IMCs’ compositional and morphological evolution was presented.

This paper reviews the progress in solidification processing of metal matrix composites (MMCs) during the last 60 years. The need for a combination of lightweight, improved mechanical and physical properties has driven interest in these materials for use in transportation-related applications. These composites, incorporating various reinforcements, including oxides, carbides, graphite, graphene, and hybrid composites in the matrix of Aluminum alloy, are discussed along with various solidification processing methods. Limited discussion on issues specific to solidification in composites, including the effect of reinforcements on nucleation, growth, fluidity, microsegregation, and interactions between reinforcements and growing solid–liquid interfaces, have been included. The current and potential future uses of metal matrix composites in transportation applications, including electric vehicles, are presented. Challenges for future growth of the use of metal matrix composites in transportation are briefly discussed.

Squeeze casting is an advanced manufacturing process for aluminum and magnesium alloys, which produces high integrity and heat-treatable cast components. The physics involved in squeeze casting is pressurized solidification and it is important to understand the fundamental knowledge of pressurized solidification and to develop numerical models both at macro- and micro-scales. This review presents the major challenge and novel research dedicated to macro- and micro-modeling on squeeze casting of aluminum and magnesium alloys, including metal displacement and free surface tracking, thermal–mechanical coupled simulation, casting–mold interfacial heat transfer model, shrinkage defect and macrosegregation prediction, pressurized solidification and microstructure modeling, through-process modeling, etc. Finally, the prospects of the macro- and micro-modeling on squeeze casting process are presented.

Many low-density metals are also reactive. This article draws inspiration from the passivation oxide layer formed on aluminum to the design of electrochemically stable surface layers on lithium metal electrodes in batteries. First, reactive molecular dynamics simulations are used to compare the oxide layer formation on lithium and aluminum metal surfaces. While a uniform dense aluminum oxide layer forms on aluminum, vertical cracks in the lithium oxide layer lead to a deformed lithium oxide layer. These observations are consistent with the empirical Pilling–Bedworth Ratio (PBR) that uses the molar volume ratio of oxide to metal to determine whether a metal is likely to passivate in dry air by creating a protective oxide layer. A passivation layer needs to form on the lithium metal surface in the presence of electrolytes. The PBR concept is thus extended to the multiple compounds found in the spontaneously formed solid electrolyte interphase (SEI). It is suggested that a mixture of LiF/Li 2 CO 3 or LiF/Li 2 O or replacing Li 2 O with Li 2 S can effectively create a PBR that is in the 1 to 1.3 range for better passivation. While these analyses are consistent with some experimental evidence, a seeding layer concept is proposed to further prevent dendrite growth and simplify the battery manufacturing process. The role of metallic nanoparticles in the metal–carbon nanocomposite seeding layer to control lithium nucleation and growth is investigated by an atomically informed phase field model (AI-PFM). The model predicts the formation of a Li-rich phase with Ag nanoparticles but non-uniform lithium metal nucleation on Au nanoparticles, showing the AI-PFM model to be a desired design tool to evaluate which metallic nanoparticles can be used to control the Li deposition morphology. These results collectively emphasize the need for highly coupled electrochemical–mechanical modeling to solve the challenges of designing a multifunctional passivation layer for metal electrodes in batteries.

Binder Jet Printing (BJP) has emerged as a viable sinter-based manufacturing method suitable for producing parts spanning a range of sizes. However, the current utilization of this technology is encumbered by the materials available. Notably, titanium is a highly desirable material for many applications owing to its exceptional specific strength, high ductility, resistance to corrosion, and biocompatibility. Regrettably, the production of titanium parts at scale through BJP remains beset by a multitude of challenges. A substantial portion of these impediments arise from the coarse microstructure commonly encountered in powder metallurgy (PM) titanium components, which hinders their competitiveness with wrought counterparts. In light of these challenges, this study explores the combination of the Hydrogen Sintering and Phase Transformation (HSPT) process with BJP, offering a promising avenue for cost-effective manufacturing of titanium components possessing strength and ductility on par with conventionally wrought counterparts. This research presents a comprehensive analysis of the synergistic effects of combining HSPT with BJP, augmented by hot isostatic pressing (HIP) when needed. This investigation aims to investigate in detail the microstructure and mechanical properties of BJP Ti-6Al-4V alloy sintered using the HSPT process. By elucidating the mechanisms and benefits of HSPT and HIP for BJP Ti-6Al-4V, this study aims to contribute valuable insights into advancing the state of the art in additive manufacturing for titanium.

The plateau pika (Ochotona curzoniae) is a keystone species on the Qinghai–Tibet Plateau, and its population density—typically inferred from burrow counts—requires rapid, low-cost monitoring. We propose YOLO-Pika, a lightweight detector built on YOLOv8n that integrates (1) a Fusion_(B)lock into the backbone, leveraging high-dimensional mapping and fine-grained gating to enhance feature representation with negligible computational overhead, and (2) an MS_(F)usion_(F)PN composed of multiple MSEI modules for multi-scale frequency-domain fusion and edge enhancement. On a plateau pika burrow dataset, YOLO-Pika increases mAP50 by 3.4 points and mAP50–95 by 5.0 points while reducing parameters by 22.7% and FLOPs by 0.01%; AP improves for small, medium, and large targets. On a public Brandt’s vole hole dataset, it achieves a further 4.9-point gain in mAP50 and reduces false detections from localization errors, redundancy, and background noise by 30–50%. Compared with five state-of-the-art lightweight detectors (including YOLOv10), YOLO-Pika attains the highest detection accuracy with the fewest parameters. These results show that YOLO-Pika balances real-time performance, detection precision, and deployment feasibility, offering a practical, scalable solution for rodent burrow detection and alpine grassland damage assessment with strong cross-regional generalization.