Hasil untuk "Mechanical industries"

Sergii G. Karnaukh, Oleg E. Markov, Nataly V. Chosta

Abstract This study focuses on the development and evaluation of a new press design for separating rolled metal products. The goal of this design is to increase energy efficiency, reduce cutting force, and improve workpiece geometric accuracy compared to traditional crank presses. A wedge-hinged mechanism with a concave wedge is proposed, enabling a hybrid cutting process combining shear and torsion. The arcuate path of the cutting knife creates a stress concentrator that initiates material fracture and minimizes plastic deformation. The proposed method reduced energy consumption by 26% during separating and decreased peak cutting force by 20%. The optimal geometric relationship for energy minimization was determined: the distance to the hinge fulcrum should be half the radius of the concave wedge. A high correlation between theoretical and experimental results (within 8%) was confirmed. Furthermore, it was found that the energy-force parameters are independent of the orientation of the rolled product, emphasizing the technological versatility of the process. The developed mechanism represents a promising solution for upgrading existing mechanical presses and designing new equipment in the mechanical engineering and railway industries. Implementation of this technology is expected to significantly reduce operating costs, increase equipment durability, and improve the quality of the final product.

Hailiang Zhao, Ziqi Wang, Daojiang Hu et al.

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

Yuxin Lu, Zhen Peng, Xiqiang Xia et al.

Against the backdrop of the global green transition and "dual carbon" goals, mining industry chain enterprises are pivotal entities in terms of resource consumption and environmental impact. Their environmental performance directly affects regional ecological security and is closely tied to national resource strategies and green transformation outcomes. Ensuring the authenticity and reliability of their environmental disclosure is thus a core and urgent issue for sustainable development and national strategic objectives.From a corporate governance perspective, this study examines equity balance as a fundamental governance mechanism, investigating its inhibitory effect on greenwashing behavior among these enterprises and the underlying pathways involved. Methodologically, the paper innovatively employs a Variational Autoencoder (VAE) and a Double Machine Learning (DML) model to construct counterfactual scenarios, mitigating endogeneity concerns and precisely identifying the causal relationship between equity balance and greenwashing. The findings indicate, first, a significant negative causal relationship between equity balance and corporate greenwashing, confirming its substantive governance effect. Second, this inhibitory effect exhibits notable heterogeneity, manifesting more strongly in western regions, upstream segments of the industrial chain, and industries with high environmental sensitivity. Third, the governance effect demonstrates clear temporal dynamics, with the strongest impact occurring in the current period, followed by a diminishing yet statistically significant lagged effect, and ultimately a stable long-term cumulative influence. Finally, mechanism analysis reveals that equity balance operates through three distinct channels to curb greenwashing: alleviating management performance pressure, enhancing the stability of the executive team, and intensifying media scrutiny.

Lindsay Shade, Karen Rignall, Lyndsay Tarus et al.

Scholars from a wide range of disciplines have grappled with defining and assessing the limitations and opportunities for ‘just’ low carbon energy transitions. Here, we offer an alternative approach to developing a framework for just transition that is grounded in long-term participatory action research with environmental justice communities in Appalachia. We ground our discussion in literature from both scholars and social movements, as well as experiences of the authors through collective autoethnography. Specifically, our work seeks to bring land ownership to the forefront of conversations about economic development in Appalachia and about energy transition more broadly, and to expand the perspectives of directly impacted communities in the scholarly literature. For the past 7 years, the contributors to this article have worked to build a broad-based ‘land study collective’ to support public knowledge, action, and policy focused on the role of land ownership in both incumbent energy systems and transition dynamics. We discuss our shared goals, our process of building the collective and defining and studying research questions across geographically dispersed stakeholders, and vignettes from empirical research of collective members. We conclude with challenges and considerations for others who may want to integrate participatory research on land ownership into their just transition frameworks. We pay special attention to the ethics and empirical benefits of collective autoethnography for developing just transition frameworks that incorporate the lived experiences of those most impacted by energy extraction regimes.

Yuriy Romasevych, Oleksandr Zarivnyi

Для підтвердження застосовності теоретичного методу синтезу оптимального керування рухом пристрою для транспортування малогабаритних вантажів постає питання проведення експериментального дослідження такого керування на практиці. В даній роботі описано методику проведення експериментального дослідження процесу стабілізації положення пристрою для транспортування малогабаритних вантажів та методи оцінки якості такої стабілізації. Очікуваним результатом було отримати експериментальні дані перевірки якості розробленого керування для 11 наборів коефіцієнтів ПІД-регулятора. Надалі з них обрано коефіцієнти регулятора, які найкраще себе показали в процесі стабілізації положення пристрою. Також отримано експериментальні дані роботи пристрою з мінімальною похибкою при порівнянні з теоретичними даними. При проведенні експериментального дослідження використано фізичну модель двоколісного пристрою для транспортування малогабаритних вантажів. Було перевірено якість реалізації регулювання положення пристрою на одинадцяти наборах коефіцієнтів ПІД-регулятора. Зібрано масиви експериментальних даних роботи пристрою, проведено порівняння з теоретичними даними та проведено оцінку якості процесу стабілізації положення пристрою. При співставленні теоретичних і експериментальних даних отримано показники максимальних та середньоквадратичних похибок кута нахилу пристрою, показники похибок максимальної та середньоквадратичної кутової швидкості нахилу пристрою. Декремент згасання коливань знаходився в межах 0,25…2,11. Серед усіх розв’язків обрано найкращим з практичної точки зору є результат набору наступних коефіцієнтів ПІД-регулятора: пропрорційний k1=-2,112, інтегральний k2=-1,756, диференціальний k3=-1,38·10-7. Цей результат відповідає найбільшому декременту згасання коливань (λ=2,11). Отриманий результат дав підстави вважати методику синтезу оптимального керування дієвою, а задачу експериментальної перевірки виконаною.

Ruowen Tu, Hyun Chan Kim, Henry A. Sodano

Additive manufacturing (AM) of lightweight, high-performance engineering polymers is an important research focus in the automotive, electronics and aerospace industries. Aramid material is a highly crystalline polymer in the form of fibers with superior mechanical and thermal properties than most high-performance thermoplastics used in AM. However, manufacturing all-aramid 3D structures has been challenging due to the processing difficulty of aramid. In this work, AM of all-aramid 3D structures is achieved by two approaches: simultaneous protonation and precipitation printing of aramid nanofiber (ANF) colloids, and precipitation printing of aramid/sulfuric acid liquid crystalline solutions. After comparison, the ANF approach proves superior to the sulfuric acid method, offering enhanced printability, greater mechanical strength in the printed parts, and improved capability for microstructure customization. Specifically, the dense all-aramid structures produced through the ANF approach exhibit exceptional mechanical properties, with a Young’s modulus of 7.2 GPa and a tensile strength of 146.6 MPa, outperforming other unfilled, high-performance polymers manufactured through AM. These structures are also capable of withstanding extreme environments, including temperatures up to 350 °C. Therefore, high-performance all-aramid 3D structures can be realized via ANF-based precipitation 3D printing, which can be used as lightweight structural or heat protection parts in aircraft and automotive systems.

Yucong Gu, Kaiwen Wang, Zhengyu Zhang et al.

Abstract Lightweight aluminum alloy is one of the widely used structural materials for various industries due to its low density, high strength-to-weight ratio, good corrosion resistance, and excellent recyclability. However, complex service conditions often result in material degradation due to simultaneous mechanical and corrosion attacks on the metal surfaces, such as tribocorrosion. This phenomenon represents a complex multiphysics challenge, wherein the tribocorrosion-induced material loss emerges as a function of varied environmental, mechanical, and electrochemical descriptors, each entailing distinct yet interlinked physical processes. The pursuit of simultaneous optimization across multiple material properties to enhance the overall tribocorrosion resistance is hampered by the inherent trade-offs between wear and corrosion resistance. Addressing this complexity, our study develops a novel methodology fusing machine-learning (ML) and genetic algorithm (GA)-based optimization techniques to tailor aluminum-based alloys for enhanced tribocorrosion resistance. Leveraging an experimentally validated multiphysics finite element analysis (FEA) model, we have used six key material parameters to model the tribocorrosion performance of Al alloys over a large property space. The ML model employs an ensemble method of artificial neural networks (ANNs) to predict the tribocorroded surface profile and total material loss based on FEA simulation results, significantly reducing computational time compared to conventional FEA methods. Crucially, our high-throughput screening pinpoints corrosion current density and yield strength as two pivotal parameters influencing tribocorrosion behavior. Harnessing GA optimization alongside the ML model, we efficiently identify a suite of optimal material properties—encompassing both mechanical and electrochemical aspects—for aluminum alloys, resulting in superior tribocorrosion resistance. This selection is substantiated through validation against high-fidelity FEA simulation results. This data-driven framework holds promise for tailoring tribocorrosion-resistant materials beyond aluminum alloys, adaptable to a wide range of metals and service environments.

Yohanes Abebe, Sivaprakasam Palani, Belete Sirahbizu

The need for strong, lightweight polymer composites constantly increases in the modern automobile, aviation, and defense sectors. Polyester resins are the predominant thermosetting polymers and are frequently utilized in various industries. E-glass fiber possesses significant qualities, including resistance to heat, chemicals, and moisture. Wool fibers were employed as reinforcement because of their accessibility and sustainability. A more comprehensive analysis is necessary to incorporate nanoparticles and the hybridization of different natural and synthetic fiber types, as seen in a composite composed of glass and wool fibers. In this research, polyester/wool-glass fiber with nano-alumina (Al2O3) particles at 2.0 wt% reinforced polymer composites (C0A-C5A) were made via hand layup and compression molding. The addition of Al2O3 nanoparticles on wool-glass fiber reinforced composites and its effect on properties and morphology were characterized using mechanical testing and scanning electron microscopy (SEM) methods. The analysis of nanoparticle dispersion and the resulting modifications to the composite structure were performed using X-ray diffraction. Adding nano alumina enhanced the tensile strength by 55 %, compressive strength by 38 %, flexural strength by 109 % and impact strength by 47 %. The higher tensile strength of 99.39 MPa, compressive strength of 102.51 MPa, flexural strength of 135.22 MPa and impact strength of 380.34 kJ/m2 were found in (C1A) glass fiber with nano alumina mixed composites. These nanocomposites can enhance the composites' tensile, compressive, flexural and impact strength and are preferred for structural application in automotive and construction industries.

Wan-Ling Chen, Rong-Shean Lee

Variations in the warm formability of AA7075 sheets are primarily attributed to differences in precipitate morphology resulting from distinct thermal histories. To better understand this relationship, this study systematically investigates the influence of precipitate characteristics—quantified by the product of precipitate volume fraction and average radius—on forming limits across various thermal routes in warm forming processes. A modified Cockcroft–Latham ductile fracture model incorporating this microstructural parameter was developed, calibrated against experimental data from warm isothermal Nakajima tests, and implemented within a finite element framework. The proposed model enables the accurate prediction of forming limit curves with minimal experimental effort, thereby significantly reducing the reliance on extensive mechanical testing. Building upon the validated FE model, a practical methodology for rapid R-value estimation under warm forming conditions was established, involving the design of specimen geometries optimised for isothermal Nakajima testing. This approach achieved R-value predictions within 5% deviation from conventional uniaxial tensile test results. Furthermore, experimental results indicated that AA7075 sheets exhibited nearly isotropic deformation behaviour under retrogression warm forming conditions. Overall, the methodology proposed in this study bridges the gap between formability prediction and process simulation, offering a robust and scalable framework for the industrial optimisation of warm forming processes for high-strength aluminium alloys.

Haijun Li, Tianqi Shi, Zifu Shang et al.

Machine translation (MT) has become indispensable for cross-border communication in globalized industries like e-commerce, finance, and legal services, with recent advancements in large language models (LLMs) significantly enhancing translation quality. However, applying general-purpose MT models to industrial scenarios reveals critical limitations due to domain-specific terminology, cultural nuances, and stylistic conventions absent in generic benchmarks. Existing evaluation frameworks inadequately assess performance in specialized contexts, creating a gap between academic benchmarks and real-world efficacy. To address this, we propose a three-level translation capability framework: (1) Basic Linguistic Competence, (2) Domain-Specific Proficiency, and (3) Cultural Adaptation, emphasizing the need for holistic evaluation across these dimensions. We introduce TransBench, a benchmark tailored for industrial MT, initially targeting international e-commerce with 17,000 professionally translated sentences spanning 4 main scenarios and 33 language pairs. TransBench integrates traditional metrics (BLEU, TER) with Marco-MOS, a domain-specific evaluation model, and provides guidelines for reproducible benchmark construction. Our contributions include: (1) a structured framework for industrial MT evaluation, (2) the first publicly available benchmark for e-commerce translation, (3) novel metrics probing multi-level translation quality, and (4) open-sourced evaluation tools. This work bridges the evaluation gap, enabling researchers and practitioners to systematically assess and enhance MT systems for industry-specific needs.

Annalena Daniels, Sebastian Kerz, Salman Bari et al.

Autonomous robotic grasping of uncertain objects in uncertain environments is an impactful open challenge for the industries of the future. One such industry is the recycling of Waste Electrical and Electronic Equipment (WEEE) materials, in which electric devices are disassembled and readied for the recovery of raw materials. Since devices may contain hazardous materials and their disassembly involves heavy manual labor, robotic disassembly is a promising venue. However, since devices may be damaged, dirty and unidentified, robotic disassembly is challenging since object models are unavailable or cannot be relied upon. This case study explores grasping strategies for industrial robotic disassembly of WEEE devices with uncertain vision data. We propose three grippers and appropriate tactile strategies for force-based manipulation that improves grasping robustness. For each proposed gripper, we develop corresponding strategies that can perform effectively in different grasping tasks and leverage the grippers design and unique strengths. Through experiments conducted in lab and factory settings for four different WEEE devices, we demonstrate how object uncertainty may be overcome by tactile sensing and compliant techniques, significantly increasing grasping success rates.

Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

This article presents applied research on line balancing within the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process, by Lean Methodology for garment modernization. It explores the application of line balancing in the modern garment industry, focusing on the significant impact of intelligent hanger systems and hanger lines on the stitching process. It aligns with Lean Methodology principles for garment modernization. Without the implementation of line balancing technology, the garment manufacturing process using hanger systems cannot improve output rates. The case study demonstrates that implementing intelligent line balancing in a straightforward practical setup facilitates lean practices combined with a digitalization system and automaton. This approach illustrates how to enhance output and reduce accumulated work in progress.

Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula et al.

The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on servo motors and a microcontroller interface with Mechanical links were also fabricated. Kinematic and dynamic analyses have been done in order to provide precise positioning and effective loads. Inverse Kinematics algorithm and Proportional-Integral-Derivative (PID) controller were also applied to improve the precision of control. The ability of the system to carry out tasks with high accuracy and repeatability is confirmed by simulation and experimental testing. The suggested robotic arm is an affordable, expandable, and dependable method of automation of numerous mundane procedures in the manufacturing industry.

Rahmat Doni Widodo, Herry Sutanto, Deni Fajar Fitriana et al.

The use of natural fiber as composite material is growing rapidly due to excellent characteristics, environmentally friendly quality and low price. For this reason, natural fiber breaks the dominance of synthetic fiber composite previously used in industries, especially in automotive manufacturing. The purpose of this study is to determine the effect of fiber orientation on the flexural and tensile strength of water hyacinth-sugarcane fiber composite with epoxy matrix. The composite then be tested as an alternative to doortrim. Fiber orientation applied to specimens was random for sugarcane fiber, but continuous on water hyacinth fiber with angle variants of -45/45, 45/90, 45/45, and 90/90. Specimen standard for flexural test is ASTM D790-15 and ASTM D638-14 for tensile test. The study results show that the highest average value for flexural strength is 51.7 MPa of the 45/90 variant, while the lowest average value of 16.6 MPa is the 45/45 variant. Both 45/90 and -45/45 variants have the highest tensile strength of 30 MPa. The 90/90 variant records the weakest tensil strength value of 20 MPa. The highest flexural and tensile strength values exceed minimum score of the SNI 01-4449-2006 and equal the values of panel assy backdoortrim. This experiment proves that fiber orientation affects mechanical properties of composite materials, especially in terms of flexural or tensile strength.

Jiang Guo, Junichiro Shiomi

Materials informatics has emerged as a powerful tool for the discovery, design, and optimization of materials with tailored thermal radiative properties. This perspective review highlights the recent advances in optimization algorithms, including Bayesian optimization, deep learning, and quantum computing, and their applications in various fields such as thermophotovoltaics, radiative cooling, gas sensors, and directional emitters. We also discuss the challenges and future directions of this rapidly evolving field.

Peng Lin, Ali Basem, As'ad Alizadeh et al.

The initial temperature has a considerable effect on aluminum polycrystals' physical stability and mechanical performance, with the possibility to optimize their mechanical properties for practical applications. Thus, using a molecular dynamics technique, the effect of temperature on the mechanical properties of aluminum polycrystals is studied. Stress-strain curves, ultimate strength, and Young's modulus were all measured at temperatures of 300, 350, 400, and 450 K. The findings from MD simulations show that the initial temperature significantly affects the physical stability and mechanical performance of designed aluminum polycrystals. The aluminum polycrystal experiences a numerical increase in ultimate strength and Young's modulus from 6640 to 74.072 to 7.055 and 79.226 GPa, respectively, when subjected to the optimal initial conditions of 350 K. With further increasing temperature to 450 K, ultimate strength and Young's modulus decrease to 6.461 and 74.413 GPa, respectively. The observed decrease in ultimate strength and Young's modulus of the aluminum polycrystal as the temperature increased from the optimal condition of 350 K–450 K can be attributed to the weakening of interatomic attraction forces at higher temperatures. This reduction in interatomic bonding strength resulted in decreased material stiffness and resistance to deformation, leading to lower ultimate strength and Young's modulus values. This study's novelty lies in its comprehensive assessment of the initial temperature's effects on the mechanical performance of aluminum polycrystals, providing valuable insights for practical applications and advancing beyond previous efforts in the literature.

Giampaolo Cuozzo, Enrico Testi, Salvatore Riolo et al.

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. The importance of this paradigm shift has produced a massive, yet heterogeneous, proliferation of scientific contributions. However, these works lack a standardized and cohesive characterization of the IIoT framework coming from different entities, like the 3rd Generation Partnership Project (3GPP) or the 5G Alliance for Connected Industries and Automation (5G-ACIA), resulting in divergent perspectives and potentially hindering interoperability. To bridge this gap, this article offers a unified characterization of (i) the main IIoT application domains, (ii) their respective requirements, (iii) the principal technological gaps existing in the current literature, and, most importantly, (iv) we propose a systematic approach for assessing and addressing the identified research challenges. Therefore, this article serves as a roadmap for future research endeavors, promoting a unified vision of the IIoT paradigm and fostering collaborative efforts to advance the field.

Jinfan Liu, Yichao Yan, Junjie Li et al.

Video anomaly detection (VAD) is a challenging task aiming to recognize anomalies in video frames, and existing large-scale VAD researches primarily focus on road traffic and human activity scenes. In industrial scenes, there are often a variety of unpredictable anomalies, and the VAD method can play a significant role in these scenarios. However, there is a lack of applicable datasets and methods specifically tailored for industrial production scenarios due to concerns regarding privacy and security. To bridge this gap, we propose a new dataset, IPAD, specifically designed for VAD in industrial scenarios. The industrial processes in our dataset are chosen through on-site factory research and discussions with engineers. This dataset covers 16 different industrial devices and contains over 6 hours of both synthetic and real-world video footage. Moreover, we annotate the key feature of the industrial process, ie, periodicity. Based on the proposed dataset, we introduce a period memory module and a sliding window inspection mechanism to effectively investigate the periodic information in a basic reconstruction model. Our framework leverages LoRA adapter to explore the effective migration of pretrained models, which are initially trained using synthetic data, into real-world scenarios. Our proposed dataset and method will fill the gap in the field of industrial video anomaly detection and drive the process of video understanding tasks as well as smart factory deployment.

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;

Naseela Pervez, Alexander J. Titus

Biotechnology Industry 5.0 is advancing with the integration of cutting-edge technologies like Machine Learning (ML), the Internet Of Things (IoT), and cloud computing. It is no surprise that an industry that utilizes data from customers and can alter their lives is a target of a variety of attacks. This chapter provides a perspective of how Machine Learning Security Operations (MLSecOps) can help secure the biotechnology Industry 5.0. The chapter provides an analysis of the threats in the biotechnology Industry 5.0 and how ML algorithms can help secure with industry best practices. This chapter explores the scope of MLSecOps in the biotechnology Industry 5.0, highlighting how crucial it is to comply with current regulatory frameworks. With biotechnology Industry 5.0 developing innovative solutions in healthcare, supply chain management, biomanufacturing, pharmaceuticals sectors, and more, the chapter also discusses the MLSecOps best practices that industry and enterprises should follow while also considering ethical responsibilities. Overall, the chapter provides a discussion of how to integrate MLSecOps into the design, deployment, and regulation of the processes in biotechnology Industry 5.0.