Hasil untuk "Mechanical industries"

Annette Cowie, Kati Koponen, Anthony Benoist et al.

ABSTRACT Bioenergy is a critical element in many national and international climate change mitigation efforts, including as a carbon dioxide removal strategy combined with the capture and durable geological storage of flue gas emissions (BECCS). However, divergent results on the effectiveness of bioenergy as a climate change mitigation measure are reported in the scientific literature. Climate impacts of bioenergy depend on case‐specific factors, primarily biophysical features of the biomass production system, and the design and efficiency of conversion and capture processes. Estimates of climate impacts are also strongly affected by methodological choices and assumptions, and much of the divergence between studies derives from differences in the assumed alternate use of the land or feedstock, the alternate energy source and the system boundaries applied. We present a methodology to support robust estimates of the climate change effects of bioenergy systems, updating the standard methodology developed by the International Energy Agency's Technology Collaboration Program on Bioenergy. We provide guidance on the key choices including the reference land use and energy system that bioenergy is assumed to displace, spatial and temporal system boundaries, co‐product handling, climate forcers considered, metrics applied and time horizon of impact assessment. Researchers should consider the whole bioenergy system including all life cycle stages, and choose system boundaries, reference systems and treatment of co‐products that are consistent with the intended application of the results. The assessment should be normalised to a functional unit that can be compared with other systems delivering an equivalent quantity of the same function. All significant climate forcers should be included, and climate effects should be quantified using appropriate impact assessment methods that distinguish the impact of time. Consistency in methodology and interpretation will facilitate comparison between studies of different bioenergy systems.

Clemens Wolf, Janine Maier, Julia Wenger et al.

Abstract Background Sustainability aspects have become a main criterion for design next to performance of material and product. Particularly the emerging field of energy storage and conversion is striving towards more sustainable solutions. However, implementing sustainability considerations during the design and development phase of energy materials and products is challenging due to the complexity and broadness of the different dimensions of sustainability. Results Here, we demonstrate that by using the principles of Safe-and-Sustainable-by-Design (SSbD), a concept can be formulated. This concept served as the basis for selecting and evaluating criteria and performance parameters aimed at enhancing the safety and sustainability aspects of redox active molecules in an organic redox flow battery. Following an iterative approach, the collected data provided valuable insights enabling us to fine-tune and enhance the materials and processes in alignment with the identified parameters. (Social) life cycle assessment focused on the workflow from sourcing, processing and generation of intermediate products to the quinone used in the redox flow batteries and revealed important insights, highlighting critical steps in the process chain. Additionally, we identified two specific points of intervention regarding solvent and quinone choice, based on sustainability parameters. The proposed solvent change resulted in a greener alternative [changed from tetrahydrofuran (THF) to 2-methyl-tetrahydrofuran (MTHF)], and the ecotoxicity testing revealed MGQ and MHQS to be improved options. However, we also faced severe challenges regarding access to reliable LCA data on the raw material sourcing. Conclusion Taken together, the modified designs led to safer and more sustainable redox active materials for both humans and the environment at lab scale. Implementing the results mentioned above to further expedite the technology will ultimately pave the way to more sustainable energy storage applications. This study proved the value of implementing of an SSbD concept in battery development is the main result of this study.

L.G. Sulyubayeva, D.B. Buitkenov, D.R. Baizhan et al.

The article investigates the influence of thermocyclic electrolytic-plasma treatment (EPT) on the mechanical properties of carbon steel U9 tool steel. U9 steel is often used for manufacturing tools working in conditions that do not cause edge heating: woodworking tools, assembly tools, gauges of simple shape and reduced accuracy classes. In this work, thermocyclic electrolytic-plasma treatment was used as a method of improving mechanical properties. This method combines electrochemical reactions and intensive thermal influence, which allows the formation of surface layers with improved characteristics. As a result of the treatment, U9 steel shows a clear division of the microstructure into three zones: hardened layer, transition layer and base metal. The hardened layer, located up to a depth of 400 μm, is characterized by a finely dispersed structure consisting of martensite and bainite with high hardness (1400–1600 HV₀.₁). This layer provides excellent wear resistance and resistance to mechanical stress. The transition layer (400–700 µm) serves as a buffer zone, distributing stresses evenly. It is characterized by a gradual decrease in hardness (800–1200 HV₀.₁) and a change in structure due to a decrease in martensite content. The base metal, deeper than 700 μm, retains the original structure with hardness 400–600 HV₀.₁, which ensures its ductility and durability. The results show that thermocyclic EPT significantly improves the performance properties of U9 steel by creating a functionally gradient structure. The technology is energy efficient and can be widely used in mechanical engineering and other industries where high mechanical characteristics of materials are required.

Amanda Ronix, Eduardo Carvalho daSilva Neto, Carlos Eduardo Pellegrino Cerri et al.

ABSTRACT In the last two decades, several studies have utilized biogeochemical models to evaluate the impact of different edaphoclimatic conditions on soil carbon storage and the dynamics of soil organic carbon. At the same time, biochar, a carbon‐rich material obtained from the pyrolysis of biomass residues, has been identified as a promising carbon sequestration material. However, current models do not adequately incorporate the role of biochar in soil management. In this context, the current state of research on biogeochemical models that include the entry of biochar into soil has been characterized. The research indicated that the development of studies on the topic “biochar” is widely explored, with 4259 papers being identified using the first search filter. Specifically, searching for studies that mentioned terms related to biogeochemical models for estimating soil carbon stock, it was observed that a small number of the studies (N = 46) considered the entry of biochar into the models. Although most studies have used the RothC model to simulate biochar within biogeochemical models, biochar inputs have also been implemented in APSIM, EPIC, Century, DNDC, and other models, including those not primarily focused on soil carbon stock estimation. Among these studies, the minority included the results of calibration and validation of the models, which are paramount for the model's credibility. Therefore, efforts must be concentrated on solving the lack of valuable data to validate the models. Data from long‐term field experiments that consider interactions between crop and climate conditions are highly desirable. The possibility of increasing carbon stocks by incorporating biochar into the soil could promote environmental and financial gains, and biogeochemical models that consider the incorporation of biochar are valuable tools for decision‐makers.

Kessia Nepomuceno, Fabio Petrillo

Context: Fairness in systems has emerged as a critical concern in software engineering, garnering increasing attention as the field has advanced in recent years. While several guidelines have been proposed to address fairness, achieving a comprehensive understanding of research solutions for ensuring fairness in software systems remains challenging. Objectives: This paper presents a systematic literature mapping to explore and categorize current advancements in fairness solutions within software engineering, focusing on three key dimensions: research trends, research focus, and viability in industrial contexts. Methods: We develop a classification framework to organize research on software fairness from a fresh perspective, applying it to 95 selected studies and analyzing their potential for industrial adoption. Results: Our findings reveal that software fairness research is expanding, yet it remains heavily focused on methods and algorithms. It primarily focuses on post-processing and group fairness, with less emphasis on early-stage interventions, individual fairness metrics, and understanding bias root causes. Additionally fairness research remains largely academic, with limited industry collaboration and low to medium Technology Readiness Level (TRL), indicating that industrial transferability remains distant. Conclusion: Our results underscore the need to incorporate fairness considerations across all stages of the software development life-cycle and to foster greater collaboration between academia and industry. This analysis provides a comprehensive overview of the field, offering a foundation to guide future research and practical applications of fairness in software systems.

Muhammad Junaid Asif, Abdul Rehman, Asim Mehmood et al.

This research proposes an extensive technique for monitoring and controlling the industrial parameters using Internet of Things (IoT) technology based on wireless communication. We proposed a system based on NRF transceivers to establish a strong Wireless Sensor Network (WSN), enabling transfer of real-time data from multiple sensors to a central setup that is driven by ARDUINO microcontrollers. Different key parameters, crucial for industrial setup such as temperature, humidity, soil moisture and fire detection, are monitored and displayed on an LCD screen, enabling factory administration to oversee the industrial operations remotely over the internet. Our proposed system bypasses the need for physical presence for monitoring by addressing the shortcomings of conventional wired communication systems. Other than monitoring, there is an additional feature to remotely control these parameters by controlling the speed of DC motors through online commands. Given the rising incidence of industrial fires over the worldwide between 2020 and 2024 due to an array of hazards, this system with dual functionality boosts the overall operational efficiency and safety. This overall integration of IoT and Wireless Sensor Network (WSN) reduces the potential risks linked with physical monitoring, providing rapid responses in emergency scenarios, including the activation of firefighting equipment. The results show that innovations in wireless communication perform an integral part in industrial process automation and safety, paving the way to more intelligent and responsive operating environments. Overall, this study highlights the potential for change of IoT-enabled systems to revolutionize monitoring and control in a variety of industrial applications, resulting in increased productivity and safety.

V. Sanchez Padilla, Albert Espinal, Jennifer M. Case et al.

ABET accreditation is an increasingly prominent system of global accreditation of engineering programs, and the assessment requires programs to demonstrate that they meet the needs of the program's stakeholders, typically industrial potential employers of graduates. To obtain these inputs, programs are required to assemble an advisory committee board. The views of the advisory board on the relevance of the degree outcomes are an essential part of this process. The purpose of this qualitative research study is to explore the viewpoints that industry stakeholders have on this type of process. The context for the study was an Ecuadorian engineering program which had successfully achieved the ABET accreditation. The study drew on interviews undertaken with industry members who were part of the advisory board. This study focuses on how they perceive the process and the accreditation awarded, analyzing their views of its usefulness, especially in relation to the employability of graduates. Based on the findings, we offer critical insights into this accreditation process when it takes place in contexts beyond highly industrialized countries.

Ali Mohammad-Djafari

Digital Twins (DTs) are virtual representations of physical systems synchronized in real time through Internet of Things (IoT) sensors and computational models. In industrial applications, DTs enable predictive maintenance, fault diagnosis, and process optimization. This paper explores the mathematical foundations of DTs, hybrid modeling techniques, including Physics Informed Neural Networks (PINNs), and their implementation in industrial scenarios. We present key applications, computational tools, and future research directions.

Unnati Chaudhary, Shuank Malik, Vikas Rana et al.

Bamboo, a member of the grass family, is a fast growing and high yielding renewable resource. The popularity of bamboo has risen in recent times owing to its multifaceted and myriad of practical applications. There are unlimited uses of bamboo, therefore proper utilization of this beneficial resource will be useful to the industrial sector. This review provides a comprehensive summary regarding the potential of bamboo as a vital non-wood fibrous raw material for pulp, paper and other allied industries owing to its ability to conserve forests and foster sustainable economic development. Bamboo's abundance, renewability, mechanical strength, and other functional features make it an appealing and potential building biomaterial in the pursuit of sustainable raw materials needed for industrial development. This review provides an in-depth summary and unique perspective on the application of bamboo at a commercial scale in various sectors, thereby encouraging the utilization of this potential material towards environmental sustainability and economic growth.

Ruitian Fan, Xing Lei, Tao Jia et al.

Amidst the swift advancement of new power systems and electric vehicles, inverter-fed machines have progressively materialized as a pivotal apparatus for efficient energy conversion. Stator winding turn insulation failure is the root cause of inverter-fed machine breakdown. The online monitoring of turn insulation health can detect potential safety risks promptly, but faces the challenge of weak characteristics of turn insulation degradation. This study proposes an innovative method to evaluate the turn insulation state of inverter-fed machines by utilizing the fractional Fourier transform with a Mel filter (FrFT-Mel). First, the sensitivity of the high-frequency (HF) switching oscillation current to variations in turn insulation was analyzed within the fractional domain. Subsequently, an improved Mel filter is introduced, and its structure and parameters are specifically designed based on the features intrinsic to the common-mode impedance resonance point of the electrical machine. Finally, an evaluation index was proposed for the turn insulation state of inverter-fed machines. Experimental results on a 3kW permanent magnet synchronous machine (PMSM) demonstrate that the proposed FrFT-Mel method significantly enhances the sensitivity of turn insulation state perception by approximately five times, compared to the traditional Fourier transform method.

Johanne Lebrun Thauront, Gerhard Soja, Hans‐Peter Schmidt et al.

Abstract Biochar is the product of intentional pyrolysis of organic feedstocks. It is made under controlled conditions in order to achieve desired physico‐chemical characteristics. These characteristics ultimately affect biochar properties as a soil amendment. When biochar is used for carbon storage, an important property is its persistence in soil, often described by the proportion of biochar carbon remaining in soil after a 100 years (Fperm). We analyzed published data on 1230 biochars to re‐evaluate the effect of pyrolysis parameters on biochar characteristics and the possibility to predict Fperm from the maximum temperature reached during pyrolysis (HTT). We showed that biochar ash and nitrogen (N) contents were mostly affected by feedstock type. The oxygen to carbon (O:C) and hydrogen to carbon (H:C) ratios were mostly affected by the extent of pyrolysis (a combination of HTT and pyrolysis duration), except for non (ligno)cellulosic feedstocks (plastic waste, sewage sludge). The volatile matter (VM) content was affected by both feedstock type and the extent of pyrolysis. We demonstrated that HTT is the main driver of H:C ‐‐ an indicator of persistence ‐‐ but that it is not measured accurately enough to precisely predict H:C, let alone persistence. We examined the equations to estimate Fperm available in the literature and showed that Fperm calculated from HTT presented little agreement with Fperm calculated from H:C. The sign and magnitude of the bias depended on the equation used to calculate Fperm and the dispersion was usually large. This could lead to improper compensation of carbon emissions and wrong reporting of carbon sinks in national carbon accounting schemes. We recommend not to use HTT as a predictor for persistence and stress the importance to rapidly develop more accurate proxies of biochar C persistence in soil.

Tanureza Jaya, Benjamin Michalak, Marcel Radke et al.

Cabling tasks (pulling, clipping, and plug insertion) are today mostly manual work, limiting the cost-effectiveness of electrification. Feasibility for the robotic grasping and insertion of plugs, as well as the manipulation of cables, have been shown in research settings. However, in many industrial tasks the complete process from picking, insertion, routing, and validation must be solved with one system. This often means the cable must be directly manipulated for routing, and the plug must be manipulated for insertion, often in cluttered environments with tight space constraints. Here we introduce an analysis of the complete industrial cabling tasks and demonstrate a solution from grasp, plug insertion, clipping, and final plug insertion. Industrial requirements are summarized, considering the space limitations, tolerances, and possible ways that the cabling process can be integrated into the production process. This paper proposes gripper designs and general robotic assembly methods for the widely used FASTON and a cubical industrial connector. The proposed methods cover the cable gripping, handling, routing, and inserting processes of the connector. Customized grippers are designed to ensure the reliable gripping of the plugs and the pulling and manipulation of the cable segments. A passive component to correct the cable orientation is proposed, allowing the robot to re-grip the plug before insertion. In general, the proposed method can perform cable assembly with mere position control, foregoing complex control approaches. This solution is demonstrated with an industrial product with realistic space requirements and tolerances, identifying difficult aspects of current cabling scenarios and potential to improve the automation-friendliness in the product design.

Kun Qian, Tianyu Sun, Wenhong Wang

Industrial anomaly detection (IAD) plays a crucial role in the maintenance and quality control of manufacturing processes. In this paper, we propose a novel approach, Vision-Language Anomaly Detection via Contrastive Cross-Modal Training (CLAD), which leverages large vision-language models (LVLMs) to improve both anomaly detection and localization in industrial settings. CLAD aligns visual and textual features into a shared embedding space using contrastive learning, ensuring that normal instances are grouped together while anomalies are pushed apart. Through extensive experiments on two benchmark industrial datasets, MVTec-AD and VisA, we demonstrate that CLAD outperforms state-of-the-art methods in both image-level anomaly detection and pixel-level anomaly localization. Additionally, we provide ablation studies and human evaluation to validate the importance of key components in our method. Our approach not only achieves superior performance but also enhances interpretability by accurately localizing anomalies, making it a promising solution for real-world industrial applications.

Benjamin Alt, Julia Dvorak, Darko Katic et al.

Over the past decade, deep learning helped solve manipulation problems across all domains of robotics. At the same time, industrial robots continue to be programmed overwhelmingly using traditional program representations and interfaces. This paper undertakes an analysis of this "AI adoption gap" from an industry practitioner's perspective. In response, we propose the BANSAI approach (Bridging the AI Adoption Gap via Neurosymbolic AI). It systematically leverages principles of neurosymbolic AI to establish data-driven, subsymbolic program synthesis and optimization in modern industrial robot programming workflow. BANSAI conceptually unites several lines of prior research and proposes a path toward practical, real-world validation.

Marcos Fernando de Oliveira Filho, Pierre D'Amelio Briquet Caradec, Rafael Calsaverini et al.

The influence of MnS inclusions on steel properties is highly noticeable. For instance, higher severity levels of inclusions are associated with lower mechanical properties and a higher risk of failure in service. Manual inclusions classification methods are the most used in laboratories and metallurgical sector industries because of their low cost, while automatic methods have high operating costs, which makes their use more restrict. Neural network models, on the other hand, are extremely advantageous for several applications. The present study is motivated by the use of a neural network model for classifying inclusions in steels. The aim is to achieve the highest possible accuracy in classifying the MnS inclusions severities (0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, and 4.5) using optical images captured from steel specimens. The results have showed that the classification of MnS severities was very sensitive to the database number of images. A 98% training accuracy was obtained by increasing the number of images from 3,156 to 4,136, mostly adding images for some severity levels. However, validation and test results were not satisfactory. As such, a severity re-categorization of the database was able to enhance the true positive values, with an error of 8%. In general, the neural network represented speed in decision making, proving to be a potential tool for classifying steel inclusions.

Sungbong Kim, Hyeong Ryeol Kim, Hyun Ju Oh et al.

Abstract Xylose‐rich undefined broth, extracted from the dilute acid pretreatment wastes of barley straw, serves as resourceful media for Acremonium chrysogenum M35 culture and production of cephalosporin C (CPC). Concentrating the extract with proper reprocessing enables to prepare various concentrations of xylose broth (2%–8%). The undefined xylose media were prepared for CPC production from A. chrysogenum M35 by the addition of other nutrients. Cell growth and CPC production were the most effective at 6% xylose and additional 2% glycerol, with maximum CPC production of 9.07 g/L after 6 days, which is higher production than that in defined media prepared with laboratory‐level nutrients and reagents. Investigation of autotrophic and reverse trans‐sulfuration pathways for cysteine synthesis, a limited element of three precursors for CPC synthesis, supports the enhanced CPC production in undefined media. Abundance of xylose ensures a maintained NADPH concentration required for sulfate reduction and synthesis of amino sulfide such as cysteine. Cystathionine‐γ‐lyase activity profiling indicated more efficient biosynthesis in undefined media than in other cultures use glycerol and glucose, and the biosynthesis pathway of CPC production by the cephalosporin gene cluster (i.e. pcbC and cefG genes) was investigated. The process using undefined xylose media was designed, and process simulation program confirmed our results.

Rakesh Bhaskaran Nair, Raunak Supekar, Seyyed Morteza Javid et al.

Thermal spray deposition techniques have been well-established, owing to their flexibility in addressing degradation due to wear and corrosion issues faced due to extreme environmental conditions. With the adoption of these techniques, a broad spectrum of industries is experiencing continuous improvement in resolving these issues. To increase industrial-level implementation, state-of-the-art advanced materials are required. High-entropy alloys (HEAs) have recently gained considerable attention within the scientific community as advanced materials, mainly due to their exceptional properties and desirable microstructural features. Unlike traditional material systems, high-entropy alloys are composed of multi-component elements (at least five elements) with equimolar or nearly equimolar concentrations. This allows for a stable microstructure that is associated with high configurational entropy. This review article provides a critical assessment of different strengthening mechanisms observed in various high-entropy alloys developed by means of deposition techniques. The wear, corrosion, and oxidation responses of these alloys are reviewed in detail and correlated to microstructural and mechanical properties and behavior. In addition, the review focused on material design principles for developing next-generation HEAs that can significantly benefit the aerospace, marine, oil and gas, nuclear sector, etc. Despite having shown exceptional mechanical properties, the article describes the need to further evaluate the tribological behavior of these HEAs in order to show proof-of-concept perspectives for several industrial applications in extreme environments.

Yanhui Xu, Haowei Chen

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer (PEMEL) under the fluctuating input of renewable energy, this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic (PV) electrolysis of water. First, voltage and performance attenuation models of the PEMEL are proposed, and the degradation cost of the electrolyzer under a fluctuating input is considered. Then, the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost. Finally, a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system. In the up-layer optimization, the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi. In low-layer optimization, the power allocation between the PEMEL and battery energy storage system (BESS) is optimized using a non-dominated sorting genetic algorithm (NSGA-II) combined with the firefly algorithm (FA). A better optimization result, characterized by lower degradation and total costs, was obtained using the method proposed in this study. The improved algorithm can search for a better population and obtain optimization results in fewer iterations. As a calculation example, data from a PV power station in northwest China were used for optimization, and the effectiveness and rationality of the proposed optimization method were verified.

Mfanafuthi Mthandeni Mkhize, Velaphi Msomi

Abstract The current study complements a broader body of research on solar distillation, including research on heat recycling capabilities and other related factors in multistage solar distillation systems. Solar stills can be used in various applications to provide safe and clean water from natural sources. This study is based on field data collected, analysed, and interpreted over ten (10) months. The solar still operated at atmospheric pressure and produced a distillate by evaporating saline water (SW) at ~ 100 °C. The maximum SW preheating was 75.5 °C with 30,821.04 kJ/m2 day collected by the solar collectors. The corresponding overall thermal efficiency of the test rig was 33.83%. The overall thermal efficiency decreased with increasing wind speed, averaging at 3.12 m/s to 28.31% due to increased heat loss to the environment when 30,780 kJ/m2day was collected. It further declined to 5.89% with low meteorological conditions of 209.81 W/m2, 15.66 °C and 2.66 m/s, respectively, on average. However, the benefits of increased wind speed were enhanced condensation and productivity. The study also found that the ideal thermal energy delivery rate was $$\sim$$ ∼  600 W/m2 or an impulsive mode at higher solar insolation. A balanced condensation rate, SW preheating, heat recovery and overall thermal efficiency can be achieved at this delivery rate. A significant correlation was observed, indicating that the simultaneous increase in the average heat input rate and wind velocities positively impacted distillate output. Conversely, low average wind velocity improved overall thermal efficiency, resulting in a distillate output of 6730 ml for the five stacked stages, despite a slight discrepancy of 3.2 W/m2 in the heat input rate.

Xiangfeng Zhou, Chunyuan Cai, Yongjian Li et al.

To accommodate wind power as safely as possible and deal with the uncertainties of the output power of wind- driven generators, a min-max-min two-stage robust optimization model is presented, considering the unit commitment, source-network load collaboration, and control of the load demand response. After the constraint functions are linearized, the original problem is decomposed into the main problem and subproblem as a matrix using the strong dual method. The minimum-maximum of the original problem was continuously maximized using the iterative method, and the optimal solution was finally obtained. The constraint conditions expressed by the matrix may reduce the calculation time, and the upper and lower boundaries of the original problem may rapidly converge. The results of the example show that the injected nodes of the wind farms in the power grid should be selected appropriately; otherwise, it is easy to cause excessive accommodation of wind power at some nodes, leading to a surge in reserve costs and the load demand response is continuously optimized to reduce the inverse peak regulation characteristics of wind power. Thus, the most economical optimization scheme for the worst scenario of the output power of the generators is obtained, which proves the economy and reliability of the two-stage robust optimization method.