Hasil untuk "Mechanical industries"

Afifah Z. Juri, Xiao-Fei Song, James Dudley et al.

Microstructures, mechanical properties and clinical processes significantly affect the surface quality of monolithic zirconia restorations. This study aims to understand the microstructure-property-processing-quality relations for the fabrication of monolithic zirconia restorations. Zirconia microstructures were characterized using scanning electron microscopy (SEM). Their mechanical properties associated with clinical processes were investigated using in-situ SEM nanoindentation techniques. Zirconia materials were subjected to CAD/CAM milling, sintering, polishing of exterior surfaces for oral function and sandblasting of intaglio surfaces for adhesion. Surface quality was measured using 3D optical profilometry. The two distinct porous (pre-sintered) and dense (sintered) microstructures yielded distinct maximum indentation depths, hardness H, the elastic moduli E, and indentation volumes. CAD/CAM milled pre-sintered zirconia had poor surface quality because its porous microstructure resulted in lower H/E and H3/E2 ratios, deeper indentation depths and volumes. Sintering diminished the average roughness of milled pre-sintered surfaces but did not heal the milling-induced peak and valley heights. Meanwhile, sintering-induced densification and grain growth increased H/E and H3/E2 ratios of sintered zirconia, making it difficult to polish. Polishing and subsequent sintering of the milled surfaces significantly improved the surface quality, achieving the lowest average roughness, peak and valley heights, and peak-to-valley heights, near zero skewness and reduced kurtosis. However, the lowest peak material portions and inverse areal material ratios obtained may compromize saliva retention and load-bearing capacity. Sandblasting of CAD/CAM milled-sintered surfaces with coarse abrasives achieved rougher surfaces than with finer abrasives. These microstructure-property-processing-surface quality relations provide technical insights into process selection and development for reliable monolithic zirconia restorations.

Ige Samuel Ayeni, Shek Poi Ngian, Omolade Regina Olulope et al.

The mechanical and microstructural responses of ordinary Portland cement (OPC) and one-part geopolymer concrete (OPGC) to fibre reinforcement have not been scientifically explored, hence generating a research gap. As more construction industries seek high-performance and ecologically friendly building materials, geopolymer concrete is becoming more popular as an alternative to OPC. Because it does not need liquid activators, a one-part geopolymer system is advantageous, and adding fibres increases its tensile and flexural strength. Despite these benefits, a comprehensive evaluation of its strength performance in comparison to conventional OPC concretes requires more research. Four concrete mixes, which are ordinary Portland cement (OPC), fibre-reinforced ordinary Portland cement (FROPC), one-part geopolymer concrete (OPGC), and fibre-reinforced one-part geopolymer concrete (FROPGC), are examined in this study for their fresh, mechanical, and microstructural characteristics. Workability was assessed using slump tests, and at 7, 14, and 28 days, compressive, flexural, and split tensile strengths were measured. Stiffness, permeability, and internal quality were evaluated using the modulus of elasticity, water absorption, and ultrasonic pulse velocity (UPV), and microstructural examination was conducted using scanning electron microscopy (SEM). According to the findings, geopolymer concretes had better fresh qualities than OPC, with slump that were 20–31% higher. While fibre insertion greatly increased tensile and flexural strengths, it decreased workability. The highest compressive strength (63.78 MPa) was obtained by OPGC, whereas the highest flexural (15.1 MPa) and tensile (9.15 MPa) strengths were attained by FROPGC. Additionally, FROPGC showed the highest modulus of elasticity (44,040 N/mm2), and a refined microstructure with fewer vacancies and well-bonded fibres was shown in FROPC and FROPGC.

Zhongmeng Zhu, Chutian Li, Jun Ma et al.

Polylactic acid (PLA) is widely utilized across various fields for its excellent biocompatibility and mechanical properties. However, inevitable sharp-object contacts cause surface scratches, hindering performance optimization, owing to the poorly characterized in situ damage processes and internal mechanisms. Our research addresses this gap through a novel integration of in situ observation and polarized light imaging, systematically investigating PLA scratch behavior under linearly increasing normal loads. The brittle PLA exhibits unexpected ductile damage characteristics under scratching: pronounced plastic deformation beneath the scratch tip at low normal loads, periodic 30°-angled crack propagation under moderate loads linked to scratch-induced high temperature rise, and lateral tearing with circumferential debris formation at high loads. These findings reveal a ductile-dominated scratch mechanism of brittle PLA, providing important guidance for understanding the damage mechanism of PLA and enhancing its practical application in diverse industries.

Constance Nakato Nakimuli, Fred Kaggwa, Johan De Greef et al.

This review discusses how Machine Learning has been applied to predict the quality of biomass briquettes produced from agricultural and municipal solid organic waste, which are crucial for advancing green and low-carbon energy solutions. Traditional methods of assessment of briquette quality involve destructive laboratory experiments, do not favor sample reuse, are time-consuming, and labor-intensive, posing barriers to efficient production. This paper reviews literature on various Machine Learning models applied for predicting and optimizing briquette quality parameters, including combustion, physical, and emission properties. Several Machine Learning models have shown promising results in predicting and optimizing these key parameters for example, a Random Forest model with R2 of 0.9936 in deformation energy prediction and Artificial Neural Networks with R2 of 0.8936 in the prediction of impact resistance. By enhancing the accuracy and efficiency of briquette quality predictions, Machine Learning algorithms contribute to the development of high-quality biomass briquettes, thereby creating sustainable and low-carbon energy systems. This review points to critical literature gaps regarding model generalizability across diverse biomass feedstocks and integration of broader quality parameters. Addressing these gaps will advance AI-based solutions, promote greener energy practices, and support sustainable development. The findings are intended to aid researchers, industry professionals, and policymakers in advancing the production of high-quality biomass briquettes for cleaner energy and sustainable development.

Anna Sitko, Przemysław Gnatowski, Edyta Piłat-Wójcik et al.

Natural rubber latex (NRL), derived from Hevea brasiliensis, is a renewable material widely used in numerous industries due to its excellent elasticity and film-forming properties. However, elastomers obtained from vulcanized NRL tend to resist biodegradation, which, especially in disposable products like i.e. balloons, poses environmental concerns. Therefore, developing strategies to enhance NRL biodegradability without compromising its functional performance is of increasing importance. This study presents the novel approach for NRL functionalization by using small-molecule additives: sorbitol, urea, and glycerol monostearate (each at 1 phr) to improve its compostability, while maintaining mechanical and application properties. The modified and unmodified latex films were subjected to degradation in simulated home composting conditions, and changes in mass, surface morphology, mechanical properties, and thermal stability throughout the composting process were analyzed using scanning electron microscopy (SEM), tensile testing, dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA). Additionally, the microbial biodiversity in composting matter used for unmodified and modified latex degradation was analyzed. Among the tested additives, sorbitol demonstrated the most promising performance, yielding a homogenous material structure, maintaining tensile strength prior to composting, and significantly accelerating biodegradation in first weeks, evidenced by a substantial decrease in tensile strength and high mass loss. These findings suggest a viable pathway sufficient for large scale production of biodegradable NRL-based products with minimal loss in performance.

K. Senthil Kumar, R. Vasanthi, Mahdi Sh. Jaafar et al.

Abstract The research was investigated Turkey Berries drying capability using Active Mode Indirect Solar Dryers at Kovaipudur in Coimbatore, India. The conic-shaped Thermal Energy Storage (TES) covered the solar collector selectively and photovoltaic (PV) panels used to power divergent ducts equipped with DC blowers to enhance the AMISD. An energy analysis revealed meaningful distinctions between the AMISD systems equipped with TES and those operated without TES. The implementation of Thermal Energy Storage brought about a 89.6% collector efficiency rate that exceeded the results commonly reported in related PCM-based solar drying platforms. The proposed combination of a conic-shaped PCM module and a PV-powered diverging duct serves as the main cause behind this performance gain by supporting heat retention and enhancing airflow distribution. The solar dryers achieved better overall efficiency when using TES because they reached 15.23% efficiency compared to 14.8% without TES. The TES system increased the Energy Utilization Ratio up to 29.31 from its initial value of 28. Without TES AMISD used 1384 W of energy but with the implementation of TES it only needed 1268 W to function properly. The information about energy output demonstrates TES produces maximum energy consumption efficiency both with and without Phase Change Material (PCM). The PCM integration in the Specific Collector Area (SAC) improved its energy efficiency from 6.84 to 7.1%. The Sustainability Index scores achieved 8.1 when PCM was included in the experiments while the baseline scores remained at 8.01 without PCM application. Regardless of positive findings the actual experimental data fell short of projecting greenhouse dryer service expectancy to last for 35 years. The study demonstrates that using AMISD with PCM works effectively with improved energy performance while diminishing environmental influence and decreasing operational costs. Based on present circumstances in the region Turkey Berries drying with these specifications appears feasible and sustainable.

Ioannis Chronis, Chao Tang, Constantinos S. Psomopoulos

Abstract Nano‐modified electrical insulating fluids are a promising new family of insulating oils with enhanced characteristics. They can significantly improve many properties, such as fire point, breakdown voltage, partial discharge inception voltage and thermal conductivity etc. However, nanoparticles have raised concerns about the possible harm to human health and the ecosystems, but the environmental impact of nano‐modified insulating oils is far more complicated than that. Following the recent research results on the stability of nano‐modified particles, the authors introduce environmental aspects that have not attracted attention so far, such as the possible loss of stability of the insulating oil, mechanical erosion problems in parts of the electrical transformer and problems in recycling processes that may turn waste nano‐modified insulating oils into an unwanted feed stock for recycling industries. An improved method for the environmental risk assessment (RA) of nano‐modified insulating oils, based on an existing model for the RA of nanoparticles, is proposed. The authors reflect the complicated nature of the nanoliquids, mainly due to the stability of the element, which seems to have a paramount role on their environmental impact and is neglected by the current approach in RA.

Chongmin HUANG, Tianhai HUANG, Yonghua CHU

First of all, the overall framework of 3D printing is briefly introduced, including the basic principles of the additive manufacturing process, the classification and summary of the seven processes. Secondly, the common negative Poisson’s ratio structure is introduced. Compared with the conventional structure, the negative Poisson’s ratio structure has stronger energy absorption capacity, better fracture resistance and better indentation resistance, which are its advantages in printing manufacturing. Finally, 3D printing, the application of negative Poisson’s ratio structure and the combination of the two are introduced from the different perspective of medical field, for example, the application of cardiovascular stent, biomedical material structure preparation, and lumbar disc implants. This paper suggests that the structural design of negative Poisson’s ratio in 3D printing guides the development of new application directions in the medical field. Negative Poisson’s ratio materials have a wide range of applications, not only in the medical field but also in mechanical equipment, automotive manufacturing, aerospace, and other high-tech industries.

Dustin G. Wilkerson, Chase R. Crowell, Christine D. Smart et al.

ABSTRACT The first step in trait introgression is to identify and assess novel sources of variation. For shrub willow (Salix) breeders, there is an abundance of understudied species within a genus that readily hybridizes. Breeding targets in shrub willow center on traits contributing to biomass yield for bioenergy. These include stem biomass, insect and pathogen resistance, and leaf architecture traits. More specifically, breeding for durable resistance to willow leaf rust (Melampsora spp.) is of particular importance as the pathogen can significantly reduce biomass yields in commercial production. The Salix F1 hybrid common parent population (Salix F1 HCP) was created to characterize the variation among eight species‐hybrid families and map QTL for targeted traits. A female and male S. purpurea were used as common parents in crosses made to male S. suchowensis, S. viminalis, S. koriyanagi, and S. udensis and female S. viminalis, S. integra, S. suchowensis to produce eight families that were planted in field trials at Cornell AgriTech in Geneva, NY and phenotyped. Using 16 previously described parental backcross linkage maps and two newly generated S. purpurea consensus maps, we identified 215 QTL across all eight families and in every parent. These included 15 leaf rust severity, 61 herbivory, 65 leaf architecture, and 74 yield component QTL, resulting in 50 unique overlapping regions within the population. These genetic loci serve as an important foundation for future shrub willow breeding, and each interspecific family was identified as a novel source of useful alleles for trait introgression into high yielding cultivars.

Siyuan Jiang, Hongjun Gao, Xiaohui Wang et al.

With the construction of the power Internet of Things (IoT), communication between smart devices in urban distribution networks has been gradually moving towards high speed, high compatibility, and low latency, which provides reliable support for reconfiguration optimization in urban distribution networks. Thus, this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution. First, the multi-level dynamic reconfiguration method was discussed, which included feeder-, transformer-, and substation-levels. Subsequently, the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network. The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct “centralized training and decentralized execution” operation modes and improve the learning efficiency of the model. Thereafter, for a multi-agent system, this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy. In the offline learning phase, a Q-learning-based multi-agent conservative Q-learning (MACQL) algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase. In the online learning phase, a multi- agent deep deterministic policy gradient (MADDPG) algorithm based on policy gradients was proposed to explore the action space and update the experience pool. Finally, the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.

Oleg Gaidai, Vladimir Yakimov, Fang Wang et al.

Abstract Modern offshore and onshore green energy engineering includes energy harvesting—as a result, extensive experimental investigations, as well as safety and reliability analysis are crucial for design and engineering. For this study, several wind-tunnel experiments under realistic in situ wind speed conditions have been conducted to examine the performance of galloping energy harvester. Next, a novel structural reliability approach is presented here that is especially well suited for multi-dimensional energy harvesting systems that have been either numerically simulated or analog observed during the representative time lapse, yielding an ergodic system time record. As demonstrated in this study, the advocated methodology may be used for risk assessment of dynamic system structural damage or failure. Furthermore, traditional reliability methodologies dealing with time series do not easily cope with the system’s high dimensionality, along with nonlinear cross-correlations between the system’s components. This study’s objective was to assess state-of-the-art reliability method, allowing efficient extraction of relevant statistical information, even from a limited underlying dataset. The methodology described in this study aims to assist designers when assessing nonlinear multidimensional dynamic energy harvesting system’s failure and hazard risks.

Sudeshna Roy, Nipu Modak

Yasutomo KANEKO, Riku YOSHIDA, Toshio WATANABE et al.

Although bladed disks are nominally designed to be cyclically symmetric (tuned system), the vibration characteristics of all blades on a disk are slightly different due to the manufacturing tolerance, deviations in the material properties, and wear during operation. These small variations break the cyclic symmetry and split the eigenvalue pairs. Bladed disks with small variations are referred to as a mistuned system. Many researchers suggest that while mistuning has an undesirable effect on the forced response, it has a beneficial (stabilizing) effect on blade flutter (the self-excited vibration). However, almost all studies have focused on the deviation of the blade frequency, and few studies have discussed the damping mistuning. In a blisk (integrally manufactured bladed disk), it seems that the damping mistuning can be neglected because the material damping is dominant. However, in a bladed disk with the blade root inserted into the disk groove, the damping mistuning caused by the partial contact between the root and groove in rotation cannot be neglected in predicting the resonant response of the bladed disk. Therefore, in this study, incorporating the damping mistuning into the FMM (Fundamental Mistuning Model), the frequency response analysis of the bladed disk is systematically carried out. From the calculated results, the effect of the damping mistuning on the vibration response of the bladed disk is clarified.

Pabitra Maji, R. K. Bhogendro Meitei, Subrata Kumar Ghosh

Copper-steel welded joints are now used widely in heat exchangers, piping and power generation industries. Owing to their different thermal characteristics, sound welding of the pair is a challenging task. Extensive research is carried out to achieve successful welding of copper and steel. This article presents an insight into the works done on the joining of copper and steel by various techniques. The microstructural modifications in different approaches are critically presented. Mechanical properties of joints obtained through different techniques are compared.

Sunil Kumar Reddy K, Kannan M, Karthikeyan R

The aluminium is used for variety of reasons and they are known for their improved strength, stiffness, wear resistance which are useful in the marine, space, transport, automobile related industries. When aluminium reinforced with ceramic materials like fly ash, silicon carbide, tungsten carbide, boron carbide, fired bricks then a composite of better plastic forming capability, excellent heat and wear resistance will be formed. The objective of the experiment is to assess the thermal and mechanical properties of the Aluminium Metal Matrix Composites (AMMCs) when reinforced with ceramics. Aluminium (Al-7475) based metal matrix composites reinforced with varying weight percentage of Graphite(Gr) (3%, 6%, 9% and 12%) and fly ash being constant (10wt%) by the stir casting process. The composites tensile strength and hardness improved with the amount of graphite content improved in weight percentage up to 9% then decreased. While the composite’s Thermal Conductivity(TC) and Coefficient of Thermal Expansion(CTE) varying temperature range from 50°Cto 300°C reduces with increase in weight percentage of the graphite content.

Jiang Wang, Yaxin Li, Ching Man Wai et al.

Abstract Considerable recent progress has been achieved in bioengineering oil accumulation in the vegetative tissues of plants, opening an opportunity for large scale production of biodiesel, jet fuel, lubricants, and high‐value lipid bioproducts. For the highly productive C4 crops, such as sugarcane, energy cane, Miscanthus, and fiber sorghums, the bulk of the biomass is the stem. However, little success has been made in accumulating oil in the stem. Since engineering a trait with a constitutive promoter often results in pleiotropic effects that counter trait improvement, identification of stem parenchyma‐specific promoters is a prerequisite for efficient use of the ample photoassimilates stored in mature stem parenchyma cells. In this study, we first identified two TST genes encoding homologues of tonoplast sugar transporters that were strongly and almost exclusively expressed in the stems of canes via a combination of RNA‐seq atlas analysis, in silico analysis of a sugarcane genome, phylogenetic analysis, and quantitative PCR analysis. They were further confirmed in the pith parenchyma cells of the mature stem by RNA in situ hybridization. When fused with the β‐Glucuronidase (GUS) reporter gene, the promoters of two alleles, TST2b‐1A and TST2b‐1C, from one TST gene demonstrated that they could drive the GUS expression exclusively in the stem in Arabidopsis.