Hasil untuk "Mechanical industries"

J. Cowie

Said Azerblou, Redouane Oubah, Hamza Ouachtouk et al.

Sustainable energy transition, particularly via the use of electric vehicles (EVs), is a prominent solution to address the environmental challenges. A catalyst for this transformation is the ability to manufacture autonomous lithium-ion batteries (LIBs), a vital component for EVs. Leading countries are competing to ensure an independent and autonomous supply of raw materials necessary for this energy shift, especially for nickel manganese cobalt oxide (NMC) chemistry, a significant cathode for LIBs. Morocco, a North African country, has a large production capacity for NMC raw materials, including nickel, manganese, and cobalt, but these resources are mostly exported in their raw or intermediate forms without significant valorisation to meet the NMC cathode requirements. To mitigate this challenge, this article develops refining processes that valorise actual mineral resources to produce the battery-grade precursors necessary for NMC cathodes. A rigorous examination of earlier studies found that sulphate precursors are the main metal sources used to make NMC cathodes in both research and industry. Refining processes have been established to transform natural manganese ore into a high-purity manganese sulphate precursor. Regarding nickel and cobalt sulphate, transformational processes were developed by adapting existing facilities. These processes will enable Morocco to produce 600 metric tonnes of nickel sulphate, 9305 metric tonnes of cobalt sulphate, and 56,160 metric tonnes of manganese sulphate, which in turn allows the manufacture of almost 370,000 EVs. This work paves the way for Morocco to valorise its mineral resources and develop an integrated industrial ecosystem for the EVs supply chain.

G. Rajeshkumar, A. Poovarasan, S.V. Naren Prasaadh et al.

Integrating natural fillers into polymer composites has been identified as a feasible and viable technique for improving performance of the material with preserving sustainability. In this work epoxy-based composites are fabricated by incorporating Phoenix sp. natural fillers in different weight proportions using compression moulding process and subjected to various experimental testings. The findings indicated that, the composites containing 10 wt.% of filler shows highest tensile, flexural and impact strengths, and vibration characteristics of 26 MPa, 47.6 MPa, 10.5 kJ/m2, and 24.16 Hz, respectively. In addition, morphological analysis of the mechanically tested samples provided insights into effective interfacial bonding. The thermal degradation analysis showed higher stability at 15 wt.% filler content, with reduced weight loss (75.43%) during the main degradation phase (stage-3) and the highest degradation temperature observed in the DTG curve is 391.9 °C. The hydrophilic property was noted in the water diffusion process, where an increase in filler content affects the water uptake rate of the composites. The Phoenix sp. filler added epoxy composites with increased mechanical, thermal and vibration characteristics could be employed in engineering applications such as roofing panels, insulation covers, and automotive and machine tool industries.

Mehdi Rahmaninia, Yasin Rahmati, Mehdi Tabarsa

Using environmentally friendly additives has been considered widely in different industries, especially papermaking, which has a high dependency on additives. The current study focused on applying a plant-based gum obtained from Astragalus gossypinus (a well-known plant in some regions of the world, especially Iran) in the papermaking process. The gum characterization showed a high content (about 84%) of carbohydrates (mainly hemicellulose with xyloarabinan monomers in the main chain and about 8% of uronic acid in the side chains), low ash content (2.58%) and insignificant protein content. FTIR spectra confirmed the structural results. The weight average molecular weight (Mw) and polydispersity of tragacanth gum were 4867 × 103 g/mol and 1.423, respectively. Considering the mechanical strengths results, applying the gum in recycled pulp improved tensile, burst, bending, and tear indices significantly. Moreover, fines retention experienced a significant increment by applying up to 2% of the gum. The pulp drainage decreased consequently by increasing the dosage of gum. The FESEM images confirmed the higher retention and bonding in paper structure by applying the gum. The results seem to open a new door for the application of different plant gums as a green additive for papermaking industries.

Richard Oladayo Olarewaju, Ayodeji Samson Olatunji Ogunjuyigbe, Temitope Raphael Ayodele et al.

In this study, ten wind turbines and fourteen solar photovoltaic (SPV) modules were employed to compare the potential of hydrogen production from wind and solar energy resources in the six geopolitical zones of Nigeria. The amount of hydrogen produced was considered as a technical parameter, cost of hydrogen production was considered as an economic index, and the amount of carbon (IV) oxide saved from the use of diesel fuel was considered as an environmental index. The results reveal that ENERCON E-40 turbine yields the highest capacity factor in Lagos, Jos, Sokoto, Bauchi and Enugu sites while FUHRLAENDER, GMBH yields the highest capacity factor in Delta. The mean annual hydrogen production from wind ranged from 2.05 tons/annum at site S6 (Delta) to 17.33 tons/annum at site S3 (Sokoto), and the mean annual hydrogen production from SPV ranged from 64.33 tons/annum at sites S1 (Lagos) to 140.28 tons/annum at site S6 (Delta). The cost of hydrogen production from wind was 6.3679 and 25.9007$/kg for sites S3 and S6, respectively, and the cost of hydrogen production from SPV was 5.6659 and 6.1206$/kg for sites S3 and S1, respectively. The amount of CO2 saved annually from wind-based hydrogen generation was 137,267 kg/year in site S6 and 504,180 kg/year in site S3, and was used to produce electricity via fuel cells. The amount of CO2 saved using hydrogen produced from SPV was 615,400 kg/year and 1,341,899 kg/year in sites S1 and S6, respectively. The results also revealed that 75.55%, 88.93%, 80.28%, 80.54%, 85.65%, 98.53% more hydrogen could be produced from SPV for sites S1–S6, respectively, compared to the wind resources. This study serves as a source of reliable technical information to relevant government agencies, policy makers and investors in making informed decisions on optimal investment in the hydrogen economy of Nigeria.

Sunawar khan, Tehseen Mazhar, Tariq Shahzad et al.

Introduction: In this study, an investigation of the nexus between state-of-the-art technology and green industrial processes with a view to how smart grid systems can be incorporated into industry 5.0 is done. Industry 5.0 stresses human-machine collaboration together with Artificial Intelligence, the Internet of Things, and Big Data while the recent electrical networks enriched by digital communication technologies are defined as the contemporary smart grids. Notwithstanding advances in both domains, there is a major research gap at the intersection of the two. Objectives: In this study, the essential elements, advantages, and potential impacts of coupling smart grids with Industry 5.0 will be examined. These aims are aimed at sustaining and improving the reliability and efficiency of industrial processes maximizing resource consumption and minimizing ecological damage. Method: ology: The use and benefits of this integration are analyzed using case studies from industrialized countries. It assesses technological developments, challenges and the emerging trends dealing with the combination of smart grid technologies with Industry 5.0. Findings and discussion: In addition, smart grid technology can make industrial processes more dependable and efficient; resulting in more appropriate resource utilization and lower emissions. It promises to revolutionize the energy management systems and production procedures. Conclusion: Drawing from this research, this integration offers the capabilities of developing a technologically advanced and environment-friendly industrial ecosystem that enables a truly sustainable future.

Kelly Joel Gurubel Tun, Elizabeth León-Becerril, Octavio García-Depraect

Dark fermentation process from low-cost renewable substrates for simultaneous wastewater treatment and hydrogen production (H2) is suitable due to economic viability and environmental sustainability. This work explores the application of an innovative control strategy in a scale fermentation bioreactor designed for energy recovery from organic wastes. This approach not only promotes low carbon emissions but also offers significant potential for industrial application. Machine learning (ML) and optimization methods are used to model the nonlinear process and then, a neural predictive control (NPC) strategy to drive the system to its optimal operating order under varying influent conditions is developed. Predictive control uses the Newton-Raphson as the optimization algorithm and a multi-layer feedforward neural network for the state prediction. This strategy has demonstrated to be a viable algorithm for real-time control applications. First, experimental data from continuous dark fermentation are modeled using support vector machine (SVM) algorithm for response prediction and then, optimization algorithms are employed to identify the key parameters that enhance H2 production. These optimal operating parameters are then used to create reference trajectory signals within a NPC scheme to achieve the optimal hydrogen production rate. The control strategy led to an HPR mean of 12.35 ± 1.2 NL H2/L-d under pseudo-steady state with hydrogen content in the gaseous phase of 63 % v/v, and a maximum COD recovery of 90% ± 2.8%. The results demonstrate that this innovative control method can significantly improve the performance and efficiency of biogas plants, showing viability for large-scale industrial implementation.

Siobhan Angus

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

Quantum Information Science and Engineering (QISE) education and workforce development are top priorities at the national level in the US. This has included a push for academia to support the development of programs that will prepare students to enter the QISE workforce. As the field of QISE has grown rapidly in academia and industry, there is a need to better understand what quantum knowledge is needed for students to be ready for the workforce. We present preliminary findings on the level of quantum expertise and the specific quantum knowledge utilized across different roles, and in the execution of specific tasks in the QISE industry. Qualitative analysis of semi-structured interviews with industry professionals elucidates these aspects of the vital work functions related to the ongoing development of quantum technologies in industry. This work will provide insights into QISE curriculum development and changes needed to better support students transitioning into this growing industry.

Xuan Zuo, Zi-Xu Lu, Jie Li

The strong coupling between light and matter gives rise to polaritons. Further coupling polaritons to phonons leads to the formation of hybrid polaromechanical systems. Recent experiments have achieved the strong coupling between polaritons and phonons in two configurations, namely, the magnon-photon-phonon and exciton-photon-phonon systems, which enables the control of mechanical motion via manipulating polaritons. Here, we present a polaromechanical cooling theory and explicitly show how two polaritons can be used to simultaneously cool two mechanical modes. The unique advantage of our protocol lies in the fact that the continuous tunability of the polariton frequencies over a wide range allows for the cooling of any two mechanical modes with their frequency difference falling within this range. We further discuss how to extend the theory to cool multiple mechanical modes. The protocol is designed for cooling mechanical motion in various emerging polaromechanical platforms, such as magnon-, exciton-, and plasmon-polaromechanical systems, which is the first step towards quantum states generation in these hybrid systems.

Lorenz Brehme, Benedikt Dornauer, Thomas Ströhle et al.

Retrieval-Augmented Generation (RAG) is a well-established and rapidly evolving field within AI that enhances the outputs of large language models by integrating relevant information retrieved from external knowledge sources. While industry adoption of RAG is now beginning, there is a significant lack of research on its practical application in industrial contexts. To address this gap, we conducted a semistructured interview study with 13 industry practitioners to explore the current state of RAG adoption in real-world settings. Our study investigates how companies apply RAG in practice, providing (1) an overview of industry use cases, (2) a consolidated list of system requirements, (3) key challenges and lessons learned from practical experiences, and (4) an analysis of current industry evaluation methods. Our main findings show that current RAG applications are mostly limited to domain-specific QA tasks, with systems still in prototype stages; industry requirements focus primarily on data protection, security, and quality, while issues such as ethics, bias, and scalability receive less attention; data preprocessing remains a key challenge, and system evaluation is predominantly conducted by humans rather than automated methods.

Jiali Zhou, Jiawei Zhang, Bo B. Iversen et al.

Serhat Acar, Kerem Altug Guler

Research into semi-solid metal forming techniques has been ongoing for an extended period with the aim of producing near-net shape products from aluminum alloys. The primary focus has traditionally been on casting alloys seeking to offer an alternative to the other processes such as high pressure die casting. Over the past twenty years, wrought aluminum alloys have also been explored for thixoforming operations as an alternative to traditional plastic deformation techniques. This research investigated the impacts of T6 heat treatment on the microstructural, mechanical, and corrosion properties of AA7075 alloy samples produced through cooling slope casting and subsequent thixoforming with different reheating durations. The selected alloy was chosen due to its widespread use in various industries, attributed to its excellent strength-to-density ratio achieved after T6 heat treatment. Building on optimized cooling slope casting parameters from a prior study, the standard solution treatment procedure following thixoforming with extended reheating times was found to be inadequate. Hardness and tensile tests revealed that specimens reheated for 120 min exhibited significantly reduced response to T6 treatment. Despite achieving the targeted hardness value of 154 HB with samples reheated for 20 min prior to thixoforming and T6 heat treatment, especially the elongation at break values were unsatisfactory. Potentiodynamic polarization tests indicated that corrosion primarily involved grain dissolution, with longer reheating times decreasing corrosion resistance due to increased amount of secondary phases and grain isolation. These findings highlight that while AA7075 alloy can be processed into semi-solid formable materials via cooling slope casting, issues such as hot tearing, micro-shrinkage, and physical defects post-thixoforming hinder the achievement of desired tensile properties.

Saket Maheshwari, Sambhav Tiwari, Shyam Rai et al.

In today's technology-driven era, the imperative for predictive maintenance and advanced diagnostics extends beyond aviation to encompass the identification of damages, failures, and operational defects in rotating and moving machines. Implementing such services not only curtails maintenance costs but also extends machine lifespan, ensuring heightened operational efficiency. Moreover, it serves as a preventive measure against potential accidents or catastrophic events. The advent of Artificial Intelligence (AI) has revolutionized maintenance across industries, enabling more accurate and efficient prediction and analysis of machine failures, thereby conserving time and resources. Our proposed study aims to delve into various machine learning classification techniques, including Support Vector Machine (SVM), Random Forest, Logistic Regression, and Convolutional Neural Network LSTM-Based, for predicting and analyzing machine performance. SVM classifies data into different categories based on their positions in a multidimensional space, while Random Forest employs ensemble learning to create multiple decision trees for classification. Logistic Regression predicts the probability of binary outcomes using input data. The primary objective of the study is to assess these algorithms' performance in predicting and analyzing machine performance, considering factors such as accuracy, precision, recall, and F1 score. The findings will aid maintenance experts in selecting the most suitable machine learning algorithm for effective prediction and analysis of machine performance.

Fei Liu, Zejun Kang, Xing Han

With the growing demand for offline PDF chatbots in automotive industrial production environments, optimizing the deployment of large language models (LLMs) in local, low-performance settings has become increasingly important. This study focuses on enhancing Retrieval-Augmented Generation (RAG) techniques for processing complex automotive industry documents using locally deployed Ollama models. Based on the Langchain framework, we propose a multi-dimensional optimization approach for Ollama's local RAG implementation. Our method addresses key challenges in automotive document processing, including multi-column layouts and technical specifications. We introduce improvements in PDF processing, retrieval mechanisms, and context compression, tailored to the unique characteristics of automotive industry documents. Additionally, we design custom classes supporting embedding pipelines and an agent supporting self-RAG based on LangGraph best practices. To evaluate our approach, we constructed a proprietary dataset comprising typical automotive industry documents, including technical reports and corporate regulations. We compared our optimized RAG model and self-RAG agent against a naive RAG baseline across three datasets: our automotive industry dataset, QReCC, and CoQA. Results demonstrate significant improvements in context precision, context recall, answer relevancy, and faithfulness, with particularly notable performance on the automotive industry dataset. Our optimization scheme provides an effective solution for deploying local RAG systems in the automotive sector, addressing the specific needs of PDF chatbots in industrial production environments. This research has important implications for advancing information processing and intelligent production in the automotive industry.

Idoia Berges, Víctor Julio Ramírez-Durán, Arantza Illarramendi

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.

Rutian Li, Rutian Li, Shuisheng Zeng et al.

Mechanical grinding is a facile method to get silica aerogels (SAs) with various particle sizes. However, the relationship between the grinding parameters and physicochemical properties is still unclear. In this study, we concentrated on the effects of grinding time and grinding speed on the physical and chemical properties of silica aerogels. The results reveal that the physicochemical properties of silica aerogels are more sensitive to the grinding speed rather than the grinding time. When the grinding speed is over 200 rpm, large particles of silica aerogels are crushed into smaller particles with obviously decreasing particle sizes and the silica skeletons of silica aerogels have collapsed. The reduction of particle sizes and the collapse of skeleton lead to an increase in both the tap density and thermal conductivity. Therein, the thermal conductivity is positively proportional to the density. Furthermore, the grinded silica aerogels powders still possess the contact angles over 135°, indicating the good hydrophobicity. All these demonstrate that the mechanical grinding has evident effects on the microstructure, density, thermal conductivity and particle sizes, which further impact the performance of silica aerogels during the practical applications. Given the expanding applications of SAs across various industries, the study serves as a valuable reference for optimizing the mechanical post-treatment of SAs, facilitating the achievement of desired particle sizes. Ultimately, this research holds great potential in diversifying the application fields of SAs in their powdered form.

Daniel Trujillo-Ramírez, Ma. Guadalupe Bustos-Vázquez, Alejandro Martínez-Velasco et al.

Background. The Conventional use of henequen (Agave fourcroydes), has mainly focused on the use of the leaves for the production of fiber. However, there are other components such as the stem (“pineapple”), the spines, and the by-product of fiber generation (leaf juice) in which we should pay attention to. Objective. To provide a systematic analysis of the biotechnological overview from those investigations where the potential of each of the structural components of A. fourcroydes is being studied. Methodology. A systematic review of the literature was carried out, based on the PRISMA protocol (Preferred Reporting Items for Systematic reviews and Meta-Analyses), search for information was carried out in the most prominent databases (Redalyc, SciELO, Scopus, Elsevier, EBSCO, and Google Academic, using A. fourcroydes as the main keyword, using inclusion and later exclusion criteria according to the literature found, in the period from 1990 to 2022, which allowed a broader perspective on this crop and its biotechnological importance. Main findings. In the bibliographic review more information was found on the applications of the plant in an integral way, so that bioactive compounds such as fructans, flavonoids, and sterols can be obtained from the henequen stem, which can be incorporated into animal and human diets, while ethanol has been obtained from the juice of the leaves and the development of that of new materials using the fiber in a native and modified way to obtain fiber-reinforced mortars for its sustainable application in the construction industry. On the other hand, contributions were found on promising alternatives for the use of crops such as modified fibers, and combined with other compounds (composites) for the mechanical reinforcement of new materials. Implications. The literature consulted allows us to report that henequen (A. fourcroydes) is not only cultivated in the Yucatan Peninsula, but also in other regions such as the State of Tamaulipas, Mexico, where its use and commercial exploitation has not well documented. Conclusion. The bibliographical review allows us to deduce that the obtaining of new henequen compounds would revalue their integral use and use in different industries.

Eric Wagner, David Heye, Martin Serror et al.

Industrial control systems increasingly rely on middlebox functionality such as intrusion detection or in-network processing. However, traditional end-to-end security protocols interfere with the necessary access to in-flight data. While recent work on middlebox-aware end-to-end security protocols for the traditional Internet promises to address the dilemma between end-to-end security guarantees and middleboxes, the current state-of-the-art lacks critical features for industrial communication. Most importantly, industrial settings require fine-grained access control for middleboxes to truly operate in a least-privilege mode. Likewise, advanced applications even require that middleboxes can inject specific messages (e.g., emergency shutdowns). Meanwhile, industrial scenarios often expose tight latency and bandwidth constraints not found in the traditional Internet. As the current state-of-the-art misses critical features, we propose Middlebox-aware DTLS (Madtls), a middlebox-aware end-to-end security protocol specifically tailored to the needs of industrial networks. Madtls provides bit-level read and write access control of middleboxes to communicated data with minimal bandwidth and processing overhead, even on constrained hardware.

Feng Dong, Shaofei Li, Peng Jiang et al.

Provenance-Based Endpoint Detection and Response (P-EDR) systems are deemed crucial for future APT defenses. Despite the fact that numerous new techniques to improve P-EDR systems have been proposed in academia, it is still unclear whether the industry will adopt P-EDR systems and what improvements the industry desires for P-EDR systems. To this end, we conduct the first set of systematic studies on the effectiveness and the limitations of P-EDR systems. Our study consists of four components: a one-to-one interview, an online questionnaire study, a survey of the relevant literature, and a systematic measurement study. Our research indicates that all industry experts consider P-EDR systems to be more effective than conventional Endpoint Detection and Response (EDR) systems. However, industry experts are concerned about the operating cost of P-EDR systems. In addition, our research reveals three significant gaps between academia and industry: (1) overlooking client-side overhead; (2) imbalanced alarm triage cost and interpretation cost; and (3) excessive server-side memory consumption. This paper's findings provide objective data on the effectiveness of P-EDR systems and how much improvements are needed to adopt P-EDR systems in industry.