Novel fuzzy measurement alternatives and ranking according to the compromise solution-based green machining optimization

Due to the increase in the impact of different manufacturing processes on the environment, green manufacturing processes are the prime focus of many current pieces of research. In the current article, a green machining process for stainless steel and SS304 and AISI1045 steel has been optimized using newly developed Fuzzy Measurement Alternatives and Ranking according to the COmpromise Solution (F-MARCOS) method in the form of two case studies. In the first case study, nose radius, cutting speed, depth of cut, and feed rate are selected as the process parameters whereas surface roughness, consumption of electrical energy, and power factor are the outputs. In the second case study width of cut, depth of cut, feed rate, and cutting speed were the process parameters and material removal rate (MRR), active energy consumption (ACE), and surface roughness (Ra) are the response variables. The MARCOS method ranks the alternatives based on the ideal and anti-ideal solutions for the different criteria. The inclusion of fuzzy logic adds worth to the model by using a linguistic scale to make the method more practical and flexible. Based on the detailed analysis, it ranked the best alternative in case study one which results in a power factor of 0.862, 26.68 kJ of electrical energy consumption, and surface roughness of 0.36 mu m. In the second case study, the best alternative selected by this method gave an MRR of 2400 mm3/min and Ra of 2.29 mu m and utilizes 53.988 kJ ACE.Web of Science1012art. no. 264

A novel MOGNDO algorithm for security-constrained optimal power flow problems

The current research investigates a new and unique Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) algorithm for solving large-scale Optimal Power Flow (OPF) problems of complex power systems, including renewable energy sources and Flexible AC Transmission Systems (FACTS). A recently reported single-objective generalized normal distribution optimization algorithm is transformed into the MOGNDO algorithm using the nondominated sorting and crowding distancing mechanisms. The OPF problem gets even more challenging when sources of renewable energy are integrated into the grid system, which are unreliable and fluctuating. FACTS devices are also being used more frequently in contemporary power networks to assist in reducing network demand and congestion. In this study, a stochastic wind power source was used with different FACTS devices, including a static VAR compensator, a thyristor- driven series compensator, and a thyristor-driven phase shifter, together with an IEEE-30 bus system. Positions and ratings of the FACTS devices can be intended to reduce the system's overall fuel cost. Weibull probability density curves were used to highlight the stochastic character of the wind energy source. The best compromise solutions were obtained using a fuzzy decision-making approach. The results obtained on a modified IEEE-30 bus system were compared with other well-known optimization algorithms, and the obtained results proved that MOGNDO has improved convergence, diversity, and spread behavior across PFs.Web of Science1122art. no. 382

Exploring Properties of Short Randomly Oriented Rattan Fiber Reinforced Epoxy Composite for Automotive Application

The acrylic acid treated rattan fiber reinforced epoxy (RF/Epoxy) composite has been fabricated by taking various weight percentages (5, 10, 15, 20, and 25 wt%) of fibers using hand layup followed by compression molding technique. The optimum fiber loading has been determined by regression analysis and it is found that 18 wt% of rattan fiber gives maximum tensile strength. The interaction of rattan fiber with epoxy matrix has been studied by Fourier transform infrared spectroscopy and X-ray diffraction. Scanning electron microscope (SEM) has been used to study the morphology of chemically modified rattan fiber and RF/Epoxy composite. The mechanical, thermal, and dynamic mechanical thermal properties of 18 wt% RF/Epoxy composite have been studied as per ASTM standards. From the results, it is observed that RF/Epoxy composite gives best properties at optimum (18 wt%) fiber loading. The maximum tensile strength and flexural strength of the composite are found to be 47.5 and 121 MPa, respectively, which is better in comparison to other natural fiber composites used for manufacturing different automobile body parts. Hence, it can be concluded that 18 wt% RF/Epoxy composite can be considered as a potential candidate for automotive body parts

Solid Particle Erosion of Date Palm Leaf Fiber Reinforced Polyvinyl Alcohol Composites

Solid particle erosion behavior of short date palm leaf (DPL) fiber reinforced polyvinyl alcohol (PVA) composite has been studied using silica sand particles (200 ± 50 μm) as an erodent at different impingement angles (15–90°) and impact velocities (48–109 m/s). The influence of fiber content (wt% of DPL fiber) on erosion rate of PVA/DPL composite has also been investigated. The neat PVA shows maximum erosion rate at 30° impingement angle whereas PVA/DPL composites exhibit maximum erosion rate at 45° impingement angle irrespective of fiber loading showing semiductile behavior. The erosion efficiency of PVA and its composites varies from 0.735 to 16.289% for different impact velocities studied. The eroded surfaces were observed under scanning electron microscope (SEM) to understand the erosion mechanism

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Not AvailableAlteration of crop root morphology is a new innovative approach to provide food security. Phosphorus is the most important nutrient to influence root properties. Efficient use of P fertilizers has become an important issue of agriculture all over the world due to limited availability of rock phosphate and its non-renewable nature. Hence, root properties and grain yield of soybean-wheat cropping system were evaluated by inoculation of phosphate solubilizing bacteria (PSB) and vesicular arbuscular microorganism (VAM) with 50% recommended P (0.5 P + PSB + VAM) against 100% P (1.0 P), 50% P and control in a Typic Ustochrepts of the Indo-Gangetic plains. The root cation exchange capacity (CEC) of soybean and wheat treated with 0.5 P + PSB + VAM were 3.6 and 4.6% higher than 1.0 P, respectively. The same treatment produced 2.3 and 2.6% higher root length density (RLD) in soybean and wheat, respectively in comparison to 1.0 P. The P inflow rate under 0.5 P + PSB + VAM was 9.2 and 4.6% higher than 1.0 P in soybean and wheat, respectively indicating higher acquisition of P through VAM, although higher rhizospheric P availability was recorded in 1.0 P. The root CEC, RLD and P inflow rate were closely related to P concentration and content in root, shoot and nodule, specific root length, root diameter and internal P requirement. The better root property observed in 0.5 P + PSB + VAM enhanced 4.1 and 4.9% grain yield of soybean and wheat, respectively as compared to 1.0 P. Inoculation of PSB and VAM could substitute 50% P of soybean-wheat cropping system with better root property and higher grain yield in semi-arid sub tropics of the Indo-Gangetic plains.Not Availabl

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Not AvailableAlteration of crop root morphology is a new innovative approach to provide food security. Phosphorus is the most important nutrient to influence root properties. Efficient use of P fertilizers has become an important issue of agriculture all over the world due to limited availability of rock phosphate and its non-renewable nature. Hence, root properties and grain yield of soybean-wheat cropping system were evaluated by inoculation of phosphate solubilizing bacteria (PSB) and vesicular arbuscular microorganism (VAM) with 50% recommended P (0.5 P + PSB + VAM) against 100% P (1.0 P), 50% P and control in a Typic Ustochrepts of the Indo-Gangetic plains. The root cation exchange capacity (CEC) of soybean and wheat treated with 0.5 P + PSB + VAM were 3.6 and 4.6% higher than 1.0 P, respectively. The same treatment produced 2.3 and 2.6% higher root length density (RLD) in soybean and wheat, respectively in comparison to 1.0 P. The P inflow rate under 0.5 P + PSB + VAM was 9.2 and 4.6% higher than 1.0 P in soybean and wheat, respectively indicating higher acquisition of P through VAM, although higher rhizospheric P availability was recorded in 1.0 P. The root CEC, RLD and P inflow rate were closely related to P concentration and content in root, shoot and nodule, specific root length, root diameter and internal P requirement. The better root property observed in 0.5 P + PSB + VAM enhanced 4.1 and 4.9% grain yield of soybean and wheat, respectively as compared to 1.0 P. Inoculation of PSB and VAM could substitute 50% P of soybean-wheat cropping system with better root property and higher grain yield in semi-arid sub tropics of the Indo-Gangetic plains.Not Availabl

Ensemble Deep Learning for Wear Particle Image Analysis

This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications