Hasil untuk "Mechanical industries"

T. Gupta, S. Chaudhary, R. Sharma

Chao Liu, Bin Li, Haishun Du et al.

In this work, nanocellulose was extracted from bleached corncob residue (CCR), an underutilized lignocellulose waste from furfural industry, using four different methods (i.e. sulfuric acid hydrolysis, formic acid (FA) hydrolysis, 2,2,6,6-tetramethylpiperidine-1-oxyl (TEMPO)-mediated oxidation, and pulp refining, respectively). The self-assembled structure, morphology, dimension, crystallinity, chemical structure and thermal stability of prepared nanocellulose were investigated. FA hydrolysis produced longer cellulose nanocrystals (CNCs) than the one obtained by sulfuric acid hydrolysis, and resulted in high crystallinity and thermal stability due to its preferential degradation of amorphous cellulose and lignin. The cellulose nanofibrils (CNFs) with fine and individualized structure could be isolated by TEMPO-mediated oxidation. In comparison with other nanocellulose products, the intensive pulp refining led to the CNFs with the longest length and the thickest diameter. This comparative study can help to provide an insight into the utilization of CCR as a potential source for nanocellulose production.

Jian Yang, Wei Zhang, Jiajun Wu et al.

Recent code large language models have achieved remarkable progress on general programming tasks. Nevertheless, their performance degrades significantly in industrial scenarios that require reasoning about hardware semantics, specialized language constructs, and strict resource constraints. To address these challenges, we introduce InCoder-32B (Industrial-Coder-32B), the first 32B-parameter code foundation model unifying code intelligence across chip design, GPU kernel optimization, embedded systems, compiler optimization, and 3D modeling. By adopting an efficient architecture, we train InCoder-32B from scratch with general code pre-training, curated industrial code annealing, mid-training that progressively extends context from 8K to 128K tokens with synthetic industrial reasoning data, and post-training with execution-grounded verification. We conduct extensive evaluation on 14 mainstream general code benchmarks and 9 industrial benchmarks spanning 4 specialized domains. Results show InCoder-32B achieves highly competitive performance on general tasks while establishing strong open-source baselines across industrial domains.

Jemal Worku Fentaw, Elvin Hajiyev, Abdul Rehman Baig et al.

CO2-based enhanced geothermal system (CO2-EGS), also known as CO2 plume geothermal, has emerged as a promising avenue to address the growing global energy demand and mitigate global climate concerns by exploiting renewable energy from geothermal reservoirs while concurrently sequestering CO2. In this method, CO2, in a supercritical state or dissolved in brine, is used as a working fluid to harness the geothermal energy held in hot reservoir rocks, with part of the CO2 being trapped in the reservoir. Despite their rapidly growing popularity, the integration assessment of CO2-EGS studies, fragmented into various subjects such as thermodynamics, heat transfer, multiphase flow, reservoir hydraulics, geomechanics, and geochemistry, remains insufficiently explored. Thus, a critical review that consolidates conducted studies, identifies gaps, and directs future research in this coupled technology is crucial. This review aims to provide a comprehensive assessment of CO2-EGS, emphasizing its significance, the major challenges affecting its performance and mitigation strategies, the thermophysical properties of CO2 as a working fluid, and CO2 storage while extracting geothermal energy. The study revealed the key benefits of CO2-EGS, including reducing corrosion and scaling effects in the wellbore, maintaining reservoir pressure, storing CO2, increasing sweep efficiency of the reservoir, lowering pumping power, and addressing water scarcity for geothermal systems. Despite its significance, CO2-EGS encounters major challenges, such as cost, drilling and operating wells in harsh geological conditions, CO2 leakage, lost circulation, premature thermal breakthrough, lower specific enthalpy, and incomplete heating. Key factors influencing its performance include properties of the reservoir, natural fractures and faults, geochemical and geomechanical factors, well design, type of thermodynamic cycle used, and CO2-related factors such as injection rate, injection pressure, temperature, and impurities. Overall, this review provides insights into significant advancements achieved and highlights future research to leverage CO2-EGS for reducing CO2 emissions while extracting geothermal energy.

Dimitrios Georgiou, Danqi Sun, Xing Liu et al.

The escalating plastic waste crisis demands global action, yet mechanical recycling - currently the most prevalent strategy - remains severely underutilized. Only a small fraction of the total plastic waste is recycled in this manner, largely due to the significant variability in recycled plastics' mechanical properties. This variability stems from compositional fluctuations and impurities introduced throughout the materials' lifecycle and the recycling process, deterring industries with stringent product specifications from adopting recycled plastics on a wider scale. To overcome this challenge, we propose a composite structure inspired by nacre's microstructure - a natural material known for its exceptional mechanical performance despite its inherent randomness across multiple length scales. This bio-inspired design features stiff recycled plastic platelets ("bricks") within a soft polymeric matrix ("mortar"). We use a tension-shear-chain model to capture the deformation mechanism of the structure, and demonstrate, through a case study of commercial stretch wrap, that the proposed design reduces variability in effective elastic modulus by 89.5% and in elongation at break by 42%, while achieving the same modulus as the virgin stretch wrap material. These findings highlight the potential of the proposed bio-inspired design to enhance the mechanical performance of recycled plastics, but also demonstrate that a universally applicable, chemistry-agnostic approach can substantially broaden their applications, paving the way for sustainable plastic waste management.

Tuğçe Bilen, Mehmet Ozdem

The convergence of Information Technology (IT) and Operational Technology (OT) is a critical enabler for achieving autonomous and intelligent industrial systems. However, the increasing complexity, heterogeneity, and real-time demands of industrial environments render traditional rule-based or static management approaches insufficient. In this paper, we present a modular framework based on the Knowledge-Defined Networking (KDN) paradigm, enabling adaptive and autonomous control across IT-OT infrastructures. The proposed architecture is composed of four core modules: Telemetry Collector, Knowledge Builder, Decision Engine, and Control Enforcer. These modules operate in a closed control loop to continuously observe system behavior, extract contextual knowledge, evaluate control actions, and apply policy decisions across programmable industrial endpoints. A graph-based abstraction is used to represent system state, and a utility-optimization mechanism guides control decisions under dynamic conditions. The framework's performance is evaluated using three key metrics: decision latency, control effectiveness, and system stability, demonstrating its capability to enhance resilience, responsiveness, and operational efficiency in smart industrial networks.

A. R. Pina, Shams El-Adawy, H. J. Lewandowski et al.

Continued growth of the quantum information science and engineering (QISE) industry has resulted in stakeholders spanning education, industry, and government seeking to better understand the workforce needs. This report presents a framework for the categorization of roles in the QISE industry based on 42 interviews of QISE professionals across 23 companies, as well as a description of the method used in the creation of this framework. The data included information on over 80 positions, which we have grouped into 29 roles spanning four primary categories. For each primary category we provide an overview of what unites the roles within a category, a description of relevant subcategories, and definitions of the individual roles. These roles serve as the basis upon which we generate profiles of these roles, which include information about role critical tasks, necessary knowledge and skills, and educational requirements. Our next report will present such profiles for each of the roles presented herein.

Yu Sha, Ningtao Liu, Haofeng Liu et al.

Cavitation intensity recognition (CIR) is a critical technology for detecting and evaluating cavitation phenomena in hydraulic machinery, with significant implications for operational safety, performance optimization, and maintenance cost reduction in complex industrial systems. Despite substantial research progress, a comprehensive review that systematically traces the development trajectory and provides explicit guidance for future research is still lacking. To bridge this gap, this paper presents a thorough review and analysis of hundreds of publications on intelligent CIR across various types of mechanical equipment from 2002 to 2025, summarizing its technological evolution and offering insights for future development. The early stages are dominated by traditional machine learning approaches that relied on manually engineered features under the guidance of domain expert knowledge. The advent of deep learning has driven the development of end-to-end models capable of automatically extracting features from multi-source signals, thereby significantly improving recognition performance and robustness. Recently, physical informed diagnostic models have been proposed to embed domain knowledge into deep learning models, which can enhance interpretability and cross-condition generalization. In the future, transfer learning, multi-modal fusion, lightweight network architectures, and the deployment of industrial agents are expected to propel CIR technology into a new stage, addressing challenges in multi-source data acquisition, standardized evaluation, and industrial implementation. The paper aims to systematically outline the evolution of CIR technology and highlight the emerging trend of integrating deep learning with physical knowledge. This provides a significant reference for researchers and practitioners in the field of intelligent cavitation diagnosis in complex industrial systems.

Zheng Liu

Abstract The current farmland energy management and monitoring system still has problems, such as poor real-time data collection, low energy utilization efficiency, and insufficient intelligent decision-making. Focusing on digital energy management, this paper proposes a data collection and analysis based on edge computing and cloud collaboration architecture to improve the accuracy and real-time performance of farmland environmental monitoring. In terms of intelligent control, deep reinforcement learning is used to optimize irrigation decision-making, and adaptive algorithms are combined to improve the flexibility of agricultural equipment scheduling. Regarding energy management, a digital twin model of the photovoltaic energy storage system is constructed to achieve accurate prediction and optimization of energy flow. Edge-cloud collaborative architecture for real-time data collection/analysis, reducing network latency by 40% compared to traditional cloud-only models; deep reinforcement learning (DRL)-driven irrigation optimization, achieving 51% crop yield increase and 18% water efficiency improvement; digital twin modeling of photovoltaic-energy storage systems, enhancing energy flow prediction accuracy to 98.2% and reducing energy waste by 9.5%; game theory-based resource allocation to balance energy supply–demand, improving system economic benefits by 15%. The system stability reached 96.24%, and the maintenance cost was reduced by 21.0%. The utilization rate of irrigation water increased from 76.9% to 43.0% by 1.8 times, reaching 77.4%.

Yiqun Wu, Shibing Zhang

Abstract With the rapid development of the digital economy, energy consumption patterns are transforming. However, problems remain, such as low energy efficiency and heavy dependence on traditional energy. By addressing data privacy compliance, enhancing model generalization, expanding to practical cases like the BRI, integrating system dynamics concepts, grounding machine learning techniques in theory, linking numerical results to theory, and highlighting research novelty through comparisons, we have provided a robust and insightful analysis. With the development of the digital economy, the transformation of energy consumption patterns is accelerating. In terms of improving energy consumption efficiency, energy consumption per unit of GDP decreased by 0.74%. Technological innovation and the widespread application of digital tools have significantly improved energy efficiency. In international trade, the proportion of exports of high energy-consuming products decreased from 44.7 to 35.67%. In comparison, the proportion of exports of new energy technology products increased from 35.8 to 40.0%. The trade volume of high-tech products accounted for 91.0%, showing that international trade is gradually transforming in a green, low-carbon, and high-value-added direction.

Sainath K, Karuppasamy R, Prabagaran S

The functionally graded materials (FGMs) have been realised to be potential candidates when it comes to high-pressure projects and applications where thermal and mechanical stability is to be ensured in extreme environments. In the research, the drawback of the widely used stainless steel SS316L facing high-stress conditions in the thermal environment will be overcome by the innovation of two new FGMs composed of SS316L and Inconel 625, Ti6Al4V, and Inconel 718. The aim was to conduct the fabrication and testing of a multi-phase FGM with the help of advanced techniques of manufacturing namely additive manufacturing and powder metallurgy, with the strict control of layer thickness of 0.2 mm and contents of its materials (60% SS316L, 20% Inconel 625 or Ti6Al4V, and 20% Inconel 718). Tensile testing, yield testing, fatigue and creep behaviour were performed at temperatures of −20°C and +60°C. The findings indicated that the FGM containing SS316L, Inconel 625, and Inconel 718 proved to be superior to SS316L at every point where its tensile strength is 992 MPa and its yield strength is 602 MPa, also at a temperature of +60 C versus 460 MPa and 186 MPa tensile and yield strengths in SS316L. The advanced fatigue performance and creep resistance were also indicated because of the better qualities of the alloys Inconel. Such results are indicative of gradient composition and layer formation in augmenting thermal and mechanical capabilities. The research ends up with a conclusion that these FGMs can be considered as excellent prospects in terms of the aerospace and power generation industries where strength and thermal endurance are of essence to the next generation of the industry.

Shrishail Basappa Angadi, Santosh Kumar, Madeva Nagaral et al.

The aerospace and automotive engineering industries are seeing a growing need for materials that are both lightweight and very durable. This increased demand has prompted the development of innovative metal matrix composites based on aluminum. The current study aimed at developing and characterization Al7020 metal matrix composites by reinforcing micro boron carbide particles, Al7020/B4C MMCs are fabricated by stir casting method by varying the boron carbide particles in wt.% (0, 2, 4, 6, and 8wt. %). Lastly, the prepared samples were subjected to tensile, compression, hardness, and fracture toughness tests to evaluate the impact of B4C particles on density, mechanical, and microstructural parameters. By incorporating B4C particles into the Al7020 alloy, the experimental results demonstrated that metal matrix composites exhibited enhanced ultimate tensile strength, yield strength, hardness, and compression strength. In addition, the lowest density, highest toughness, and superior micrograph were observed in Al7020/B4C MMCs with 8 wt. % reinforcement of B4C particles with a minor decrease in elongation.

Olajesu Olanrewaju, Isiaka Oluwole Oladele, Samson Oluwagbenga Adelani

Natural fiber-reinforced composites (NFRCs) have become vital in various engineering applications due to their exceptional properties and ease of manufacturing. Properties such as lightweight, sustainability, design flexibility, microstructure, durability, and advanced fabrication techniques have expanded their use across industries. Also, NFRCs are preferred because the extensive reliance on synthetic fibers presents significant challenges in recycling and waste management. Despite their excellent properties, NFRCs have three main challenges: fiber degradation, water degradation, and weak interfacial strength (incompatibility of fibers with the matrix). Consequently, research efforts have been directed at combating these challenges using different surface treatment techniques. However, research has been skewed towards experimental approaches for improving the interfacial strength in plant fiber polymer matrix composites (PMCs). Hence, there exists a dearth of information on the computational approaches for optimizing the interfacial properties of NFRCs. Hence, this review provides experimental and computational approaches (machine learning) for comprehensive optimizing strategies for different natural fibers (plant, animal, and microorganism) and matrices (polymers, metals, and ceramics). This review also highlights the importance of theoretical approaches and numerical modeling in analyzing and optimizing NFRCs. Finally, the review highlights recent advancements in NFRCs, their mechanical properties, potential applications, and future directions.

Ahmed Gailani, Peter Taylor

Decarbonising the industrial sector is vital to reach net zero targets. The deployment of industrial decarbonisation technologies is expected to increase industrial electricity demand in many countries and this may require upgrades to the existing electricity network or new network investment. While the infrastructure requirements to support the introduction of new fuels and technologies in industry, such as hydrogen and carbon capture, utilisation and storage are often discussed, the need for investment to increase the capacity of the electricity network to meet increasing industrial electricity demands is often overlooked in the literature. This paper addresses this gap by quantifying the requirements for additional electricity network capacity to support the decarbonisation of industrial sectors across Great Britain (GB). The Net Zero Industrial Pathways model is used to predict the future electricity demand from industrial sites to 2050 which is then compared spatially to the available headroom across the distribution network in GB. The results show that network headroom is sufficient to meet extra capacity demands from industrial sites over the period to 2030 in nearly all GB regions and network scenarios. However, as electricity demand rises due to increased electrification across all sectors and industrial decarbonisation accelerates towards 2050, the network will need significant new capacity (71 GW + by 2050) particularly in the central, south, and north-west regions of England, and Wales. Without solving these network constraints, around 65% of industrial sites that are large point sources of emissions would be constrained in terms of electric capacity by 2040. These sites are responsible for 69% of industrial point source emissions.

Leonhard Faubel, Klaus Schmid

As Machine Learning (ML) becomes more prevalent in Industry 4.0, there is a growing need to understand how systematic approaches to bringing ML into production can be practically implemented in industrial environments. Here, MLOps comes into play. MLOps refers to the processes, tools, and organizational structures used to develop, test, deploy, and manage ML models reliably and efficiently. However, there is currently a lack of information on the practical implementation of MLOps in industrial enterprises. To address this issue, we conducted a multiple case study on MLOps in three large companies with dedicated MLOps teams, using established tools and well-defined model deployment processes in the Industry 4.0 environment. This study describes four of the companies' Industry 4.0 scenarios and provides relevant insights into their implementation and the challenges they faced in numerous projects. Further, we discuss MLOps processes, procedures, technologies, as well as contextual variations among companies.

Xiao Xia, Dan Zhang, Zibo Liao et al.

The modeling of industrial scenes is essential for simulations in industrial manufacturing. While large language models (LLMs) have shown significant progress in generating general 3D scenes from textual descriptions, generating industrial scenes with LLMs poses a unique challenge due to their demand for precise measurements and positioning, requiring complex planning over spatial arrangement. To address this challenge, we introduce SceneGenAgent, an LLM-based agent for generating industrial scenes through C# code. SceneGenAgent ensures precise layout planning through a structured and calculable format, layout verification, and iterative refinement to meet the quantitative requirements of industrial scenarios. Experiment results demonstrate that LLMs powered by SceneGenAgent exceed their original performance, reaching up to 81.0% success rate in real-world industrial scene generation tasks and effectively meeting most scene generation requirements. To further enhance accessibility, we construct SceneInstruct, a dataset designed for fine-tuning open-source LLMs to integrate into SceneGenAgent. Experiments show that fine-tuning open-source LLMs on SceneInstruct yields significant performance improvements, with Llama3.1-70B approaching the capabilities of GPT-4o. Our code and data are available at https://github.com/THUDM/SceneGenAgent .

Sandeep Gupta

Secure and efficient communication to establish a seamless nexus between the five levels of a typical automation pyramid is paramount to Industry 4.0. Specifically, vertical and horizontal integration of these levels is an overarching requirement to accelerate productivity and improve operational activities. Vertical integration can improve visibility, flexibility, and productivity by connecting systems and applications. Horizontal integration can provide better collaboration and adaptability by connecting internal production facilities, multi-site operations, and third-party partners in a supply chain. In this paper, we propose an Edge-computing-based Industrial Gateway for interfacing information technology and operational technology that can enable Industry 4.0 vertical and horizontal integration. Subsequently, we design and develop a working prototype to demonstrate a remote production-line maintenance use case with a strong focus on security aspects and the edge paradigm to bring computational resources and data storage closer to data sources.

Dayu Yang

Merger and Acquisition (M&A) activities play a vital role in market consolidation and restructuring. For acquiring companies, M&A serves as a key investment strategy, with one primary goal being to attain complementarities that enhance market power in competitive industries. In addition to intrinsic factors, a M&A behavior of a firm is influenced by the M&A activities of its peers, a phenomenon known as the "peer effect." However, existing research often fails to capture the rich interdependencies among M&A events within industry networks. An effective M&A predictive model should offer deal-level predictions without requiring ad-hoc feature engineering or data rebalancing. Such a model would predict the M&A behaviors of rival firms and provide specific recommendations for both bidder and target firms. However, most current models only predict one side of an M&A deal, lack firm-specific recommendations, and rely on arbitrary time intervals that impair predictive accuracy. Additionally, due to the sparsity of M&A events, existing models require data rebalancing, which introduces bias and limits their real-world applicability. To address these challenges, we propose a Temporal Dynamic Industry Network (TDIN) model, leveraging temporal point processes and deep learning to capture complex M&A interdependencies without ad-hoc data adjustments. The temporal point process framework inherently models event sparsity, eliminating the need for data rebalancing. Empirical evaluations on M&A data from January 1997 to December 2020 validate the effectiveness of our approach in predicting M&A events and offering actionable, deal-level recommendations.

Hanqiao Zhang

Neighborhood characteristics have been broadly studied with different firm behaviors, e.g. birth, entry, expansion, and survival, except for firm exit. Using a novel dataset of foreign-invested enterprises operating in Shenzhen's electronics manufacturing industry from 2017 to 2021, I investigate the spillover effects of firm exits on other firms in the vicinity, from both the industry group and the industry class level. Significant neighborhood effects are identified for the industry group level, but not the industry class level.

Mamdouh I. Elamy, Suha A. Mohammed, Ali Basem et al.

This study explored methods to enhance the performance of a coiled solar still (COSS). One technique involved adding a vertical wick distiller (VWSS) with built-in reflectors positioned after the COSS. Additionally, the research examined the impact of incorporating a fan and a separate condenser on the COSS's distillate output. Finally, the investigation assessed the potential advantages of incorporating paraffin wax infused with nanomaterial beneath the COSS base. The key findings revealed significant improvements in distillate production with the COSS modifications. Compared to a standard solar still (CSS), the COSS alone demonstrated a 76 % increase in daily output. Integrating a heating coil and internal reflectors with the COSS further boosted productivity by an impressive 92 %. The most significant advancements were achieved by combining the COSS with a VWSS and additional features. The MCOSS (COSS with VWSS and internal reflectors) exhibited a remarkable 209 % increase in distillate production compared to the CSS. This value increased to a staggering 269 % when incorporating a heating coil, VWSS, and an external condenser. Adding a fan to the MCOSS further enhanced efficiency to 68 %. Notably, incorporating nanomaterial-infused paraffin wax (PCM-Ag) with the MCOSS with VWSS resulted in a 246 % increase in productivity compared to the standard design. The research also revealed a significant decrease in freshwater production costs. The cost per liter of freshwater was determined to be $0.024 for the CSS and a considerably lower $0.0126 for the MCOSS with a fan.