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Talc is widely used in the automotive industry as a primary reinforcement in part manufacturing. However, its relatively high density, low impact strength, and difficulty in recycling pose challenges. The present study investigates the utilization of graphene grown on talc derived from waste polypropylene (PP) and graphene nanoplatelet obtained from waste tires by recycling and upcycling processes to be used as reinforcing agents in thermoplastic processing by keeping mechanical integrity and part performance properties the same compared to the part used in serial production. The whole value chain was examined by developing compound formulation, scaling up with a high shear mixer, and injection molding of the selected exterior part of the glass front grill with its part performance tests with the real‐case thermal and structural finite element analysis. Regarding the mechanical performance analysis, the developed compound formulation having 9 wt% micron scale talc with graphene nanoparticles and hybrid additive yielded substantial enhancements of 32% and 22% in flexural and tensile strength, respectively, in the homopolymer polypropylene (HomoPP) matrix by achieving 10% weight reduction. With a systematic life cycle assessment study, CO2 reduction was provided by 55% for the selected part by replacing virgin HomoPP with mechanically recycled waste PP originally unknown. [ABSTRACT FROM AUTHOR]

With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively. [ABSTRACT FROM AUTHOR]

With the rapid expansion of the automotive industry and the continuous growth of vehicle fleets, traffic safety has become a critical global social issue. Developing detection and alert systems for fatigue and distracted driving is essential for enhancing traffic safety. Factors, such as variations in the driver’s facial details, lighting conditions, and camera pixel quality, significantly affect the accuracy of fatigue and distracted driving detection, often resulting in the low effectiveness of existing methods. This study introduces a new network designed to detect fatigue and distracted driving amidst the complex backgrounds typical within vehicles. To extract driver and facial information as well as gradient details more efficiently, we introduce the Multihead Difference Kernel Convolution Module (MDKC) and Multiscale Large Convolutional Fusion Module (MLCF) in baseline. This incorporates a blend of Multihead Mixed Convolution and Large and Small Convolutional Kernels to amplify the spatial intricacies of the backbone. To extract gradient details from different illumination and noise feature maps, we enhance the network’s neck by introducing the Adaptive Convolutional Attention Module (ACAM) in NECK, optimizing feature retention. Extensive comparative experiments validate the efficacy of our network, showcasing superior performance not only on the Fatigue and Distracted Driving Dataset but also competitive results on the public COCO dataset. Source code is available at . [ABSTRACT FROM AUTHOR]

The imperative for lightweighting technologies, paramount in mission-critical cyber-physical systems (CPSs) including aerospace, automotive and allied sectors, hinges upon optimizing energy efficiency and curbing product weight. Honeycomb structures, celebrated for their exceptional strength-to-weight ratio, have indisputably guided the pursuit of lightweight design. This paper expounds upon the versatility of honeycomb structures by scrutinizing their in-plane mechanical attributes. Leveraging finite element simulations and polynomial fitting, we enhance the prevailing equivalent elastic modulus model for uniform honeycomb structures, expanding its domain to encompass a broader spectrum of relative density values. Deliberations ensue concerning the model's constraints and its inapplicability to nonuniform honeycomb structures. The investigation introduces nodes as pivotal influencers in the mechanical comportment of nonuniform honeycomb structures, delineating the nexus between the equivalent elastic modulus and node dimensions through a fusion of finite element simulations and mechanical experimentation. Furthermore, this research delves into the tenets and constructs of density-based variable density methodologies within the ambit of topology optimization, with an overarching goal of maximizing stiffness. We furnish a holistic design protocol for optimizing honeycomb structures, underscored by a pragmatic instantiation of the density-based variable density approach. Scrutinizing the geometric interplay between honeycomb structures and design spaces, we posit an innovative paradigm employing concentric circles to approximate cellular envelopes, streamlining numerical cartography and the conversion of optimization outputs into variable density honeycomb configurations. Evaluation of the in-plane mechanical attributes of variable density honeycomb structures reveals that TPU material augments the resilience of both uniform and variable density honeycomb structures, whereas topology optimization amplifies specific stiffness and resilience modulus in variable density honeycomb structures relative to their uniform counterparts. This study sheds light on the complexities of honeycomb structures, providing valuable insights for their optimization in lightweight applications. [ABSTRACT FROM AUTHOR]

The pursuit of lightweight, environmentally friendly composite structures in transportation is crucial for minimizing ecological footprints and promoting energy-efficient manufacturing techniques. This study presents a novel approach by replacing traditional long glass fiber reinforced homopolymer polypropylene (homoPP) compounds with short hemp fiber reinforced homoPP, incorporating graphene nanoplatelets (GNP) derived from recovered carbon black via waste tire pyrolysis, resulting in a remarkable 15% weight reduction. With new compound formulation by adjusting the amounts of compatibilizer and GNP, injection moulding process was integrated with overmoulding process by using bio-based UD prepregs to enhance the adhesion of injected part and interfacial interaction by decreasing the stress concentrations in the structure. This novel hybrid composite design having 40 wt% hemp fiber, 1.0 wt% GNP and 2.7 wt% compatibilizer provided to improve flexural modulus and strength by 169% and 67.9%, respectively, compared to neat homoPP. The overmolding process employed bio-based natural fibers reinforced UD tapes as inserts, leading to an impressive enhancement of 211% in tensile modulus and 93.6% in strength, further surpassing the performance of neat homoPP. This work not only achieves the conversion of conventional composite structures into recyclable, sustainable thermoplastic composites but also introduces multi-scale reinforcements with customizable functionality, demonstrating a significant step forward in the development of environmentally conscious materials and manufacturing methods by adopting Life-Cycle Assessment (LCA) methodology regarding the sustainability of the newly developed composites. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management. [ABSTRACT FROM AUTHOR]

Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection. [ABSTRACT FROM AUTHOR]

The paper focuses on the research of hybrid aluminium castings produced by overcasting technology. This is an advanced technology for ensuring the lightness of castings by using the principle of overcasting a core with a porous cellular structure produced by foaming. Process parameters in the foaming phase of the material have a great influence on the resulting porous structure. The article focuses on controlling the influence of pressure during the foaming process on the resulting porosity and evaluating by X-ray tomograph and measuring the relative density. Variants using an initial pressure of 0.3 MPa appear to be the most satisfactory. The challenge of this technology is to ensure adequate bonding of the metals at the interface between the porous core and the solidified metal without penetrating the coating layer. For this reason, the surface treatment of foamed cores with various etchants has been proposed to disrupt the oxide layer on their surface. Macrographs of the uncoated sample and samples etched with 0.5% HF and 10% H3PO4 demonstrated the need for core surface treatment to prevent liquid metal penetration. EDX analysis confirmed the presence of AlPO4 at the core/casting interface in the treated sample.

In the global pursuit of energy conservation, reduction in consumption and emissions, as well as lightweighting, 5xxx-series aluminum alloys have emerged as a promising lightweight and high-strength material with a wide range of applications in aerospace, transportation, building structures, medical devices, and more. However, it is worth noting that 5XXX-series aluminum alloys belong to a category of non-heat-treatable reinforced aluminum alloys with limited means of enhancing their properties through heat treatment. Consequently, the development of high-performance 5XXX-series aluminum alloys poses a significant challenge. This paper provides an overview of the research progress on 5xxx-series aluminum alloys while discussing the current state of research regarding the influence of microalloying elements on their microstructure and properties. Furthermore, it elucidates the emerging trends in synergistic regulation between deformation processes and heat treatment processes for enhancing organizational refinement and improving alloy properties. Additionally, this study analyzes the reinforcement mechanism responsible for the exceptional properties exhibited by 5xxx-series aluminum alloys. Lastly, it discusses the exceptional properties exhibited by 5xxx-series alloys along with their primary areas of application. The findings presented herein can serve as a valuable reference for future advancements in advanced high-performance 5xxx-series aluminum alloys.

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the mAP@0.5 by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness. [ABSTRACT FROM AUTHOR]

Relying on the research and development project of a specific company, the present study conducts a topology optimisation design for an aluminium alloy frame intended for a light-duty truck. In light of the discrepancy observed in the conventional strength calculation outcomes for the unconstrained frame, the inertia release method is employed to determine the strength of the said frame. A novel approach is presented to address the issue of multi-condition frame topology optimisation. The proposed method incorporates weighting coefficients to facilitate the optimisation process. The weight coefficients for each working condition are determined using the exponential scale hierarchical analysis method. Subsequently, an objective function is formulated for the multi-objective topology optimisation of an aluminium alloy frame. The proposed method aims to optimise the topology of an aluminium alloy frame by considering the constraints of volume fraction and the objective of minimising flexibility under various working conditions. The findings indicate that the optimised aluminium alloy frame exhibits an 18.9% increase in bending stiffness and a 150.3% increase in torsional stiffness. The safety coefficient of the frame strength exceeds 1 under all operational circumstances. The frame has a mass of 157 kg, which is reduced by 30.8%. The performance of the optimised frame is significantly enhanced, resulting in a noticeable lightweight effect. [ABSTRACT FROM AUTHOR]

Safflower is one of the most important cash crops in China. Its production area is concentrated in Xinjiang, Gansu and Ningxia. However, the harvesting of safflower relies mainly on manual labour at present. Particularly, the operating environment is easily affected by weather factors. Fortunately, intelligent harvesting can be expected to improve the efficiency of safflower harvesting with labour cost savings. Previous research often focused on pneumatic, pulling, combing, and cutting harvesting. However, it is still required for manual work during harvesting. The autonomous operation can be realized by combining target detection and navigation in the harvesting robots. However, the complex working environment in the field has limited the accurate recognition and localization in the harvesting process. This study aims to promote the performance of safflower recognition under the complex environment in the field during intelligent harvesting. A lightweight safflower recognition was also proposed using an improved YOLOv8n. The computational resources of the device were then deployed to the model on the mobile for detection. A dataset of 2309 images was created to categorize into two classes: picked and no picked. The safflower blooming was categorized into four stages, namely the bud, first flowering, prime bloom, and septum stage. The prime bloom stage was the best picking time in the most economically beneficial period of safflower. Therefore, the safflower only in the prime bloom stage was picked rather than the bud, first flowering, and septum stage. The improvement procedures were as follows. Firstly, the Vanillanet lightweight network structure was applied to substitute for the Backbone of YOLOv8n, in order to reduce the complex structure of the model. Secondly, the large separable kernel attention (LSKA) module was introduced into the Neck, in order to reduce the amount of storage and computational resource consumption. Thirdly, The YOLOv8n's loss function was revised from the center intersection of union (CIoU) to the wise intersection of union (WIoU), in order to improve the overall performance of the detector. Finally, the stochastic gradient descent (SGD) was chosen to train the model for robustness. The experimental results showed that the frames per second (FPS) of the improved lightweight model increased by 7.41%, while the weight file was only 50.17% of the original one. The precision (P) and the mean average precision (mAP) values reached 93.10% and 96.40%, respectively. Furthermore, the FPS was improved by 25.93% and 19.76%, the weight file was reduced by 21.90% and 25.86%, respectively, compared with the YOLOv5s and YOLOv7-tiny models. Meanwhile, better robustness was achieved in the improved model. The Jetson Orin NX flatform was then selected to deploy for testing. The single-image detection time of YOLOv8n and YOLOv8n-VLWS was 0.38s and 0.27s, which was 28.95% shorter than the original model. The high precision and lightweight of real-time detection was realized for the safflower in the field. The findings can provide the technical support to develop intelligent harvesting equipment for safflower. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive dynamic finite element analysis (FEA) of a cryogenic fuel tank made from an innovative aluminium/lithium–graphene nano-composite material, assessing its suitability for aerospace launch vehicles carrying cryogenic hydrogen and oxygen. The study focuses on the effects of lightweighting, utilizing 0.5 wt.% reinforced graphene in the Al 2195 matrix, a material poised to revolutionize the aerospace industry. Objectives include developing a digital twin of the fuel tank, CAD modeling to aerospace standards, and conducting ANSYS simulations under launch conditions to evaluate stress, strain, and deformation. Numerical results reveal a significant weight reduction of approximately 19,420 kg and a notable maximum stress reduction of 1.3% compared to traditional Al 2195 alloy tanks. The novelty of this research lies in its pioneering analysis of aluminium/lithium–graphene composites for lightweighting in cryogenic fuel tanks under space launch conditions. Conclusions affirm the composite's viability, advocating for the development of lighter yet robust aerospace structures and fostering innovation in spacecraft design and materials science. [ABSTRACT FROM AUTHOR]

The airport waiting chair frames, as an important part of the overall seating, must be designed to provide comfort, safety, and aesthetic appeal. While the airport furniture industry has made progress in terms of sustainability, more efforts are needed to improve material selection, manufacturing processes, and supply chain management to support the development of sustainable furniture. This study proposes innovative ideas for the lightweight design of the frame, based on the limitations of the existing design. Firstly, structural innovations are discussed, non-traditional mesh panels and curved rounded designs are discussed, and non-introduced mesh panels and curved designs are used to enhance the strength and stability of airport waiting chairs and enhance their overall performance. Secondly, innovations in lightweighting have focused on adjusting the thickness dimensions to enhance comfort, material utilization, and sustainability as well as to achieve a lightweight and thin appearance effect. In order to determine the optimal ranges of values for the thickness of the seat surface support strip (P5), the thickness of the backrest strip (P3), and the thickness of the seat panel (P1), nine groups of chairs with different frame sizes were tested using an orthogonal experimental method. Based on the experimental results for size and topology optimization, NX2312 software modeling will be imported into ANSYS Workbench for static analysis. Using the optimized results, the use of 2.842 kg of steel was successfully reduced by 34.8% to ensure the seat's stability. This provides a reference and idea for the digital and standardized innovative design of airport waiting chair furniture structure in the future. Through digital design and lightweight optimization, material savings and effective use of resources can be achieved, promoting the goal of sustainable development. [ABSTRACT FROM AUTHOR]

A theoretical model of the normal sound insulation of a rectangular locally resonant (LR) plate with simply supported boundary conditions is established. The accuracy of the model is verified through comparison with finite element simulation results. Considering a 1-m2 aluminum plate as the research object, a non-dominated genetic algorithm is introduced. The algorithm optimizes the lightweight sound insulation of an LR finite plate with a center frequency band of 20–800 Hz as the optimization objective of maximum average sound insulation and minimum mass. The optimization parameters are substrate thickness and local resonance parameters (including resonator target frequency (f obj), number of resonators, additional mass ratio, and resonator damping). Optimization results indicate that the average sound insulation fluctuates at approximately 2 dB due to changes in f obj. The results also provide an optimal solution to the algorithm. The optimal sound insulation value curve of LR plates corresponding to different surface densities of intermediate improved cases is fitted and compared with the sound insulation value of an equal-mass bare plate. Under lightweight optimization, the resonators are deemed capable of improving the local frequency band sound insulation. In contrast, the wideband average sound insulation can approximate but not surpass the insulation provided by the equal-mass bare plate. In practical applications, an LR plate that can improve the local frequency band sound insulation and ensure the overall lightweight sound insulation performance of the wide frequency band can be obtained. This can be achieved by analyzing the frequency spectrum of optimal points in the lightweight sound insulation optimization process. Relevant research can provide a basis for the evaluation and design of the low-frequency sound insulation of plates for transportation equipment, such as airplanes, high-speed trains, and cars. [ABSTRACT FROM AUTHOR]

Fast and accurate counting and positioning of flowers is the foundation of automated flower cultivation production. However, it remains a challenge to complete the counting and positioning of high-density flowers against a complex background. Therefore, this paper proposes a lightweight flower counting and positioning model, Light-FC-YOLO, based on YOLOv8s. By integrating lightweight convolution, the model is more portable and deployable. At the same time, a new efficient detection head, Efficient head, and the integration of the LSKA large kernel attention mechanism are proposed to enhance the model's feature detail extraction capability and change the weight ratio of the shallow edge and key point information in the network. Finally, the SIoU loss function with target angle deviation calculation is introduced to improve the model's detection accuracy and target positioning ability. Experimental results show that Light-FC-YOLO, with a model size reduction of 27.2% and a parameter reduction of 39.0%, has a Mean Average Precision (mAP) and recall that are 0.8% and 1.4% higher than YOLOv8s, respectively. In the counting comparison experiment, the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of Light-FC-YOLO reached 0.9577 and 8.69, respectively, both superior to lightweight models such as YOLOv8s. The lightweight flower detection method proposed in this paper can efficiently complete flower positioning and counting tasks, providing technical support and reference solutions for automated flower production management. [ABSTRACT FROM AUTHOR]

The extensive use of carbon fiber-reinforced composites and aluminum alloys represents the highest level of automotive body-in-white lightweighting. The effective and secure joining of these heterogeneous materials remains a prominent and actively researched topic within the scientific community. Among various joining techniques, clinching has emerged as a particularly cost-effective solution, experiencing significant advancements. However, the application of clinching is severely limited by the properties of the joining materials. In this work, various clinching processes for the joining of composites and aluminum alloys reported in recent research are described in detail according to three broad categories based on the principle of technological improvement. By scrutinizing current clinching technologies, a forward-looking perspective is presented for the future evolution of clinching technology in terms of composite–aluminum joints, encompassing aspects of tool design, process analysis, and the enhancement of joint quality. This work provides an overview of current research on clinching of CFRP and aluminum and serves as a reference for the further development of clinching processes. [ABSTRACT FROM AUTHOR]

As a promising solution to the growing demand for lightweighting, hot stamping has gained considerable applications in the automotive industry. Over the past few decades, the market for hot stamping has experienced explosive growth, with ongoing advancements offering potential for further expansion of its applications. This paper provides a historical overview of hot stamping alongside an in-depth analysis of future trends. Scientific publications, patents and industrial applications of hot stamping are systematically reviewed, with major developments in materials, processes, tools, and other relevant aspects being highlighted. Through data analysis, the current state of hot stamping is comprehensively depicted, and the trends in the development of hot stamping are revealed. Additionally, the future of extending hot stamping technologies to a broader range of materials is discussed, with suggestions furnished from both academic and industrial perspectives.

The rapid advancement of intelligent assisted driving technology has significantly enhanced transportation convenience in society and contributed to the mitigation of traffic safety hazards. Addressing the potential for drivers to experience abnormal physical conditions during the driving process, an enhanced lightweight network model based on YOLOv5 for detecting abnormal facial expressions of drivers is proposed in this paper. Initially, the lightweighting of the YOLOv5 backbone network is achieved by integrating the FasterNet Block, a lightweight module from the FasterNet network, with the C3 module in the main network. This combination forms the C3-faster module. Subsequently, the original convolutional modules in the YOLOv5 model are replaced with the improved GSConvns module to reduce computational load. Building upon the GSConvns module, the VoV-GSCSP module is constructed to ensure the lightweighting of the neck network while maintaining detection accuracy. Finally, channel pruning and fine-tuning operations are applied to the entire model. Channel pruning involves removing channels with minimal impact on output results, further reducing the model's computational load, parameters, and size. The fine-tuning operation compensates for any potential loss in detection accuracy. Experimental results demonstrate that the proposed model achieves a substantial reduction in both parameter count and computational load while maintaining a high detection accuracy of 84.5%. The improved model has a compact size of only 4.6 MB, making it more conducive to the efficient operation of onboard computers. [ABSTRACT FROM AUTHOR]

With the vigorous implementation of national policies and regulations such as energy-saving and emission reduction, lightweighting has become one of the trends in the development of the automotive industry, which is of great significance in reducing energy consumption and increasing the range of vehicles. This paper is based on a newly developed smartness car body designed for elderly users, which aims to solve the problem of mobility and companionship for the elderly, with a maximum speed of 20 km/h and a weight requirement of 45 kg or less. This paper focuses on the lightweight technology of the body of the smartness car, using carbon fibre composite material as the carrier, and using simulation analysis combined with experimental testing to systematically study the body material selection, structure and process, and to carry out force analysis, finite element analysis and modal analysis on the designed body, verifying that the body of the smartness car meets the lightweight design requirements. [ABSTRACT FROM AUTHOR]

In the field of defect detection in sewers, some researches focus on high accuracy. However, it is challenging for portable on-site devices to provide high performance. This paper proposes a lightweight sewer defect detection model, You Only Look Once (YOLO) v5-Sewer. Firstly, the backbone network of YOLOv5s is replaced with a stacked MobileNetV3 block. Secondly, the C3 module of the neck of YOLOv5s is improved with a C3-Faster module. Thirdly, to compensate for the accuracy loss due to the lightweight network, a channel attention (CA) and convolutional block attention module (CBAM) are added to the proposed method. Finally, the Efficient Intersection over Union (EIOU) is adopted as the localization loss function. Experimental validation on the dataset shows that YOLOv5-Sewer achieves a 1.5% reduction in mean Average Precision (mAP) while reducing floating-point operations by 68%, the number of parameters by 55%, and the model size by 54%, compared to the YOLOv5s model. The detection speed reaches 112 frames per second (FPS) with the GPU (RTX 3070Ti). This model successfully implements a lightweight design while maintaining the detection accuracy, enhancing its functionality on low-performance devices. [ABSTRACT FROM AUTHOR]

Copyright of Cyber Security & Data Governance is the property of Editorial Office of Information Technology & Network Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Cast Al-Si alloys, recognized for their excellent mechanical properties, constitute one of the most widely employed non-ferrous substrates in several sectors, and are particularly relevant in the transport industry. Nevertheless, these alloys also display inherent limitations that significantly restrict their use in several applications. Among these limitations, their low hardness, low wear resistance, or limited anti-corrosion properties, which are often not enough when the component is subjected to more severe environments, are particularly relevant. In this context, surface modification and the development of coatings are essential for the application of cast Al-Si alloys. This review focuses on the development of coatings to overcome the complexities associated with improving the performance of cast Al-Si alloys. Against this background, plasma electrolytic oxidation (PEO), an advanced electrochemical treatment that has revolutionized the surface modification of several metallic alloys in recent years, emerges as a promising approach. Despite the growing recognition of PEO technology, the achievement of high-performance coatings on cast Al-Si is still a challenge nowadays, for which reason this review aims to provide an overview of the PEO treatment applied to these alloys. In particular, the impact of the electrolyte chemical composition on the properties of the coatings obtained on different alloys exposed to harsh environments has been analyzed and discussed. By addressing the existing gaps and challenges, this paper contributes to a better understanding of the intricacies associated with the development of robust PEO coatings on cast Al-Si alloys. [ABSTRACT FROM AUTHOR]

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ABSTRACT As the application scenarios of embedded devices become increasingly extensive, the use of high-performance convolutional neural networks can solve the problem of low accuracy of multiple defects detection in apples. However, owing to the overly large parameters and network structure of the convolutional neural network, perfectly integrating it with the embedded devices is difficult. Therefore, this study proposes a lightweight and improved algorithm based on Yolov5s. First, the structure of the optimized MobileNetV3 is introduced in the backbone layer to reduce the computational and parametric quantities of the model. Wise-IoU is used as the loss function of the localization regression of the bounding box to reduce the harm of low-quality samples on anchor box regression. The efficient multiscale attention mechanism is embedded in each downsampling layer of the backbone, and small target detection is added to the neck layer to improve the attention of the convolutional layer on important features. The experimental results showed that the Yolov5s-Super model parametric count decreased by 78%, and accuracy P, mAP@50, and mAP@50:95 improved by 10.3%, 3.2%, and 4.2%, respectively, compared to the original model. Theoretical support is provided for the migration of this network model to embedded devices.

Multiscale Dense Network (MSDN) is a deep convolutional neural network method proposed to solve the loss of fine features in hyperspectral remote sensing images. Multiscale Dense Network (MSDN), which makes full use of the different scale information in the network, implements feature extraction of HSI in both horizontal and vertical dimensions. Since MSDN maintains all fine scales until the last layer of the network, the computational effort is large. To address this problem, this paper proposes a lightweight Inter layer Short Multiscale Dense Network (IMSDN) method for hyperspectral image classification. HSI classification is performed using network reduction and inert computation to avoid unnecessary computation, the MSDN network is divided into S blocks along the horizontal dimension, keeping the coarsest (S-i+1) scale only in the ith block and only down sampling the final output of each block in the Short Multiscale Dense Network (SMSDN) for HSI classification. On the basis of the SMSDN network, in order to maintain the classification accuracy of the network, the Lightweight Interlayer Multiscale Dense Network (IMSDN) utilizes three scales of convolution kernels in different horizontal directions to extract the image features separately, which are then fused for the classification of HSI. Experiments are conducted on two publicly available datasets and the results are compared with other methods to demonstrate that the IMSDN network has less computation and shortens the experiment time compared to the MSDN network while guaranteeing the classification accuracy.

Seeds are the most basic and important means of production for agriculture. During the production and processing of seeds, they may undergo potential mechanical damages and mildew alterations, which might jeopardize their germination viability. Hence, checking the quality of seeds before sowing is of paramount importance for the benefit of the sower and the safety of agricultural production. In order to achieve an efficient detection of maize seed quality, our experiment assembled a dataset composed of 2,128 seeds with four different health statuses of maize: healthy, broken, moth-eaten, and mildewed. In this paper, we proposed a lightweight maize seed quality detection model for small objects based on improved YOLOv8: I-YOLOv8. Firstly, we introduced a multi-scale attention mechanism called EMA to efficiently retain information across channels and reduce computational load. Next, we chosen the SPD-Conv module for low-resolution images and small objects, and applied it to the backbone, which addressed the loss of fine-grained information and the less efficient learning of feature representations present in YOLOv8. Lastly, we reduced the large detection layer, which directed the network to pay more attention to the location, channel, and dimensional information of smaller objects, and we also replaced the loss function with WIoUv3. We validated our model using ablation studies and compared it with YOLOv5, YOLOv6, and YOLOv8. The mAP (Mean Average Precision) of the improved model I_YOLOv8 reaches 98.5%, which is 6.7% higher than YOLOv8. The average recognition time per image was 163.9fps, a boost of 5.2fps compared to YOLOv8. This study lays a theoretical foundation for the efficient, convenient, and rapid detection of maize quality, while also offering a technical basis for advancing automated maize quality detection means.

As the lightest structural metal and one of the most abundant metallic elements on earth, magnesium (Mg) has been used as an “industrial metal” for lightweighting in the transportation and electronics industries, in addition to other traditional applications in aluminum alloying, steel desulfurization and protective anodes. In recent years, research has shown significant potential for Mg to become a “technology metal” in a variety of new applications from energy storage/battery to biomedical products. However, global Mg production has shown steady but moderate growth in the last three decades. Mg applications as an industry metal are still limited due to some sustainability concerns of primary Mg production, as well as a number of technical issues related to the structural and corrosion performance of commercial Mg alloys. New Mg applications as an industrial or technology metal face tremendous technical challenges, which have been reflected in the intensified global research efforts in the last twenty years. This paper will review some past and present applications, and discuss future opportunities and challenges for Mg research and applications for the global Mg community.

With the progress of social life, the aging of building facilities has become an inevitable phenomenon. The efficiency of manual crack detection is limited, so it is necessary to explore intelligent detection technology. This article proposes a novel crack detection method TF-MobileNet. We took into account the effect of lightweight and crack feature extraction, so we developed a novel crack feature extraction backbone network, which combined Transformer and MobileNetV3. Then we improved the feature fusion network by using the multi-headed attention mechanism of the Bottleneck Transformer, which enables the feature fusion effect to be improved. Then, we integrated SENet and SimAM attention mechanisms into the networks used for feature extraction and feature fusion, thereby further improving the crack detection performance. Finally, we deployed our model in edge devices (NVIDIA Jeston Nano). The findings indicate that our proposed model has achieved 90.8% mAP on the dataset and worked well on the edge device side, which meet the requirements of automatic crack detection. Our model enables real-time monitoring of pavement using edge devices. This approach allows for timely maintenance and repair of the pavement. In the future, we can train the model to recognize more pavement distress features, addressing road safety issues effectively.

With the continuous development and construction of new power systems, using drones to inspect the condition of transmission line insulators has become an inevitable trend. To facilitate the deployment of drone hardware equipment, this paper proposes IDD-YOLO (Insulator Defect Detection-YOLO), a lightweight insulator defect detection model. Initially, the backbone network of IDD-YOLO employs GhostNet for feature extraction. However, due to the limited feature extraction capability of GhostNet, we designed a lightweight attention mechanism called LCSA (Lightweight Channel-Spatial Attention), which is combined with GhostNet to capture features more comprehensively. Secondly, the neck network of IDD-YOLO utilizes PANet for feature transformation and introduces GSConv and C3Ghost convolution modules to reduce redundant parameters and lighten the network. The head network employs the YOLO detection head, incorporating the EIOU loss function and Mish activation function to optimize the speed and accuracy of insulator defect detection. Finally, the model is optimized using TensorRT and deployed on the NVIDIA Jetson TX2 NX mobile platform to test the actual inference speed of the model. The experimental results demonstrate that the model exhibits outstanding performance on both the proprietary ID-2024 insulator defect dataset and the public SFID insulator dataset. After optimization with TensorRT, the actual inference speed of the IDD-YOLO model reached 20.83 frames per second (FPS), meeting the demands for accurate and real-time inspection of insulator defects by drones.

Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection.

PCB board defect detection is a necessary part of the PCB manufacturing process and needs to be repeated several times to ensure the quality of the PCB board. However, existing PCB surface defect detection methods suffer from high computational effort, low robustness and slow speed. To this end, this paper proposes the ShuffleNetV2-YOLOv5 model, a fast PCB surface defect detection method based on an attention mechanism and multi-source information fusion. The model uses a modified ShuffleNetV2 as the backbone network and incorporates an attention mechanism in multiple stages of the backbone network to improve the model's focus on valid information at different depths. Parameters for each stage of the Efficient NAS are introduced to limit the effect of degrees of freedom on the robustness of the network and to effectively reduce the number of parameters of the model. In the feature fusion section, an enhanced feature fusion structure, S3Head structure, is proposed to incorporate information from the sampled part of each stage into the feature fusion, enriching the information source for feature fusion.In addition, a CBAM attention mechanism is introduced at each sampling output stage to enhance the contextual information interaction capability. A migration learning algorithm is employed in the training process to further improve the training of the model. Experimental results show that the ShuffleNetV2-YOLOv5 model has higher accuracy and lower floating point operations than several lightweight models such as YOLOv3tiny and YOLOv4tiny. To further improve the human-computer interaction experience, an upper computer interface was designed using PyQT. [ABSTRACT FROM AUTHOR]

Distracted behavior detection is an important task in computer-assisted driving. Although deep learning has made significant progress in this area, it is still difficult to meet the requirements of the real-time analysis and processing of massive data by relying solely on local computing power. To overcome these problems, this paper proposes a driving distraction detection method based on cloud–fog computing architecture, which introduces scalable modules and a model-driven optimization based on greedy pruning. Specifically, the proposed method makes full use of cloud–fog computing to process complex driving scene data, solves the problem of local computing resource limitations, and achieves the goal of detecting distracted driving behavior in real time. In terms of feature extraction, scalable modules are used to adapt to different levels of feature extraction to effectively capture the diversity of driving behaviors. Additionally, in order to improve the performance of the model, a model-driven optimization method based on greedy pruning is introduced to optimize the model structure to obtain a lighter and more efficient model. Through verification experiments on multiple driving scene datasets such as LDDB and Statefarm, the effectiveness of the proposed driving distraction detection method is proved. [ABSTRACT FROM AUTHOR]

• Non/low-carbon production of primary Mg. • Increased end-of-life recycling of Mg components. • Corrosion-resistant and "stainless" Mg alloys. • Flame-resistant Mg alloy development. • Alloy development for high thermal and electric conductivity. • Mega-casting development. • Room temperature sheet applications. • Additive manufacturing. • Mg-ion battery development. • Hydrogen storage. As the lightest structural metal and one of the most abundant metallic elements on earth, magnesium (Mg) has been used as an "industrial metal" for lightweighting in the transportation and electronics industries, in addition to other traditional applications in aluminum alloying, steel desulfurization and protective anodes. In recent years, research has shown significant potential for Mg to become a "technology metal" in a variety of new applications from energy storage/battery to biomedical products. However, global Mg production has shown steady but moderate growth in the last three decades. Mg applications as an industry metal are still limited due to some sustainability concerns of primary Mg production, as well as a number of technical issues related to the structural and corrosion performance of commercial Mg alloys. New Mg applications as an industrial or technology metal face tremendous technical challenges, which have been reflected in the intensified global research efforts in the last twenty years. This paper will review some past and present applications, and discuss future opportunities and challenges for Mg research and applications for the global Mg community. [ABSTRACT FROM AUTHOR]

Intelligent wheelchair blind spot obstacle detection is an important issue for semi-enclosed special environments in elderly communities. However, the LiDAR- and 3D-point-cloud-based solutions are expensive, complex to deploy, and require significant computing resources and time. This paper proposed an improved YOLOV5 lightweight obstacle detection model, named GC-YOLO, and built an obstacle dataset that consists of incomplete target images captured in the blind spot view of the smart wheelchair. The feature extraction operations are simplified in the backbone and neck sections of GC-YOLO. The backbone network uses GhostConv in the GhostNet network to replace the ordinary convolution in the original feature extraction network, reducing the model size. Meanwhile, the CoordAttention is applied, aiming to reduce the loss of location information caused by GhostConv. Further, the neck stem section uses a combination module of the lighter SE Attention module and the GhostConv module to enhance the feature extraction capability. The experimental results show that the proposed GC-YOLO outperforms the YOLO5 in terms of model parameters, GFLOPS and F1. Compared with the YOLO5, the number of model parameters and GFLOPS are reduced by 38% and 49.7%, respectively. Additionally, the F1 of the proposed GC-YOLO is improved by 10% on the PASCAL VOC dataset. Moreover, the proposed GC-YOLO achieved mAP of 90% on the custom dataset. [ABSTRACT FROM AUTHOR]

Abstract:: Background: With the rapid development of Web3D technologies, the online Web3D visualization, particularly for complex models or scenes, has been in a great demand. Owing to the major conflict between the Web3D system load and resource consumption in the processing of these huge models, the huge 3D model lightweighting methods for online Web3D visualization are reviewed in this paper. Methods: By observing the geometry redundancy introduced by man-made operations in the modeling procedure, several categories of lightweighting related work that aim at reducing the amount of data and resource consumption are elaborated for Web3D visualization. Results: By comparing perspectives, the characteristics of each method are summarized, and among the reviewed methods, the geometric redundancy removal that achieves the lightweight goal by detecting and removing the repeated components is an appropriate method for current online Web3D visualization. Meanwhile, the learning algorithm, still in improvement period at present, is our expected future research topic. Conclusions: Various aspects should be considered in an efficient lightweight method for online Web3D visualization, such as characteristics of original data, combination or extension of existing methods, scheduling strategy, cache management, and rendering mechanism. Meanwhile, innovation methods, particularly the learning algorithm, are worth exploring.

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management.

Printed circuit board (PCB) surface defect detection is an essential part of the PCB manufacturing process. Currently, advanced CCD or CMOS sensors can capture high-resolution PCB images. However, the existing computer vision approaches for PCB surface defect detection require high computing effort, leading to insufficient efficiency. To this end, this article proposes a local and global context-enhanced lightweight CenterNet (LGCL-CenterNet) to detect PCB surface defects in real time. Specifically, we propose a two-branch lightweight vision transformer module with local and global attention, named LGT, as a complement to extract high-dimension features and leverage context-aware local enhancement after the backbone network. In the local branch, we utilize coordinate attention to aggregate more powerful features of PCB defects with different shapes. In the global branch, Bi-Level Routing Attention with pooling is used to capture long-distance pixel interactions with limited computational cost. Furthermore, a Path Aggregation Network (PANet) feature fusion structure is incorporated to mitigate the loss of shallow features caused by the increase in model depth. Then, we design a lightweight prediction head by using depthwise separable convolutions, which further compresses the computational complexity and parameters while maintaining the detection capability of the model. In the experiment, the LGCL-CenterNet increased the mAP@0.5 by 2% and 1.4%, respectively, in comparison to CenterNet-ResNet18 and YOLOv8s. Meanwhile, our approach requires fewer model parameters (0.542M) than existing techniques. The results show that the proposed method improves both detection accuracy and inference speed and indicate that the LGCL-CenterNet has better real-time performance and robustness.

This research presents a comprehensive dynamic finite element analysis (FEA) of a cryogenic fuel tank made from an innovative aluminium/lithium–graphene nano-composite material, assessing its suitability for aerospace launch vehicles carrying cryogenic hydrogen and oxygen. The study focuses on the effects of lightweighting, utilizing 0.5 wt.% reinforced graphene in the Al 2195 matrix, a material poised to revolutionize the aerospace industry. Objectives include developing a digital twin of the fuel tank, CAD modeling to aerospace standards, and conducting ANSYS simulations under launch conditions to evaluate stress, strain, and deformation. Numerical results reveal a significant weight reduction of approximately 19,420 kg and a notable maximum stress reduction of 1.3% compared to traditional Al 2195 alloy tanks. The novelty of this research lies in its pioneering analysis of aluminium/lithium–graphene composites for lightweighting in cryogenic fuel tanks under space launch conditions. Conclusions affirm the composite’s viability, advocating for the development of lighter yet robust aerospace structures and fostering innovation in spacecraft design and materials science.