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Numerous metals can be processed using the additive manufacturing process laser-based powder bed fusion of metals (PBF-LB/M, ISO/ASTM 52900). The main advantages of additive manufacturing technologies are the high degree of design freedom and the cost-effective implementation of lightweight structures. This could be profitable for gears with increased power density, combining reduced mass with considerable material strength. Current research on additively manufactured gears is focused on developing lightweight structures but is seldom accompanied by simulations and even less by mechanical testing. There has been very little research into the mechanical and material properties of additively manufactured gears. The purpose of this study is to investigate the behavior of lightweight structures in additively manufactured gears under static loads.

This research identifies the static load-carrying capacity of helical gears with different lightweight structures produced by PBF-LB/M with the case hardening steel 16MnCr5. A static gear loading test rig with a maximum torque at the pinion of T₁ = 1200 Nm is used. Further focus is set on analyzing material properties such as the relative density, microstructure, hardness depth profile and chemical composition.

All additively manufactured gear variants show no failure or plastic deformation at the maximum test load. The shaft hub connection, the lightweight hub designs and the gearing itself are stable and intact regarding their form and function. The identified material characteristics are comparable to conventionally manufactured gears (wrought and machined), but also some particularities were observed.

This research demonstrates the mechanical strength of lightweight structures in gears. Future research needs to consider the dynamic load-carrying capacity of additively manufactured gears.

Nowadays, residential buildings have become increasingly important due to the growing communities. The purpose of this study is to investigate the behavior of a steel structural framing system that incorporates lightweight load-bearing walls and slabs, and to compare the weight of materials used in cold-formed and hot-finished steel structural systems for affordable housing.

Four types of models consisting of 243 members were simulated. Model 1 is a cold-formed steel structural framing system, while Model 2 is a hot-finished steel structural framing system. Both Models 1 and 2 use lightweight wall panels and lightweight composite slabs. Models 3 and 4 are made with brick walls and precast reinforced concrete systems, respectively. These structures use different wall and slab materials, namely, brick walls and precast reinforced concrete. The analysis includes bending behavior, buckling resistance, shear resistance and torsional rotation analysis.

This study found that using thinner steel sections can increase the deflection value. Meanwhile, increasing member length and the ratio of slenderness will decrease buckling resistance. As the applied load increases, buckling deformation also increases. Furthermore, decreasing shear area causes a reduction in shear resistance. Thicker sections and the use of lightweight materials can decrease the torsional rotation value.

The weight comparison of the steel structures shows that Model 1, which is a cold-formed steel structure with lightweight wall panels and lightweight composite slabs, is the most suitable model due to its lightweight and affordability for housing. This model can also be used as a reference for the optimal design of modular structural framing using cold-formed steel materials in the field of civil engineering and as a promotional tool.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

Fake news in cyberspace has greatly interfered with national governance, economic development and cultural communication, which has greatly increased the demand for fake news detection and intervention. At present, the recognition methods based on news content all lose part of the information to varying degrees. This paper proposes a lightweight content-based detection method to achieve early identification of false information with low computation costs.

The authors' research proposes a lightweight fake news detection framework for English text, including a new textual feature extraction method, specifically mapping English text and symbols to 0–255 using American Standard Code for Information Interchange (ASCII) codes, treating the completed sequence of numbers as the values of picture pixel points and using a computer vision model to detect them. The authors also compare the authors' framework with traditional word2vec, Glove, bidirectional encoder representations from transformers (BERT) and other methods.

The authors conduct experiments on the lightweight neural networks Ghostnet and Shufflenet, and the experimental results show that the authors' proposed framework outperforms the baseline in accuracy on both lightweight networks.

The authors' method does not rely on additional information from text data and can efficiently perform the fake news detection task with less computational resource consumption. In addition, the feature extraction method of this framework is relatively new and enlightening for text content-based classification detection, which can detect fake news in time at the early stage of fake news propagation.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

The aim of this paper is to determine the thermal conductivity of a protective layer of alkali-activated cement and the possibility of performing fire protection with fireclay sand and Lightweight mortar. Unprotected steel structures have generally low fire resistance and require surface protection. The design of passive protection of a steel element must consider the service life of the structure and the possible need to replace the fire protection layer. Currently, conventional passive protection options include intumescent coatings, which are subject to frequent inspection and renewal, gypsum and cement-based fire coatings and gypsum and cement board fire protection.

Alkali-activated cements provide an alternative to traditional Portland clinker-based materials for specific areas. This paper presents the properties of hybrid cement, its manufacturability for conventional mortars and the development of passive fire protection. Fire experiments were conducted with mortar with alkali-activated and fireclay sand and lightweight mortar with alkali-activated cement and expanded perlite. Fire experiment FE modelling.

The temperatures of the protected steel and the formation of cracks in the protective layer were investigated. Based on the experiments, the thermal conductivities of the two protective layers were determined. Conclusions are presented on the applicability of alkaline-activated cement mortars and the possibilities of applicability for the protection of steel structures. The functionality of the passive fire layer was confirmed and the strengths of the mortar used were determined. The use of alkali-activated cements was shown to be a suitable option for sustainable passive fire protection of steel structures.

Eco-friendly fire protection based on hybrid alkali-activated cement of steel members.

This paper aims to characterize and develop a new ecological lightweight concrete reinforced by addition of palm plant fibers (from vegetal waste) to be used in the thermal and acoustical insulation of local constructions. The date palm plant fibers are characterized by their low sensitivity to chemical reactions, low cost and large availability in local regions. Therefore, the newly obtained lightweight concrete may suggest a great interest, as it seems to be able to achieve good solutions for local construction problems, technically, economically and ecologically.

The experimental program focused on developing the composition of palm-fiber-reinforced concrete, by studying the effect of the length of the fibers (10, 20, 30 and 40 mm) and their mass percentage (0.5%, 1%, 1.5% and 2%), on the mechanical and acoustical properties of the composite. The main measured parameters were the compressive strength and flexural strength, sound absorption coefficient, noise reduction coefficient (NRC), etc. These tests were also borne out by the measure of density and water absorption, as well as microstructure analyses. To fully appreciate the behavior of the material, visualizations under optical microscope and scanning electron microscope analyses were carried out.

The addition of plant fibers to concrete made it possible to formulate a new lightweight concrete having interesting properties. The addition of date palm fibers significantly decreased the density of the concrete and consequently reduced its mechanical strength, particularly in compression. Acceptable compressive strength values were possible, according to the fibers content, while better values have been obtained in flexion. On the other hand, good acoustical performances were obtained: a considerable increase in the sound absorption coefficient and the NRC was recorded, according to the content and length of fibers. Even the rheological behavior has been improved with the addition of fibers, but with short fibers only.

Over the recent decades, many studies have attempted to search for more sustainable and environmentally friendly building materials. Therefore, this work aims to study the possibility of using waste from date palm trees as fibers in concrete instead of the conventionally used fibers. Although many researches have already been conducted on the effect of palm plant fibers on the mechanical/physical properties of concrete, no information is available neither on the formulation of this type of concrete nor on its acoustical properties. Indeed, due to the scarcity of raw materials and the excessive consumption of energy, the trend of plant fibers as resources, which are natural and renewable, is very attractive. It is therefore a major recycling project of waste and recovery of local materials.

The purpose of this paper is mainly to know: (1) the sound absorption coefficient of porous composite structures constituted by a new kind of lightweight ceramic foam and perforated plate; (2) the availability of an equivalent porous material model, recently proposed by the present author, to these composite structures in sound absorption.

A kind of lightweight ceramic foam with bulk density of 0.38–0.56 g·cm-3 was produced by means of molding, drying and sintering. The effect of stainless steel perforated plate on sound absorption performance of the ceramic foam was investigated by means of JTZB absorption tester.

The results indicate that the sound absorption performance could be obviously changed by adding the stainless steel perforated plate in front of the porous samples and the air gap in back of the porous samples. Adding the perforated plate to the porous sample with a relatively large pore size, the sound absorption performance could be evidently improved for the composite structure. When the air gap is added to the composite structure, the first absorption peak shifts to the lower frequency, and the sound absorption coefficient could increase in the low frequency range.

Based on the equivalent porous material model and the “perforated plate with air gap” model, the sound absorption performance of the composite structures can be simulated conveniently to a great extent by using Johnson-Champoux-Allard model.

Owing to the complex space environment and limited computing resources, traditional and deep learning-based methods cannot complete the task of satellite component contour extraction effectively. To this end, this paper aims to propose a high-quality real-time contour extraction method based on lightweight space mobile platforms.

A contour extraction method that combines two edge clues is proposed. First, Canny algorithm is improved to extract preliminary contours without inner edges from the depth images. Subsequently, a new type of edge pixel feature is designed based on surface normal. Finally, surface normal edges are extracted to supplement the integrity of the preliminary contours for contour extraction.

Extensive experiments show that this method can achieve a performance comparable to that of deep learning-based methods and can achieve a 36.5 FPS running rate on mobile processors. In addition, it exhibits better robustness under complex scenes.

The proposed method is expected to promote the deployment process of satellite component contour extraction tasks on lightweight space mobile platforms.

A pixel feature for edge detection is designed and combined with the improved Canny algorithm to achieve satellite component contour extraction. This study provides a new research idea for contour extraction and instance segmentation research.

Elevated temperature material properties are essential in predicting structural member's behavior in high-temperature exposures such as fire. Even though experimental methodologies are available to determine these properties, advanced equipment with high costs is required to perform those tests. Therefore, performing those experiments frequently is not feasible, and the development of numerical techniques is beneficial. A numerical technique is proposed in this study to determine the temperature-dependent thermal properties of the material using the fire test results based on the Artificial Neural Network (ANN)-based Finite Element (FE) model.

An ANN-based FE model was developed in the Matlab program to determine the elevated temperature thermal diffusivity, thermal conductivity and the product of specific heat and density of a material. The temperature distribution obtained from fire tests is fed to the ANN-based FE model and material properties are predicted to match the temperature distribution.

Elevated temperature thermal properties of normal-weight concrete (NWC), gypsum plasterboard and lightweight concrete were predicted using the developed model, and good agreement was observed with the actual material properties measured experimentally. The developed method could be utilized to determine any materials' elevated temperature material properties numerically with the adequate temperature distribution data obtained during a fire or heat transfer test.

Temperature-dependent material properties are important in predicting the behavior of structural elements exposed to fire. This research study developed a numerical technique utilizing ANN theories to determine elevated temperature thermal diffusivity, thermal conductivity and product of specific heat and density. Experimental methods are available to evaluate the material properties at high temperatures. However, these testing equipment are expensive and sophisticated; therefore, these equipment are not popular in laboratories causing a lack of high-temperature material properties for novel materials. However conducting a fire test to evaluate fire performance of any novel material is the common practice in the industry. ANN-based FE model developed in this study could utilize those fire testing results of the structural member (temperature distribution of the member throughout the fire tests) to predict the material's thermal properties.