Search results

This study aims to understand the current production scenario emphasizing the significance of green manufacturing in achieving economic and environmental sustainability goals to fulfil future needs; to determine the viability of particular strategies and actions performed to increase the process efficiency of electrical discharge machining; and to uphold the values of sustainability in the nonconventional manufacturing sector and to identify future works in this regard.

A thorough analysis of numerous experimental studies and findings is conducted. This prominent nontraditional machining process’s potential machinability and sustainability challenges are discussed, along with the current research to alleviate them. The focus is placed on modifications to the dielectric fluid, choosing affordable substitutes and treating consumable tool electrodes.

Trans-esterified vegetable oils, which are biodegradable and can be used as a substitute for conventional dielectric fluids, provide pollution-free machining with enhanced surface finish and material removal rates. Modifying the dielectric fluid with specific nanomaterials could increase the machining rate and demonstrate a decrease in machining flaws such as micropores, globules and microcracks. Tool electrodes subjected to cryogenic treatment have shown reduced tool metal consumption and downtime for the setup.

The findings suggested eco-friendly machining techniques and optimized control settings that reduce energy consumption, lowering operating expenses and carbon footprints. Using eco-friendly dielectrics, including vegetable oils or biodegradable dielectric fluids, might lessen the adverse effects of the electrical discharge machine operations on the environment. Adopting sustainable practices might enhance a business’s reputation with the public, shareholders and clients because sustainability is becoming increasingly significant across various industries.

A detailed general review of green nontraditional electrical discharge machining process is provided, from high-quality indexed journals. The findings and results contemplated in this review paper can lead the research community to collectively apply it in sustainable techniques to enhance machinability and reduce environmental effects.

The main objective of this paper is to investigate the response of human skin to an intense temperature drop at the surface. In addition, this paper aims to evaluate the efficiency of finite difference and finite volume methods in solving the highly nonlinear form of Pennes’ bioheat equation.

One-dimensional linear and nonlinear forms of Pennes’ bioheat equation with uniform grids were used to study the behavior of human skin. The specific heat capacity, thermal conductivity and blood perfusion rate were assumed to be linear functions of temperature. The nonlinear form of the bioheat equation was solved using the Newton linearization method for the finite difference method and the Picard linearization method for the finite volume method. The algorithms were validated by comparing the results from both methods.

The study demonstrated the capacity of both finite difference and finite volume methods to solve the one-dimensional and highly nonlinear form of the bioheat equation. The investigation of human skin’s thermal behavior indicated that thermal conductivity and blood perfusion rate are the most effective properties in mitigating a surface temperature drop, while specific heat capacity has a lesser impact and can be considered constant.

This paper modeled the transient heat distribution within human skin in a one-dimensional manner, using temperate-dependent physical properties. The nonlinear equation was solved with two numerical methods to ensure the validity of the results, despite the complexity of the formulation. The findings of this study can help in understanding the behavior of human skin under extreme temperature conditions, which can be beneficial in various fields, including medical and engineering.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

The purpose of this paper is to optimize and improve a bipolar charge transport (BCT) model used to simulate charge dynamics in insulating polymer materials, specifically low-density polyethylene (LDPE).

An optimization algorithm is applied to optimize the BCT model by comparing the model outputs with experimental data obtained using two kinds of measurements: space charge distribution using the pulsed electroacoustic (PEA) method and current measurements in nonstationary conditions.

The study provides an optimal set of parameters that offers a good correlation between model outputs and several experiments conducted under varying applied fields. The study evaluates the quantity of charges remaining inside the dielectric even after 24 h of short circuit. Moreover, the effects of increasing the electric field on charge trapping and detrapping rates are addressed.

This study only examined experiments with different applied electric fields, and thus the obtained parameters may not suit the experimental outputs if the experimental temperature varies. Further improvement may be achieved by introducing additional experiments or another source of measurements.

This work provides a unique set of optimal parameters that best match both current and charge density measurements for a BCT model in LDPE and demonstrates the use of trust region reflective algorithm for parameter optimization. The study also attempts to evaluate the equations used to describe charge trapping and detrapping phenomena, providing a deeper understanding of the physics behind the model.

The aim of this study is to determine the fracture behavior of wool felt and fabric based epoxy composites and their responses to electromagnetic waves.

Notched and unnotched tensile tests of composites made of wool only and hybridized with a glass fiber layer were carried out, and fracture behavior and toughness at macro scale were determined. They were exposed to electromagnetic waves between 8 and 18 GHz frequencies using two horn antennas.

The keratin and lignin layer on the surface of the wool felt caused lower values to be obtained compared to the mechanical values given by pure epoxy. However, the use of wool felt in the symmetry layer of the laminated composite material provided higher mechanical values than the composite with glass fiber in the symmetry layer due to the mechanical interlocking it created. The use of wool in fabric form resulted in an increase in the modulus of elasticity, but no change in fracture toughness was observed. As a result of the electromagnetic analysis, it was also seen in the electromagnetic analysis that the transmittance of the materials was high, and the reflectance was low throughout the applied frequency range. Hence, it was concluded that all of the manufactured materials could be used as radome material over a wide band.

Sheep wool is an easy-to-supply and low-cost material. In this paper, it is presented that sheep wool can be evaluated as a biocomposite material and used for radome applications.

The combined evaluation of felt and fabric forms of a natural and inexpensive reinforcing element such as sheep wool and the combined evaluation of fracture mechanics and electromagnetic absorption properties will contribute to the evaluation of biocomposites in aviation.

Electrical characteristics of transformer oil (TO) have been studied during normal and thermal aging conditions. In this paper, breakdown voltage (BDV), partial discharge (PD), heat transfer results and the physical mechanisms considering the impact of varying the diameter of Al₂O₃ nanoparticles (NPs) have been investigated. Different quantities of the two sizes of Al₂O₃ were added to the oil using a two-step method to determine the positive effect of NPs on the electrical and thermal properties of TO. Finally, the physical mechanisms related to the obtained experimental results have been performed.

The implementation of nanoparticles in this paper was provided by US Research Nanomaterials, Inc., USA. The provided Al₂O₃ NPs have an average particle size of 20–80 nm and a specific surface area of 138 and 58 m²/g, respectively, which have a purity of over 99%. Thermal aging has been done. The IEC 60156 standard has been implemented to calculate the BDV, and a 500-mL volume test cell (Apar TO 1020) has been used. PD test is performed according to Standard IEC 60343, and a JDEVS-PDMA 300 device was used for this test.

BDV tests indicate that 20 nm Al₂O₃ is more effective at improving BDV than 80 nm Al₂O₃, with an improvement of 113% compared to 99% for the latter. The analysis of Weibull probability at BDV indicates that 20 nm Al₂O₃ performs better, with improvements of 141%, 125% and 112% at probabilities of 1, 10 and 50%, respectively. The results of the PD tests using the PDPR pattern also show that 20 nm Al₂O₃ is superior. For the heat transfer test, 0.05 g/L of both diameters were used to ensure fair conditions, and again, the advantage was with 20 nm Al₂O₃ (23% vs 18%).

The effect of Al₂O₃ NP diameter (20 and 80 nm) on various properties of virgin and aged TO has been investigated experimentally in this paper to examine the effect of proposed NP on electrical improvement of TO.

Millets are ancient grains, following wheat, that have been a fundamental source of human sustenance. These are nutrient-rich small-seeded grains that have gained prominence and admiration globally due to their super resilience in diverse climates and significant nutritional benefits. As millets are renowned for their nutritional richness, the demand for millet-based products increases. Hence, this paper aims in identifying the growing need for innovative processing techniques that not only preserve their nutritional content but also extend their shelf life.

In traditional times, heat was the only means of cooking and processing of the foods, but the amount of damage they used to cause to the sensorial and nutritional properties was huge. Millets’ sensitivity toward heat poses a challenge, as their composition is susceptible to disruption during various heat treatments and manufacturing processes. To cater to this drawback while ensuring the prolonged shelf life and nutrient preservation, various innovative approaches such as cold plasma, infrared technology and high hydrostatic pressure (HPP) processing are being widely used. These new methodologies aim on inactivating the microorganisms that have been developed within the food, providing the unprocessed, raw and natural form of nutrients in food products.

Among these approaches, nonthermal technology has emerged as a key player that prioritizes brief treatment periods and avoids the use of high temperatures. Nonthermal techniques (cold plasma, infrared radiation, HPP processing, ultra-sonication and pulsed electric field) facilitate the conservation of millet’s nutritional integrity by minimizing the degradation of heat-sensitive nutrients like vitamins and antioxidants. Acknowledging the potential applications and processing efficiency of nonthermal techniques, the food industry has embarked on substantial investments in this technology. The present study provides an in-depth exploration of the array of nonthermal technologies used in the food industry and their effects on the physical and chemical composition of diverse millet varieties.

Nonthermal techniques, compared to conventional thermal methods, are environmentally sound processes that contribute to energy conservation. However, these conveniences are accompanied by challenges, and this review not only elucidates these challenges but also focuses on the future implications of nonthermal techniques.

Due to herbs and plants’ therapeutic properties and simplicity of availability in nature, humans have used them to treat a variety of maladies and diseases since ancient times. Later, as technology advanced, these plants and herbs gained significant relevance in some industries due to their suitable chemical composition, abundant availability and ease of access. Aegle marmelos is a species of plant that may be found in nature. Yet, little or very little literature was located on the coloration behavior of this plant’s leaves. This study aims to focus on the effect of different parameters on the extraction of colorant from Aegle marmelos leaves.

Some factors that affected on the extraction processes were examined and found to have significant impacts on the textile dyeing such as the initial dye concentration, extracted temperature, extracted bath pH and extracted time were all changed to see how they affected color extraction. The authors report a direct comparison between three heating methods, namely, microwave irradiation (MWI), ultrasonic waves (USW) and conventional heating (CH). The two kinetic models have been designed (pseudo-first and pseudo-second orders) in the context of these experiments to investigate the mechanism of the dyeing processes for fabrics under study. Also, the experimental data were analyzed according to the Langmuir and Freundlich isotherms.

From the result, it was discovered these characteristics were found to have a substantial effect on extraction efficiency. Temperature 90°C and 80°C when using CH and USW, respectively, while at 90% watt when using MWI, period 120 min when using CH as well as USW waves, while 40 min when using MWI, and pH 4, 5 and 10 for polyamide, wool and cotton, respectively, were the optimal extraction conditions. Also, the authors can say that wool gives a higher absorption than the other fabric. Additionally, MWI provided the best color strength (K/S) value, and homogeneity, at low temperatures reducing the energy and time consumed. The coloring follows the order: MWI > USW > CH. The adsorption isotherm of wool could be well fitted by Freundlich isotherm when applying CH and USW as a heating source, while it is well fitted by the Langmuir equation in the case of MWI. In the study, it was observed that the pseudo-first-order kinetic model fits better the experimental results of CH with a constant rate K₁ = −0.000171417 mg/g.min, while the pseudo-second-order kinetic model fits better the experimental results of absorption of both MWI (K2 = 38.14022572 mg/g.min) and USW (K₂ = 12.45343554 mg/g.min).

There is no research limitation for this work. Dye was extracted from Aegle marmelos leaves by applying three different heating sources (MWI, ultrasonic waves [USWW] and CH).

This work has practical applications for the textile industry. It is concluded that using Aegle marmelose leaves can be a possible alternative to extract dye from natural resource by applying new technology to save energy and time and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the extracted dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.

Radio frequency (RF) technology relies on the electromagnetic properties of the materials used, which includes their complex permittivities and loss tangents. To measure these properties, techniques for material characterization such as the transmission/reflection method are used in conjunction with conversion techniques to calculate these values from scattering parameters. Unfortunately, these techniques rely on relatively expensive rectangular waveguide adaptors and components, especially if testing over large frequency ranges. This paper aims to overcome this challenge by developing a more affordable test equipment solution based on additively manufactured waveguide sections.

To evaluate the effectiveness of using additively manufactured waveguides to perform electromagnetic characterization with the transmission/reflection method, samples of PLA Tough with varying infill percentages and samples made from several Formlabs photopolymer resins are fabricated and analyzed.

Results show that the method yielded permittivity and loss tangent values for the measured materials that generally agree with those found in the literature, supporting its credibility.

The accessibility of this measurement technique will ideally allow for more electromagnetic material characterization to occur and expand the possible use of additive manufacturing in future RF designs. This work also provides characterization of several Formlabs photopolymer resins, which have not been widely analyzed in the current literature.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.