Hasil untuk "Mechanical industries"

C. Suryanarayana, In-Seop An

Yanjin Lu, Songquan Wu, Y. Gan et al.

R. Wauthlé, B. Vrancken, Britt Beynaerts et al.

N. Lamba, P. Bhad, V. Manthena et al.

In order to examine the relationship between temperature change and thermoelastic deformation in the assumed functionally graded materials (FGMs), the present work involves fractional-order mathematical modeling of a thermoelastic thick elliptical annular plate, where the material properties of the plate are considered thermo-sensitive. To determine the precise thermal response of the problem, the density, heat capacity, and thermal conductivity are also graded axially, in addition to the thermo-sensitivity. The temperature distribution of the plate is assumed to be zero at the start and end of the thickness variation, with convective heat exchange boundary conditions in the spatial direction. Kirchhoff's variable transformation method is used to eliminate temperature-dependent nonlinearity in the heat transfer equations. The Laplace transform for the initial condition and a perturbation solution are obtained through an asymptotic series expansion. The heat transfer problem is ultimately solved using the modified Mathieu transform, the Taylor series technique, and its inversion, yielding the required temperature distribution function in the Laplace domain. Using nickel as the metal and titanium carbide as the ceramic, a model of a ceramic-metal-based FGM is constructed for numerical computations. All results are graphically presented, showing the distributions of temperature, displacement, and stress curves along the spatial variables for two different fractional-order parameters by varying the plate’s thickness. This study enables a deeper understanding of the thermo-mechanical responses of FGMs through its novel application to FGMs with elliptical geometry and its innovative modeling approach incorporating thermally sensitive material properties. It opens the door to optimized designs in thermal industries. The understanding and improvement of thermal behavior in advanced materials have been significantly enhanced by the discovery of the influence of fractional parameters in an FGM elliptical plate.

Supriyono, Hendra Franka, Rusmalah et al.

This study aims to develop biodegradable materials for sustainable food packaging and household products by enhancing their mechanical strength, barrier properties, and environmental performance. Conducted between January 2022 and July 2023, the research employed a mixed-method experimental design involving material formulation, functional performance testing, and lifecycle assessment (LCA). Three biopolymers – PLA, PHA, and TPS – were reinforced with natural additives such as cellulose, lignin, and nano-fillers. In addition, functional additives including thyme oil, cinnamon oil, tocopherols, and catechins were integrated to create active packaging solutions. The results showed that the modified biopolymers exhibited up to 60% higher mechanical strength and improved thermal and barrier properties. Antimicrobial additives reduced bacterial growth by 60%, while antioxidants extended food shelf life by 30%. Lifecycle analysis revealed a 50% reduction in carbon emissions and lower energy consumption compared to conventional plastics. This study contributes a novel, scalable approach to biodegradable packaging development, offering practical solutions for reducing plastic waste while maintaining product quality and safety. The findings support broader adoption of sustainable materials across packaging and household industries, promoting circular economy practices.

Chao Yin, Hongzhe Yue, Qing Han et al.

Automated semantic understanding of dense point clouds is a prerequisite for Scan-to-BIM pipelines, digital twin construction, and as-built verification--core tasks in the digital transformation of the construction industry. Yet for industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: TLS acquisitions of water treatment plants, chiller halls, and pumping stations exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks (e.g., S3DIS or ScanNet) cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset comprising 612 million expertly labelled points at 6 mm resolution from 13 water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest and most demanding testbed for industrial 3D scene understanding to date. We further establish the first industrial cross-paradigm benchmark, evaluating nine representative methods across fully supervised, weakly supervised, unsupervised, and foundation model settings under a unified benchmark protocol. The best supervised method achieves 55.74% mIoU, whereas zero-shot Point-SAM reaches only 15.79%--a 39.95 percentage-point gap that quantifies the unresolved domain-transfer challenge for industrial TLS data. Systematic analysis reveals that this gap originates from a dual crisis: statistical rarity (215:1 imbalance, 3.5x more severe than S3DIS) and geometric ambiguity (tail-class points share cylindrical primitives with head-class pipes) that frequency-based re-weighting alone cannot resolve. Industrial3D, along with benchmark code and pre-trained models, will be publicly available at https://github.com/pointcloudyc/Industrial3D.

Jiahui Zhou, Dan Li, Ruibing Jin et al.

Providing reliable predictive maintenance is a critical industrial AI service essential for ensuring the high availability of manufacturing devices. Existing deep-learning methods present competitive results on such tasks but lack a general service-oriented framework to capture complex dependencies in industrial IoT sensor data. While Transformer-based models show strong sequence modeling capabilities, their direct deployment as robust AI services faces significant bottlenecks. Specifically, streaming sensor data collected in real-world service environments often exhibits multi-scale temporal correlations driven by machine working principles. Besides, the datasets available for training time-to-failure predictive services are typically limited in size. These issues pose significant challenges for directly applying existing models as robust predictive services. To address these challenges, we propose MsFormer, a lightweight Multi-scale Transformer designed as a unified AI service model for reliable industrial predictive maintenance. MsFormer incorporates a Multi-scale Sampling (MS) module and a tailored position encoding mechanism to capture sequential correlations across multi-streaming service data. Additionally, to accommodate data-scarce service environments, MsFormer adopts a lightweight attention mechanism with straightforward pooling operations instead of self-attention. Extensive experiments on real-world datasets demonstrate that the proposed framework achieves significant performance improvements over state-of-the-art methods. Furthermore, MsFormer outperforms across industrial devices and operating conditions, demonstrating strong generalizability while maintaining a highly reliable Quality of Service (QoS).

Liam Doyle, Fargol Seifollahi, Chandralekha Singh

The rapid advancement of quantum information science and technology (QIST) has generated significant attention from people in academia, industry, and the public. Recent advances in QIST have led to both opportunities and challenges for students and researchers who are curious about the potential of the field amid hype, considering whether their skills are aligned with what the field needs, and contemplating how collaborating with industries may impact their research. This qualitative study presents perspectives from leading quantum researchers who are educators on three critical aspects shaping QIST's development: (1) the impact of hype in the field and strategies for managing expectations, (2) approaches to creating conducive environments that attract students and established researchers from non-physics disciplines, and (3) effective models for fostering university-industry partnerships that can be valuable for students and researchers alike. These aspects, along with several interconnected challenges, were explored through in-depth interviews with quantum educators. Our findings reveal nuanced perspectives on managing the hype cycle and its risks in creating unrealistic expectations. Regarding greater interdisciplinary engagement and attracting more non-physicists to QIST, educators emphasized the need to recognize and leverage existing expertise from other fields while developing educational pathways that meet diverse student backgrounds to prepare them for the QIST workforce. On university-industry partnerships, respondents highlighted successful models, while noting persistent challenges around intellectual property, confidentiality, and differing organizational goals. These insights provide valuable guidance for educators, policymakers, and industry leaders working to build a sustainable quantum workforce while maintaining realistic expectations about the field's trajectory.

Ruomu Tan, Martin W Hoffmann

The integration of artificial intelligence (AI) into the industrial sector has not only driven innovation but also expanded the ethical landscape, necessitating a reevaluation of principles governing technology and its applications and awareness in research and development of industrial AI solutions. This chapter explores how AI-empowered industrial innovation inherently intersects with ethics, as advancements in AI introduce new challenges related to transparency, accountability, and fairness. In the chapter, we then examine the ethical aspects of several examples of AI manifestation in industrial use cases and associated factors such as ethical practices in the research and development process and data sharing. With the progress of ethical industrial AI solutions, we emphasize the importance of embedding ethical principles into industrial AI systems and its potential to inspire technological breakthroughs and foster trust among stakeholders. This chapter also offers actionable insights to guide industrial research and development toward a future where AI serves as an enabler for ethical and responsible industrial progress as well as a more inclusive industrial ecosystem.

Lin Liu, Rita Machacy, Simi Kuniyilh

The Industrial Internet of Things (IIoT) integrates intelligent sensing, communication, and analytics into industrial environments, including manufacturing, energy, and critical infrastructure. While IIoT enables predictive maintenance and cross-site optimization of modern industrial control systems, such as those in manufacturing and energy, it also introduces significant privacy and confidentiality risks due to the sensitivity of operational data. Contrastive learning, a self-supervised representation learning paradigm, has recently emerged as a promising approach for privacy-preserving analytics by reducing reliance on labeled data and raw data sharing. Although contrastive learning-based privacy-preserving techniques have been explored in the Internet of Things (IoT) domain, this paper offers a comprehensive review of these techniques specifically for privacy preservation in Industrial Internet of Things (IIoT) systems. It emphasizes the unique characteristics of industrial data, system architectures, and various application scenarios. Additionally, the paper discusses solutions and open challenges and outlines future research directions.

Zhifei Yi

Abstract On the basis of reviewing relevant literature research, this paper uses the LDA clustering model to conduct a clustering analysis on the news corpus of China’s A-share market. Three environmental risk factors, namely “climate”, “carbon emissions”, and “ecology”, are extracted and their impact on stock excess returns is empirically tested. The results showed that environmental risk factors could better explain the returns of long-short portfolios based on environmental ratings, while traditional Fama–French five-factors and momentum factors could not explain this portfolio return. This paper further proposes a weighted investment portfolio strategy based on environmental risk factors. Compared with the equally weighted portfolio, the long-short portfolio constructed using this strategy demonstrates a stronger upward trend and lower volatility during the sample period from 2009 to 2024, effectively avoiding the impact of the 2015 A-share market crash. At the individual stock level, the Fama–MacBeth regression results showed that all three environmental risk factors had significant predictive power for future returns.

Shaoyang Yin, Xiaohua Yang, Qi Xu et al.

Abstract This study conducts an in-depth analysis of the optimization model of the industrial value chain under the Energy Internet environment, and provides strong data support for the optimization of the industrial value chain through the presentation of specific data. Research data show that the application effect of Energy Internet in the industrial value chain is remarkable. In terms of energy consumption, the implementation of Energy Internet technology has resulted in a notable 25% reduction in the average energy consumption cost across the industrial value chain. Specifically, considering a prominent manufacturing enterprise as a case study, the introduction of the Energy Internet has led to a substantial 30% decline in its energy consumption cost, thereby enhancing the company's cost control capabilities significantly. In terms of production efficiency, the utilization of Energy Internet technology has brought forth remarkable improvements. According to statistics, the production efficiency of enterprises implementing Energy Internet technology has increased by an average of 18%. Especially in the manufacturing industry, some leading companies have realized the intelligence and automation of production processes through the Energy Internet, and the increase in production efficiency has reached more than 25%. Based on these specific data, this study builds an industrial value chain optimization model. The proposed model takes into account the multifaceted impact of the Energy Internet on each link of the industrial value chain, achieving comprehensive optimization by rationalizing resource allocation, enhancing energy utilization efficiency, and mitigating operational expenses. The simulation data reveal that, guided by this optimization model, enterprises can achieve a marked enhancement in their overall competitiveness. Furthermore, it is anticipated that this approach will potentially lead to a further 10% improvement in energy efficiency and a reduction of 15% in operational costs. In addition, this study also combines the actual cases of many industries to verify the optimization model. Following the implementation of the optimization model, the participating case companies have exhibited remarkable outcomes. Specifically, they have achieved an average reduction of 22% in energy costs and a corresponding increase of 17% in production efficiency. These findings further corroborate the efficacy of the optimization model.

Sabreen Bashir, Maqsood A Siddiqui, Abdulaziz A Al-Khedhairy et al.

Cannabis sativa (Hemp) is renowned for its diverse applications across multiple industries. This versatile plant is utilized in food production, paper manufacturing, pharmaceutical development, cosmetic formulations, biofuel generation, and most notably, in the textile sector. The hemp fiber’s mechanical performance, low cost, and environmental sustainability make it a promising alternative to conventional fiber but the plant is highly susceptible to several agronomic and environmental factors, particularly herbicides. Our research investigates the impact of glyphosate and metribuzin herbicides on Cannabis sativa fiber quantity and quality. Cellulose and hemicellulose content, mechanical properties, and morphological features of fiber from treated plants were analyzed. Herbicide treatments significantly affected fiber composition and properties. Treatment with low-concentration glyphosate resulted in a reduction of both cellulose and hemicellulose content, whereas low-concentration metribuzin induced a slight increase in cellulose levels. Exposure to high concentrations of either herbicide led to a significant decrease in both cellulose and hemicellulose components. Mechanical tests and X-Ray Diffraction revealed that low-concentration glyphosate weakened fiber’s tensile strength, whereas low-concentration metribuzin enhanced it. However, high concentrations of both herbicides decreased tensile strength. Bast fiber content initially increased with low herbicide concentrations but declined at higher levels. Scanning electron microscopy revealed progressive structural damage to fiber with increasing herbicide concentrations. Glyphosate caused surface disruption, while metribuzin induced more severe degradation, including surface erosion and bubbling at high concentrations. These findings highlight the complex effects of glyphosate and metribuzin on Cannabis sativa fiber properties, emphasizing the need for careful consideration of herbicide use in hemp cultivation for textile production.

Mingshen Li, Bo Yang, Chun Li et al.

Thanks to its excellent thermal conductivity, copper material is widely used in industrial plate heat exchangers within the nuclear energy and power generation industries. However, due to its relatively poor mechanical strength, the copper components requires surfaces strengthening. One viable approach is to form a fine-grained layer with enhanced mechanical properties through surface mechanical treatment. However, the fine-grained structures are prone to grain coarsening and even abnormal grain growth (AGG), leading to rapid deterioration of strengthening. In this study, we successfully implemented friction stir surface compositing (FSSC) on pure copper with hybrid micro/nano-TiC particles under ultralow heat-input, achieving a low processing peak temperature of 367.3 °C. The TiC particles provided effective Zener pinning on dislocation migration and grain boundary coalescence, resulting in a strengthened fine-grained surface with remarkable microstructural and mechanical thermal stability. After 700°C-30min thermal exposure, FSSC specimens maintained 83 HV surface hardness (50.9 % increase over base material) and 203.2 MPa tensile strength (54.5 % enhancement). Notably, the size of TiC particles performed a temperature-adaptive relationship with the thermal stability of the FSSC-processed surface. At intermediate temperatures (400 °C), 40 nm TiC particles provided superior thermal stable effect, while at high temperatures (700 °C), 5 μm TiC particles exhibited better thermal stable improvement. Furthermore, in addition to fine grain strengthening and dislocation strengthening mechanisms, the FSSC specimens benefited from Orowan strengthening and twinning-induced plasticity (TWIP) effects induced by TiC particles, achieving a strength-ductility synergy compared to the FSSP. This study provides a practical example for surface strengthening of copper alloys in high-temperature service environments.

Kirsten Franzen, Alice Favero, Caleb Milliken et al.

ABSTRACT As global interest in enhancing energy security, reducing energy costs, and promoting rural economic development grows, the use of forest residues for bioenergy has gained attention. While bioenergy derived from forest residues can help meet power needs and support policy goals, significant uncertainty remains regarding the greenhouse gas (GHG) emissions associated with their production and use. This study aims to explore the key drivers of these uncertainties by reviewing estimates of GHG emissions from forest residue use for energy, as presented in peer‐reviewed journals, reports, and gray literature. The findings reveal a wide range of GHG emission outcomes, with some studies suggesting net emissions and others indicating net removals. This uncertainty stems from the complexity of time scales, variety of forest management approaches and feedstock quality, assumptions about alternative scenarios, and varying approaches to emissions accounting. Recognizing that each method has its unique attributes, we propose an ideal framework that integrates multiple approaches to provide a more comprehensive assessment of the potential net GHG outcomes of using forest residues for energy.

Peter Hines, Florian Magnani, Josefa Mula et al.

This paper proposes a human-centered conceptual model integrating lean and Industry 4.0 based on the literature review and validated it through a case study in the context of an advanced automotive first-tier supplier. Addressing a significant gap in existing research on lean Industry 4.0 implementations, the study provides both theoretical insights and practical findings. It emphasizes the importance of a human-centered approach, identifies key enablers and barriers. In the implementation process of the case study, it is considered at group level and model site level through operational, social and technological perspectives in a five-phase multi-method approach. It shows what effective human-centered lean Industry 4.0 implementation look like and how advanced lean tools can be digitized. It highlights 26 positive and 10 negative aspects of the case and their causal relation. With the appropriate internal and external technological knowhow and people skills, it shows how successful implementation can benefit the organization and employees based on the conceptual model that serves as a first step toward lean Industry 5.0.

Siwei Li, Liang Yue, Xiangyu Kong et al.

This article introduces the concept of load aggregation, which involves a comprehensive analysis of loads to acquire their external characteristics for the purpose of modeling and analyzing power systems. The online identification method is a computer-involved approach for data collection, processing, and system identification, commonly used for adaptive control and prediction. This paper proposes a method for dynamically aggregating large-scale adjustable loads to support high proportions of new energy integration, aiming to study the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction methods. The experiment selected 300 central air conditioners as the research subject and analyzed their regulation characteristics, economic efficiency, and comfort. The experimental results show that as the adjustment time of the air conditioner increases from 5 minutes to 35 minutes, the stable adjustment quantity during the adjustment period decreases from 28.46 to 3.57, indicating that air conditioning loads can be controlled over a long period and have better adjustment effects in the short term. Overall, the experimental results of this paper demonstrate that analyzing the aggregation characteristics of regional large-scale adjustable loads using online identification techniques and feature extraction algorithms is effective.

Wai-Yu Ashley Lam, Hong Zhao, Bo Zhang et al.

With the increasing demand for electric vehicles, further development of Li+ batteries require more comprehensive studies and advanced techniques to analyze various battery material and mechanisms. Determining the concentration of Li+ and electric potential inside batteries can effectively reveal and predict the electrochemical performance, understanding the charge/discharge processes and failure mechanisms. Recently, in situ observation of Li+ movement have been reported by utilizing optical microscopy, neutron imaging (NI), neutron depth profiling (NDP), and transmission electron microscopy (TEM)-based electron energy-loss spectroscopy (EELS). These extensive works suggest their broad potential applications, including revealing the spatial distribution of Li+, mapping electrode elements, and indicating degradation mechanisms. Moreover, direct visualization of potential changes through TEM-based electron holography (EH) and Kelvin probe force microscopy (KPFM) can discover and validate more valuable information. This perspective paper summarizes the current development of advanced in situ techniques for observing Li+ and potential distribution inside batteries for the first time. Additionally, we address the key challenges faced by these techniques along with their possible solutions. The aim of this paper is to provide a comprehensive discussion of in situ methods for analyzing reaction mechanisms, optimizing electrochemical performance, and potentially supporting the further development of battery simulation.

Sher Muhammad Ghoto, Ramez Raja, Sajjad Bhnagwar et al.

Rapid urbanization worldwide is driving increased demand for petroleum products. Yet, crude oil reserves—finite, geographically concentrated resources—are insufficient to meet this rising need, especially in countries lacking substantial fossil fuel reserves. This situation underscores the urgency of shifting toward alternative energy sources before reserves are exhausted. This study conducted particulate matter emissions and endurance testing using diesel fuel mixed with aluminum oxide nanoparticles. The endurance test involved a single-cylinder, horizontal diesel engine, running for 60 hours without modifications. Two fuel samples were examined: D100 (pure diesel) as the baseline and D97Al?O? (97% diesel with 3% aluminum oxide nanoparticles). Engine performance metrics and sound pressure levels were recorded at a constant 1400 RPM, with variable loads from 0.0 to 1.6 Kg-m, incremented by 0.1 Kg-m. The load was set at 1.0 Kg-m for endurance testing with a constant 1400 RPM. Visual inspection of fuel injector tips helped analyze the deposition of aromatic compounds on injector surfaces for each fuel sample. Electron microscopy provided detailed insights into deposit formation, showing that carbon deposition was reduced by 22.22% when aluminum oxide was used as an additive further analysis of the particulate matter emissions the results shows that PM reduced by 12.08% in aluminum oxide compared to the diesel fuel. Because they aid in the creation of cleaner fuel technologies that can lessen reliance on traditional petroleum products and minimize pollution, the study's findings have wider energy and environmental ramifications.

Congcong Cai, Xinyuan Li, Hao Fan et al.

Abstract Layered metal oxides are promising cathode materials for sodium-ion batteries (SIBs) due to their high theoretical specific capacity and wide Na+ diffusion channels. However, the irreversible phase transitions and cationic/anionic redoxes cause fast capacity decay. Herein, P2-type Na0.67Mg0.1Mn0.8Fe0.1O2 (NMMF-1) cathode material with moderate active Fe3+ doping has been designed for sodium storage. Uneven Mn3+/Mn4+distribution is observed in NMMF-1 and the introduction of Fe3+ is beneficial for reducing the Mn3+ contents both at the surface and in the bulk to alleviate the Jahn–Teller effect. The moderate Fe3+/Fe4+ redox can realize the best tradeoff between capacity and cyclability. Therefore, the NMMF-1 demonstrates a high capacity (174.7 mAh g−1 at 20 mA g−1) and improved cyclability (78.5% over 100 cycles) in a wide-voltage range of 1.5–4.5 V (vs. Na+/Na). In-situ X-ray diffraction reveals a complete solid-solution reaction with a small volume change of 1.7% during charge/discharge processes and the charge compensation is disclosed in detail. This study will provide new insights into designing high-capacity and stable layered oxide cathode materials for SIBs.