Hasil untuk "Mechanical industries"

J. Abbott

O. Sigmund

Touseef Khan, Koki Toyama, Meor Faisal Zulkifli et al.

In recent years, magnesium (Mg) alloys have gained considerable attention across diverse industries, including aerospace, automotive, electronics, and hydrogen storage, due to their low density, high specific strength, excellent damping capability, and favorable casting, machining, and recycling characteristics. Beyond structural applications, Mg alloys are regarded as favorable implant materials owing to their biodegradability, biocompatibility, and an elastic modulus close to that of cortical bone. Among Mg alloys, alloyed with rare-earth (RE) elements, particularly the WE series, exhibit enhanced formability, reduced basal texture intensity, superior high-temperature performance, and improved mechanical strength. Within this category, the WE43 alloy, based on the magnesium (Mg)–Yttrium (Y)-Neodymium (Nd) system, has become one of the most extensively studied and commercially successful alloys. However, the limited corrosion resistance of this alloy restricts its broader application. This review covers the microstructural evolution, mechanical performance, corrosion behaviour, and interfacial properties of WE43 alloy-based composites, with a special emphasis on metallic glass (MG) reinforcement strategies. The influence of various reinforcements and processing techniques on grain refinement, phase stability, and strengthening mechanisms, is carefully studied. The mechanisms for strengthening and stabilizing MG reinforcements in metallic matrices are next investigated, with a focus on interfacial bonding, improved load transfer, and thermal stability. Despite extensive research on MG-reinforced metal matrix composites, this study highlights a fundamental research gap; the integration of metallic glass into WE43 alloy, especially via friction stir processing (FSP), has not yet been reported. This gap highlights a prospective and untapped avenue for developing enhanced biodegradable Mg composites with superior mechanical integrity and corrosion resistance.

Hongye Zhao, Yi Zhao, Chengzhi Zhang

The academia and industry are characterized by a reciprocal shaping and dynamic feedback mechanism. Despite distinct institutional logics, they have adapted closely in collaborative publishing and talent mobility, demonstrating tension between institutional divergence and intensive collaboration. Existing studies on their knowledge proximity mainly rely on macro indicators such as the number of collaborative papers or patents, lacking an analysis of knowledge units in the literature. This has led to an insufficient grasp of fine-grained knowledge proximity between industry and academia, potentially undermining collaboration frameworks and resource allocation efficiency. To remedy the limitation, this study quantifies the trajectory of academia-industry co-evolution through fine-grained entities and semantic space. In the entity measurement part, we extract fine-grained knowledge entities via pre-trained models, measure sequence overlaps using cosine similarity, and analyze topological features through complex network analysis. At the semantic level, we employ unsupervised contrastive learning to quantify convergence in semantic spaces by measuring cross-institutional textual similarities. Finally, we use citation distribution patterns to examine correlations between bidirectional knowledge flows and similarity. Analysis reveals that knowledge proximity between academia and industry rises, particularly following technological change. This provides textual evidence of bidirectional adaptation in co-evolution. Additionally, academia's knowledge dominance weakens during technological paradigm shifts. The dataset and code for this paper can be accessed at https://github.com/tinierZhao/Academic-Industrial-associations.

Daniel Ruan, Salma Mozaffari, Sigrid Adriaenssens et al.

Industrial robots are increasingly deployed in contact-rich construction and manufacturing tasks that involve uncertainty and long-horizon execution. While learning-based visuomotor policies offer a promising alternative to open-loop control, their deployment on industrial platforms is challenged by a large observation-execution gap caused by sensing, inference, and control latency. This gap is significantly greater than on low-latency research robots due to high-level interfaces and slower closed-loop dynamics, making execution timing a critical system-level issue. This paper presents a latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms under realistic timing constraints. The framework integrates calibrated multimodal sensing, temporally consistent synchronization, a unified communication pipeline, and a teleoperation interface for demonstration collection. Within this framework, we introduce a latency-aware execution strategy that schedules finite-horizon, policy-predicted action sequences based on temporal feasibility, enabling asynchronous inference and execution without modifying policy architectures or training. We evaluate the framework on a contact-rich industrial assembly task while systematically varying inference latency. Using identical policies and sensing pipelines, we compare latency-aware execution with blocking and naive asynchronous baselines. Results show that latency-aware execution maintains smooth motion, compliant contact behavior, and consistent task progression across a wide range of latencies while reducing idle time and avoiding instability observed in baseline methods. These findings highlight the importance of explicitly handling latency for reliable closed-loop deployment of visuomotor policies on industrial robots.

Hasan Tarik Akbaba, Efe Bozkir, Anna Puhl et al.

Extended Reality (XR) offers transformative potential for industrial support, training, and maintenance; yet, widespread adoption lags despite demonstrated occupational value and hardware maturity. Organizations successfully implement XR in isolated pilots, yet struggle to scale these into sustained operational deployment, a phenomenon we characterize as the ``Pilot Trap.'' This study examines this phenomenon through a qualitative ecosystem analysis of 17 expert interviews across technology providers, solution integrators, and industrial adopters. We identify a ``Great Inversion'' in adoption barriers: critical constraints have shifted from technological maturity to organizational readiness (e.g., change management, key performance indicator alignment, and political resistance). While hardware ergonomics and usability remain relevant, our findings indicate that systemic misalignments between stakeholder incentives are the primary cause of friction preventing enterprise integration. We conclude that successful industrial XR adoption requires a shift from technology-centric piloting to a problem-first, organizational transformation approach, necessitating explicit ecosystem-level coordination.

Xinwei Cao

Léa Boros, Lucie Martin, Marco Carozzi et al.

ABSTRACT Biogas production is increasingly promoted across Europe as a renewable energy source, with growing attention to minimizing land use impacts and preserving food production. In France, biogas plant development has rapidly expanded in recent years, along with the use of energy cover crops. This study examines the national land cover changes following the implementation of biogas plants and explores potential explanatory variables for these changes. Using four key databases (the French Land Parcel Identification System, the SINOE database, the Open Data Reseaux Energies database, and the 2020 French Agricultural Census), we identified farms linked to biogas plants and analyzed their land cover dynamics across various farm characteristics between 2010 and 2021. A typology of land cover changes was developed through clustering techniques. At the national level, our results showed significant land cover changes, including increases in maize and other cereal areas (e.g., rye, triticale, sorghum, among others) and decreases in rapeseed and common wheat. Regional variability emerged which suggests distinct strategies of energy crop introduction. Notably, stronger land cover changes were observed on field crop farms and on those with injection‐based biogas plants, which are expected to become the dominant system in the future. Additionally, irrigation availability tended to favor summer energy cover crops over winter energy cover crops. Distinct land cover changes were also observed on organic farms, with a notable increase in “grassland and forage crop” areas (excluding silage maize). As the European biomethane market expands, concerns arise regarding the long‐term land cover implications of this growth. While energy cover crops are promoted as a sustainable feedstock for biomethane production, their widespread adoption could still lead to significant land cover changes. This raises important questions about the feasibility of achieving Europe's biomethane production goals while addressing potential land use challenges.

Shahbaz Hussain, Sibel Irmak, Muhammad Usman Farid

Hydrogen is a promising clean fuel with 0 carbon emission; only byproduct released from its use is water. The current large-scale hydrogen production methods are expensive and do not meet sustainability criteria. Finding alternative but cheaper sustainable ways for hydrogen production is important, and the catalyst plays a key role in this process. This study was designed to develop hierarchical porous carbons (HPCs)-based catalysts to enhance hydrogen production yield from lignocellulosic biomass by hydrothermal gasification. HPCs were synthesized from widely available waste materials, forest-based woody biomass, and poultry feathers with a promising approach (use of solubilized fractions of the precursors rather than direct carbonization of their solid forms, performing in-situ heteroatom doping and enhancing the porosity of the carbon by using a gas-forming salt, etc.). The HPC prepared from biomass/chicken feather mixture in the presence of a gas-forming salt, NaHCO3, was the most promising carbon because of its high porosity structure with pore size ranging from ∼65 nm to ∼1.8 µm, and the 80% of the pores was around 200–450 nm. The specific surface area of the catalyst prepared by deposition of Pt particles on this carbon was found to be 3200 m2/g with an average pore size of 2.3 nm. On the other hand, the HPC prepared in the absence of NaHCO3 had 2900 m2/g surface area and 1.8 nm average pore size. The hydrogen production activity of HPC-with NaHCO3/Pt catalyst was found to be 23.81 ml H2/mg Pt, which was the highest activity among the catalysts tested. This was attributed to the highly porous structure and the presence of sodium or sodium-containing species (e.g., Na2O) in the carbon network. The findings of this study have the potential to open new catalytic opportunities for different reactions using HPCs-based multifunctional catalysts.

Zhengqiu Wu, Yunliang Yu, Jia Chen et al.

Most photocatalyzed CO2 reduction systems employed visible light photosensitizer, metal-containing CO2 reduction catalyst and sacrificial reagent, demonstrating excellent efficiency and high selectivity. However, the influence of metal ion leached from decomposition of trace amounts of metal-containing catalyst has rarely been discussed. Here, we discovered that leaching Fe ion from Fe-MOF during the catalyzed CO2 reduction process was the crucial species for efficient CO2 reduction in our system which utilized [Ru(bpy)3]Cl2·6H2O as photosensitizer, tri-isopropanolamine (TIPA) as sacrificial reagent and Fe-MOF as CO2 reduction catalyst. FeCl3 was tested as CO2 reduction catalyst instead of Fe-MOF and provided 73,750 μmol g−1 h−1 of CO in MeCN, 329,500 μmol g−1 h−1 in N,N-Dimethylformamide after 4 h of visible light irradiation. Additionally, we investigated other metal chlorides (Na, Cr, Mn, Ni et, al.) to study the effect of Fe ion. Both Fe3+, Co2+ and Ni2+ provided satisfactory catalytic efficiency which indicated that the effect of metal ion leaching in Metal-organic frameworks (MOFs) contained photocatalytic CO2 reduction systems should be appreciated. Furthermore, the concentration of Cl− also played a beneficial role and enhanced the catalysis process.

Kieran M R Hunt, Hannah C Bloomfield

Accurate forecasts of energy demand are crucial for managing India’s rapidly growing energy needs as it continues to decarbonise its grid. In this study, we develop state-level data driven models to predict weather-driven energy demand across India using the eXtreme Gradient Boosting framework. The models use as input population-weighted meteorological variables averaged over various timescales. The models are trained on daily energy demand data, scraped from reports issued by Grid-India, which we correct for trends in population and economic growth. The models demonstrate high skill, with half having $r^2 \gt 0.8$ , significantly outperforming traditional multivariate linear regression models. We explain model behaviour through Shapley analysis and find a strong sensitivity to day of the week and public holidays, with reductions in energy demand on Sundays and varying impacts during holidays. While important variables vary by state and season, daily minimum temperature and 30 d mean temperature consistently emerge as key predictors, reflecting nighttime air conditioning use and seasonal heating or cooling needs. We also identify threshold behaviours, indicating large increases in energy demand once temperatures pass certain values. Using reanalysis, we extend our models to estimate all-India energy demand from 1979–2023, calibrated to 2023 conditions. We confirm a pronounced seasonal cycle, with greatest demand during the pre-monsoon and monsoon onset (May–June) and lowest demand in the winter (November–December). Combining these results with timeseries of renewable energy production, we find the largest energy deficit (demand minus renewable generation) occurs during or after monsoon withdrawal (September–October). Extreme deficit days, posing a risk to the national grid, are associated with early monsoon withdrawal or late monsoon breaks, leading to low wind speeds and persistently high dewpoint temperatures and cloud cover. The demand dataset created here can be used for energy grid management, siting of future renewable energy generation, and to aid with ensuring security of supply.

Jianbo Zhao, Danjie Li, Ronghua Deng et al.

Soybean protein isolates (SPIs) have been widely studied because of their excellent gel-forming properties. However, their unstable gel structures and poor strength limit their applications in the food industry. To address this, konjac glucomannan (KGM) and oxidized chitin nanocrystals (O-ChNCs) were introduced into SPI-based hydrogels to enhance their mechanical properties. The present study investigated the effects of incorporating KGM and O-ChNCs on the physical properties and microstructure of SPI hydrogels, as well as the possible underlying mechanisms. The rheological behavior test of the solution demonstrated that the viscoelastic properties of the sol were enhanced upon incorporating O-ChNCs and KGM. Scanning electron microscopy showed highly compact and uniformly distributed SPI hydrogels with the addition of O-ChNCs and KGM. Gel strength and textural property tests showed that the gel strength and gel hardness of SPI hydrogels with the addition of O-ChNCs and KGM were 102.57 ± 1.91 g/cm² and 545.29 ± 6.84 g. O-ChNCs effectively filled the SPI hydrogel network, while KGM enhanced physical entanglement between SPI molecular chains and formed intermolecular hydrogen bonds. Therefore, this study provides an important basis for the introduction of SPI-based hydrogels in the biomedical and food industries.

Sarah K. Yorke, Zhenze Yang, Aviad Levin et al.

Peptides are recognized for their varied self-assembly behaviors, forming a wide array of structures and geometries, such as spheres, fibers, and hydrogels, each presenting a unique set of material properties. The functionalities of these materials hold exceptional interest for applications in biology, medicine, photonics, nanotechnology and the food industry. In specific, the ability to exploit peptides as viable and sustainable mechanical materials requires sequence design that enables superior performance, notably a high Young's modulus. As the peptide sequence space is vast, however, even a slight increase in sequence length leads to an exponential increase in the number of potential peptide sequences to be characterized. Here, we combine coarse-grained molecular dynamics simulations, atomic force microscopy experiments and machine learning models to correlate the sequence length and composition with the mechanical properties of self-assembled peptides. We calculate the Young's modulus for all possible amino acid sequences of di- and tripeptides using high-throughput coarse-grained methods, and validate these calculations through in-situ mechanical characterization. For pentapeptides, we select and calculate properties for a subset of sequences to train a machine learning model, which allows us to predict the modulus for other sequences. The combined workflow not only identifies promising peptide candidates with exceptional mechanical performances, but also extends current understanding of the sequence-to-function relationships for peptide materials, for specific applications.

Liangtao Lin, Zhaomeng Zhu, Tianwei Zhang et al.

Mission-critical industrial infrastructure, such as data centers, increasingly depends on complex management software. Its operations, however, pose significant challenges due to the escalating system complexity, multi-vendor integration, and a shortage of expert operators. While Robotic Process Automation (RPA) offers partial automation through handcrafted scripts, it suffers from limited flexibility and high maintenance costs. Recent advances in Large Language Model (LLM)-based graphical user interface (GUI) agents have enabled more flexible automation, yet these general-purpose agents face five critical challenges when applied to industrial management, including unfamiliar element understanding, precision and efficiency, state localization, deployment constraints, and safety requirements. To address these issues, we propose InfraMind, a novel exploration-based GUI agentic framework specifically tailored for industrial management systems. InfraMind integrates five innovative modules to systematically resolve different challenges in industrial management: (1) systematic search-based exploration with virtual machine snapshots for autonomous understanding of complex GUIs; (2) memory-driven planning to ensure high-precision and efficient task execution; (3) advanced state identification for robust localization in hierarchical interfaces; (4) structured knowledge distillation for efficient deployment with lightweight models; and (5) comprehensive, multi-layered safety mechanisms to safeguard sensitive operations. Extensive experiments on both open-source and commercial DCIM platforms demonstrate that our approach consistently outperforms existing frameworks in terms of task success rate and operational efficiency, providing a rigorous and scalable solution for industrial management automation.

Eneko Osaba, Iñigo Perez Delgado, Alejandro Mata Ali et al.

This article explores the current state and future prospects of quantum computing in industrial environments. Firstly, it describes three main paradigms in this field of knowledge: gate-based quantum computers, quantum annealers, and tensor networks. The article also examines specific industrial applications, such as bin packing, job shop scheduling, and route planning for robots and vehicles. These applications demonstrate the potential of quantum computing to solve complex problems in the industry. The article concludes by presenting a vision of the directions the field will take in the coming years, also discussing the current limitations of quantum technology. Despite these limitations, quantum computing is emerging as a powerful tool to address industrial challenges in the future.

Elizabeth Lin, Jonah Ghebremichael, William Enck et al.

Software supply chains, while providing immense economic and software development value, are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks specifically targeting vulnerable links in critical software supply chains. These attacks disrupt the day-to-day functioning and threaten the security of nearly everyone on the internet, from billion-dollar companies and government agencies to hobbyist open-source developers. The ever-evolving threat of software supply chain attacks has garnered interest from both the software industry and US government in improving software supply chain security. On Thursday, March 6th, 2025, four researchers from the NSF-backed Secure Software Supply Chain Center (S3C2) conducted a Secure Software Supply Chain Summit with a diverse set of 18 practitioners from 17 organizations. The goals of the Summit were: (1) to enable sharing between participants from different industries regarding practical experiences and challenges with software supply chain security; (2) to help form new collaborations; and (3) to learn about the challenges facing participants to inform our future research directions. The summit consisted of discussions of six topics relevant to the government agencies represented, including software bill of materials (SBOMs); compliance; malicious commits; build infrastructure; culture; and large language models (LLMs) and security. For each topic of discussion, we presented a list of questions to participants to spark conversation. In this report, we provide a summary of the summit. The open questions and challenges that remained after each topic are listed at the end of each topic's section, and the initial discussion questions for each topic are provided in the appendix.

Ali Raza, Muhammad Farhan Khan, Zeeshan Alam et al.

This paper presents a joint framework that integrates reconfigurable intelligent surfaces (RISs) with Terahertz (THz) communications and non-orthogonal multiple access (NOMA) to enhance smart industrial communications. The proposed system leverages the advantages of RIS and THz bands to improve spectral efficiency, coverage, and reliability key requirements for industrial automation and real-time communications in future 6G networks and beyond. Within this framework, two power allocation strategies are investigated: the first optimally distributes power between near and far industrial nodes, and the second prioritizes network demands to enhance system performance further. A performance evaluation is conducted to compare the sum rate and outage probability against a fixed power allocation scheme. Our scheme achieves up to a 23% sum rate gain over fixed PA at 30 dBm. Simulation results validate the theoretical analysis, demonstrating the effectiveness and robustness of the RIS-assisted NOMA MIMO framework for THz enabled industrial communications.

Mohammed A. Sarran, Adnan A. AbdulRazak, Mohammed F. Abid et al.

Oily wastewater is a major environmental issue resulting from different industrial and manufacturing activities. Contaminated water with oil represents a significant environmental hazard that can harm numerous life forms. Several methodologies have been tested for the removal of oily wastewater from aqueous solutions, and adsorption in a flow-through reactor is an effective mechanism to reduce these effluents. This study focuses on evaluating the ability of Fe₃O₄/Bent material to adsorb gasoline emulsion from a solution using a fixed-bed column, and it involves analyzing the resulting breakthrough curves. The FT-IR, SEM, EDX, and XRD techniques were used to characterize Fe₃O₄/Bent. Various ranges of variables were examined, including bed height (2–4 cm), flow rate (3–3.8 mL/min), and initial concentration (200–1000 mg/L), to determine their impacts on the mass transfer zone (MTZ) length and the adsorption capacity (q_e). It was shown that a higher bed height and a lower flow rate contributed to a longer time of breakthrough and exhaustion. At the same time, it was noted that under high initial gasoline concentrations, the fixed-bed system rapidly reached breakthrough and exhaustion. Models like the Yoon–Nelson and Thomas kinetic column models were employed to predict the breakthrough curves. Thomas and Yoon–Nelson’s breakthrough models provided a good fit for the breakthrough curves with a correlation coefficient of R² > 0.95. Furthermore, with a fixed-bed system, the Thomas and Yoon–Nelson models best describe the breakthrough curves for gasoline removal.

Harsh Kumar, Vaibhav Gupta, Velamala Bharath et al.

Deep groove ball bearings (DGBBs) are extensively utilized in industrial machinery, mechanical systems, and household appliances due to their simple design, low maintenance, and ability to operate at high speeds. A critical issue in the performance of these bearings is the power loss by internal friction torque, which adversely affects system efficiency, longevity, and reliability, particularly in demanding applications such as aviation and marine systems. The friction torque in DGBBs is influenced by factors such as load, speed, surface roughness, and lubricant viscosity, making the precise understanding of these elements essential for optimizing system efficiency. Despite its significance, the effect of surface roughness on friction torque in DGBBs remains underexplored. This paper presents an analytical model to evaluate the frictional moments resulting from interactions between the ball–race and ball–cage in lubricated, low-speed DGBBs. This model employs a mixed elastohydrodynamic lubrication approach to determine the friction coefficient at the contact interfaces. This study explores how surface roughness and speed affect both ball–race and ball–cage friction torque, offering a comprehensive analysis of their influence on overall frictional torque. Additionally, the effect of surface roughness on ball–cage contact forces is investigated, enhancing the understanding of its contribution to friction torque. These insights aim to improve DGBB design and operation, maximizing performance and energy efficiency.

Marta Mroczkowska, David Culliton, Kieran J. Germaine et al.

The valorisation of food by-products is an important step towards sustainability in food production. Tomatoes constitute one of the most processed crops in the world (160 million tonnes of tomatoes are processed every year), of which 4% is waste. This translates to 6.4 million tonnes of tomato skins and seeds. Currently, this waste is composted or is used in the production of low-value animal feed; higher value can be achieved if this waste stream is re-appropriated for more advanced purposes. Plant cuticle is a membrane structure found on leaves and fruit, including tomatoes, and is mainly composed of cutin. The main function of plant cuticle is to limit water loss from the internal tissue of the plant. Cutin, which can be recovered from the tomato skins by pH shift extraction, has hydrophobic (water repellent) properties and is therefore an ideal raw material for the development of a novel water-resistant coating. In this study, biomass-based bioplastics were developed. Unfortunately, although these bioplastics have good mechanical properties, their hydrophilic nature results in poor water barrier properties. To mitigate this, a very effective water-resistant coating was formulated using the cutin extracted from tomato peels. The water vapour permeability rates of the bioplastics improved by 74% and the percentage swelling of the bioplastic improved by 84% when treated with the cutin coating. With physicochemical properties that can compete with petroleum-based plastics, these bioplastics have the potential to address the growing market demand for sustainable alternatives for food packaging. Using ingredients generated from by-products of the food processing industries (circular economy), the development of these bioplastics also addresses the UN’s Sustainable Development Goal (SDG) 12, Sustainable Consumption and Production (SCP).