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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]

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]

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]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering 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.)

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]

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]

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]

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]

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]

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. [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]

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office 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.)

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]

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]

The paper presented studies on reducing traction energy consumption with a decrease in the weight of an all-metal gondola car. Based on the proposed mathematical criterion, a new form of a blind floor was obtained, which makes it possible to reduce the weight of an all-metal gondola car. The aim of the paper was to reduce traction energy consumption with a decrease in the weight of an all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, strength studies were performed based on the finite element method. The equivalent stresses of the blind floor of an all-metal gondola car were 140.6 MPa, and the equivalent strains were 7.08 × 10−4. The margin of safety of the blind floor of an all-metal gondola car was 1.57. The weight of an all-metal gondola car with a modified form of a blind floor was reduced by 5.1% compared to a typical all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, a comparison was made of the traction energy consumption with typical all-metal gondola cars. Traction energy consumption with empty all-metal gondola cars were reduced by 2.5–3.1%; with loaded all-metal gondola cars by 2.4–7.3%, depending on the travel time interval. [ABSTRACT FROM AUTHOR]

The components’ lightweighting has been pursued, especially in the transport industry, for greenhouse gas reduction. Topology optimization, being able to allocate the material within a provided design space, is a mathematical method that can support the design of lightweight components, preserving, at the same time, their mechanical performances. In this paper, a standard shape of a component, specifically an automotive bracket, was topology optimized by estimating the impacts of the new designs from an eco-friendly point of view. A subtractive, an additive and a casting manufacturing process were considered as possible manufacturing routes achieving an optimized geometry of the component for each of them. The topology optimizations were performed considering each processes’ peculiarities, introduced as constraints. Same strength for a given set of loads and boundary conditions was the target of each analysis. The component’s lightening can be considered environmentally friendly just after assessing the impacts associated with all the stages of the product’ life cycle. Indeed, each phase of the product’ life cycle can be affected, differently, by the performed topology optimization taking into account the peculiarities of the employed manufacturing process. The overall considerations on the most environmentally safe strategies can, therefore, change according to the specificities of the optimized shapes. The topology optimization showed its utmost potentiality, from a sustainable point of view, if applied to additive manufacturing techniques for the advantages arisen by the capability to manufacture complex shapes benefiting also of reduction time process owing to less material to be deposited. [ABSTRACT FROM AUTHOR]

Due to the fact that the green features of papaya skin are the same colour as the leaves, the dense growth of fruits causes serious overlapping occlusion phenomenon between them, which increases the difficulty of target detection by the robot during the picking process. This study proposes an improved YOLOv5s-Papaya deep convolutional neural network for achieving dense multitarget papaya detection in natural orchard environments. The model is based on the YOLOv5s network architecture and incorporates the Ghost module to enhance its lightweight characteristics. The Ghost module employs a strategy of grouped convolutional layers and weighted fusion, allowing for more efficient feature representation and improved model performance. A coordinate attention module is introduced to improve the accuracy of identifying dense multitarget papayas. The fusion of bidirectional weighted feature pyramid networks in the PANet structure of the feature fusion layer enhances the performance of papaya detection at different scales. Moreover, the scaled intersection over union bounding box regression loss function is used rather than the complete intersection over union bounding box regression loss function to enhance the localisation accuracy of dense targets and expedite the convergence of the network model training. Experimental results show that the YOLOv5s-Papaya model achieves detection average precision, precision, and recall rates of 92.3%, 90.4%, and 83.4%, respectively. The model's size, number of parameters, and floating-point operations are 11.5 MB, 6.2 M, and 12.8 G, respectively. Compared to the original YOLOv5s network model, the model detection average precision is improved by 3.6 percentage points, the precision is improved by 4.3 percentage points, the number of parameters is reduced by 11.4%, and the floating-point operations are decreased by 18.9%. The improved model has a lighter structure and better detection performance. This study provides the theoretical basis and technical support for intelligent picking recognition of overlapping and occluded dense papayas in natural environments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are a highly sought-after technology with numerous applications in both military and non-military uses. The identification of targets is a crucial aspect of UAV applications, but there are challenges associated with complex detection models and difficulty in detecting small targets. To address these issues, this study proposes the lightweight L-YOLO algorithm for target detection tasks from a UAV perspective. The L-YOLO algorithm improves on YOLOv5 by improving the model's detection performance for small targets while reducing the number of parameters and computational effort. The GhostNet module replaces the relevant convolution in the YOLOv5 model to create a lightweight model. The EIoU loss is used as the loss function of the algorithm to accelerate convergence and improve regression accuracy. Furthermore, feature-level extensions based on YOLOv5 are implemented, and a new detection head is proposed to improve the model's detection accuracy for small targets. The size of the anchor boxes is redesigned to suit the small targets using the K-means++ clustering algorithm. The experiments were conducted on the VisDrone-2022 dataset, and the L-YOLO algorithm demonstrated a reduction in computational effort by 42.42% and number of parameters by 48.6% compared to the original algorithm. Furthermore, recall and mAP@0.5 improved by 2.1% and 1.4%, respectively. These results demonstrate that the L-YOLO algorithm not only has better detection performance for small targets but is also a lighter model, indicating promising prospects for target detection from a UAV perspective. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer-Aided Design & Computer Graphics / Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao is the property of Gai Kan Bian Wei Hui 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 describes the design of a lightweight wing rib structure by combining topology optimisation with additive manufacturing. In addition, a deep feed-forward neural network model is proposed to perform the load prediction for the constructed wing structure incorporated with this optimised rib. The strain energy of the front rib has a minimum strain energy 1330 J for the initial volume state when the topology is not optimised for lightweighting. The relative error of the load prediction values obtained by the output layer of the deep feed-forward neural network is less than 0.02%. The absolute error of the small load prediction was less than 0.30 N. The presented results demonstrate the viability of additive manufactured rib and its implementation in global wing load prediction model for faster designs. [ABSTRACT FROM AUTHOR]

An approach to the simulation of foamed injection molded Polypropylene parts subjected to impact loading is presented in this paper. The proposed method, which considers strain-rate-dependent material properties and the possible occurrence of fracture, is, in particular, suitable for parts manufactured with core-back technology. The method was developed to be used within the functionality of a commercial Finite Element solver using a shell-type element mesh. The material model is based on a three-layer structure, with two compact skin layers and a foamed core layer made of expanded material. The properties of the foamed material are assumed as those of the compact grade scaled by a suitable factor, which is identified via inverse engineering on a set of bending tests executed on specimens having different foam densities. The fracture of the material is then predicted using a damage model which considers the effects of triaxiality. The approach is then validated on industrial parts from the automotive sector, subjected to impact in a component test. Despite the simplicity of the presented approach, which makes this method suitable for industrial applications and especially for early-stage design, the validation shows a sufficiently accurate simulation of part behavior under the impact, with a reasonable prediction of damage and fracture. [ABSTRACT FROM AUTHOR]

Rail vehicle lightweighting using fibre reinforced polymer composite materials is essential for the future of rail. This is recognised as a means of reducing carbon dioxide production through lower energy consumption, as well as reducing the impact on track degradation, thus delivering improved rail capacity and performance. This paper presents an overview of the work conducted within work package three of the NEXTGEAR project focused on the 'wheelset of the future'. Three concepts for a hybrid metallic-composite railway axle are proposed and their strengths and weaknesses are assessed. A finite element analysis on the selected concept was conducted, including a solution for the bonded joints of the metallic collars which provide the interface to the wheels and bearings. An axle mass reduction of over 63% was shown. An overview is also provided regarding the analysis of manufacturability of the axle, non-destructive methods for axle inspection/structural health monitoring and effects of impacts from ballast stones. Finally, a preliminary evaluation of the benefits arising from the reduction of the unsprung masses is provided, based on multibody simulations of vehicle dynamics. [ABSTRACT FROM AUTHOR]

Hybrid gears are composed of high-strength steel, carbon fiber reinforced plastic (CFRP), and adhesive bond to join these materials. In this study, the effect of rack tip radius (TR), drive side pressure angle (DSPA), and rim thickness (RT) on the stress of hybrid gears is investigated numerically. In addition, the stress results of hybrid gears are compared with steel gears that are used for validation of the numerical results. Single tooth stiffness (STS) values are calculated based on tooth deformations obtained from numerical analyses. The 2-DOF model is used to observe the influence of DSPA and RT on the dynamic factor (DF) and static transmission error (STE). According to the results, increasing RT has a positive effect on stress, STS, and STE, while DSPA is effective on the dynamic factor at most. [ABSTRACT FROM AUTHOR]

In this research, the design of a new competitive handbike fork, made of a composite material, is presented. The study is based both on an early finite element analysis and on a CFD analysis of the characteristics and performance of a standard fork made of aluminum, allowing to define the loading and the flux conditions and to provide a design optimization of the fork. The model was later implemented iteratively with the properties of a carbon-fiber composite material. The results obtained show that the new model allows a weight and a drag force reduction and a downforce improvement, with a stiffness and a safety coefficient comparable to the standard aluminum fork. [ABSTRACT FROM AUTHOR]

Copyright of Acta Materiae Compositae Sinica is the property of Acta Materiea Compositae Sinica Editorial Department 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.)

Copyright of Electroplating & Finishing is the property of Electroplating & Finishing Editorial Office 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.)

The use of fibre reinforced composite materials is one method by which the lightweighting of rail vehicles can be achieved. However, the issue of impact damage, amongst other challenges, limits their safety certification. This issue is accentuated by the high levels of loading a rail vehicle may be subjected to during service. This paper addresses the significance of pre-tension on large composite structures, specifically for a composite redesign of a pressure vessel for a freight tank wagon. Preloading was determined to be detrimental to the overall impact resistance of a large composite vessel. At 15.71 J of impact energy, there was a 22% increase in mean absorbed energy for a uniaxially loaded panel over an unloaded panel. However, there was only a 4% difference in penetration depth between uniaxial and biaxial loading. A novel finding from these results is that the effects of preloading are more profound if the loading does not act parallel to a principal fibre direction. Matrix cracking and delaminations are the most common failure modes observed for specimens under low-velocity impact and are intensified by preload. [ABSTRACT FROM AUTHOR]

Over the last 100 years, it has been a full circle for the automotive industry. Electric vehicles which generally dominated the roads till 1920s slowly lost the ground to the gasoline vehicles due to the large-scale discovery of petroleum across the globe. Electric vehicles are now back again, and this time it is going to stay. In this paper, after discussing the roles of the component shape and the functionality integration to the component to reduce the number of parts, and therefore the weight, the importance of novel manufacturing technologies in achieving the desired shape and part reduction is discussed. Further, the global optima in terms of weight and cost reduction while meeting the performance targets can be achieved with the use of novel forming technology. Finally, the novel forming technologies developed at Tata Steel such as the micro-pillar forming, manufacturing of variable thickness tube, electropulse-assisted forming and modified hot stamping technologies have been discussed in detail. [ABSTRACT FROM AUTHOR]

Purpose: The importance of glass in the production of food packaging has stimulated the assessment of its impact. This study evaluates the environmental performance of hollow glass production in a manufacturing plant operating in Apulia (Italy), compares baseline and two alternative scenarios based on the life cycle assessment (LCA) method and proposes more environmentally friendly options. Methods: The LCA study was performed on primary data provided by a company for 2017 and secondary data retrieved from databases. The LCA was conducted according to ISO 14040:2006 and 14044:2018 with product category rule (PCR) basic module CPC 37. A midpoint approach was chosen. The software selected for the analysis was GaBi thinkstep AG version 8.7.0.18. The study was performed from cradle to grave, and the functional unit considered was 1 kg of finished hollow glass. Results and discussion: A comparative analysis of the different scenarios was conducted considering seven environmental impact indicators. The results highlight major improvements in the environmental impact indicators: in particular, the replacement of raw materials with cullets would yield significant improvements if all of the cullets are recovered in the regional territory. These improvements, as confirmed by the results of Scenario 2, could be further increased both by the use of renewable energy such as photovoltaic (PV) energy and by technological improvements such as lightweighting. Conclusions: Through the LCA, the authors quantified the potential environmental impact associated with the production of hollow glass, thereby identifying solutions and formulating suggestions to reduce the environmental impacts associated with food packaging. The evaluation of the different scenarios could help managers in the decision-making process, thus improving the environmental performance of both the process and final product. [ABSTRACT FROM AUTHOR]

The rising economic and environmental pressures associated with the generation and consumption of energy necessitates the need for lightweighting of railway vehicles. The railway axle is a prime candidate for lightweighting of the unsprung mass. The reduction of unsprung mass correlates to reduced track damage, energy consumption and total operating costs. This paper presents the design of a lightweight multifunctional hybrid metallic-composite railway axle utilising coaxial skins. The lightweight axle assembly comprises a carbon fibre reinforced polymer composite tube with steel stub axles bonded into either end. The structural hybrid metallic-composite railway axle is surrounded by coaxial skins each performing a specific function to meet the secondary requirements. A parametric sizing study is conducted to explore the sensitivity of the design parameters of the composite tube and the stub axle interaction through the adhesive joint. The optimised design parameters of the axle consist of a; composite tube outer diameter of 225 mm, composite tube thickness of 7 mm, steel stub axle extension thickness of 10 mm and a bond overlap length of 100 mm. The optimised hybrid metallic-composite railway axle design concept has a mass of 200 kg representing a reduction of 50% over the solid steel version. [ABSTRACT FROM AUTHOR]

The current performance requirements for the global rail industry demand that trains are more reliable, efficient and can accommodate an increased capacity for more passengers. Lightweight construction of rail vehicles is thus required to meet these requirements. This paper has identified the key components for lightweighting of rail vehicles using fibre reinforced polymer composite materials. A methodology used to select and rank those metallic components suitable for redesign in composite, developed as part of the ACIS (Advanced Composite Integrated Structures) UK project is described. From the audit, five demonstrator components – a cantilevered seat bracket, luggage rack module, intermediate end structure, body side structure and roof structure – were identified by the consortium using the methodology. These are components that the consortium believes to be the most suitable to demonstrate the benefit of a composite replacement in terms of integration potential, lightweighting benefits and commercial viability. Furthermore, rail car body structural components, forming the primary structure of a rail vehicle, were determined to be the most optimal components to replace in composites for maximum lightweighting of the sprung mass. It was estimated that a composite redesign of these components would result in a mass savings of 57% for intermediate end structures, 47% for body side structures and 51% for roof structures. [ABSTRACT FROM AUTHOR]

The potential for lightweighting of railway axles was investigated to primarily reduce the unsprung mass of rail vehicles. The reduction of unsprung mass equates to an overall lighter train, which will help to reduce track damage, energy consumption and total operating costs. Two approaches were considered for the lightweighting of railway axles, which include a hollow axle design and material substitution using advanced composite materials, to offer a more track-friendly design. The first approach showed that if the outer diameter of a hollow axle is increased by 30% over that of the solid axle diameter, a mass reduction of 56% is achievable for a hollow steel axle design. The second approach explored further mass savings that could be achieved through material substitution of a hollow axle. A systematic approach to material selection for the design requirements and constraints of a railway axle was considered to identify the candidate materials for the application. The optimum material identified was a 'bismaleimide matrix + carbon fibre composite.' A hollow axle manufactured from this composite material offered 64% savings in mass when compared to a hollow steel axle, and 84% savings in mass when compared to a solid steel axle. Estimates for the cost savings of lightweighting of an axle were quantified by utilising Network Rail's variable usage charge calculator, to assess the track access charge savings that can be achieved. For the scenario described in this paper, a potential £5.58 million per year could be saved for an intercity 220/M Voyager train, in terms of variable usage charges, over the entire fleet of 34 trains (four carriages per train) by implementing hollow composite axles. This is an example of a costing approach to support the decision making of lightweighting of rail vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics 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.)

Reducing the weight of automobiles is a major contributor to increased fuel economy. The baseline materials for vehicle construction, low-carbon steel and cast iron, are being replaced by materials with higher specific strength and stiffness: advanced high-strength steels, aluminum, magnesium, and polymer composites. The key challenge is to reduce the cost of manufacturing structures with these new materials. Maximizing the weight reduction requires optimized designs utilizing multimaterials in various forms. This use of mixed materials presents additional challenges in joining and preventing galvanic corrosion. [ABSTRACT FROM AUTHOR]

The focus of this study is to (i) understand the effect of the fiber type and content on the mechanical properties of sheet-molding compounds composites and (ii) investigate possible lightweight alternatives to glass fibers-sheet molding compound composites. Glass fiber and basalt fibers are used to make sheet-molding compound composites and the mechanical performance are determined as a function of the fiber type and content. In addition, cellulose nanocrystals are used to enhance the properties of the sheet-molding compound resin system. The possibility of lightweighting the basalt fiber/epoxy and glass fiber/epoxy sheet-molding compound composites is explored by replacing a portion of the fibers, i.e. 12–16 wt%, with a small amount cellulose nanocrystals, i.e. 1–2 wt%. No significant difference was found between the basalt fiber/epoxy and glass fiber/epoxy sheet-molding compound composites in terms of mechanical and impact properties. When cellulose nanocrystals were added to the composites, the properties of glass fiber/epoxy sheet-molding compound composites were enhanced while those of basalt fiber/epoxy sheet-molding compound composites deteriorated. [ABSTRACT FROM AUTHOR]