MAPAN最新文献

Near-dry electric discharge machining (ND-EDM) has emerged as an advanced and sustainable technique for precision machining of hard and resilient materials. This study investigates the ND-EDM of EN-31 steel with the objective of optimizing key performance characteristics, including material removal rate (MRR), surface roughness (SR), residual stress (RS), and microhardness (MH). The influence of discharge current, pulse on/off time, gap voltage, and mist pressure was systematically examined using an L27 orthogonal array. Experimental results were analyzed through Taguchi-based Grey Relational Analysis (GRA) in combination with Analysis of Variance (ANOVA) to identify significant process factors. Furthermore, an objective weighting methodology incorporating standard deviation and multi-attribute decision-making (MADM) optimization was employed to establish the most favorable parameter combination. The results confirmed that the A1B1C1D1 setup yielded the most balanced machining performance, achieving higher productivity with improved surface integrity. The integration of MADM with ND-EDM provides a novel framework for sustainable machining optimization, demonstrating clear industrial advantages over conventional die-sinking EDM.

Copper oxide nanoparticles have sparked widespread interest due to their outstanding characteristics, making them intriguing candidates for a variety of applications, including bio-lubricants. This work investigates the synthesis, characterization, and prospective uses of CuO NPs in the context of sustainable technologies, specifically in the automobile industry. In the present work, CuO is synthesized through chemical precipitation methods followed by a calcination process. The calcination process is conducted at various temperature levels, forming pure copper oxide at a temperature of 1000 °C. The properties of the synthesized nanoparticles were analyzed by a field emission scanning electron microscope, which shows the spherical structure of CuO. X-ray diffraction declares the improved crystal structure of the phase analysis after calcination, and the Fourier transform infrared spectroscopy shows the purity of CuO after calcination at 1000 °C. The economic viability of the suggested synthesis technique was evaluated, revealing a production cost of INR 170.6 per gram, which is much cheaper than the current market price. This shows that the suggested technique might produce CuO NPs on a large scale at a low cost.

Road traffic noise is a significant source of environmental pollution in urban areas, with increasing traffic leading to elevated noise levels that adversely affect city life. As a result, ongoing research globally focused on controlling and mitigating traffic noise. This problem is particularly severe near intersections, where inadequate planning and the absence of noise reduction strategies exacerbate the situation. Therefore, this study develops a traffic noise model for the mid-sized city of Agartala to evaluate vehicular noise levels at intersections. It evaluates two noise prediction models, multiple linear regression (MLR) and artificial neural network (ANN) to estimate the equivalent sound level (Leq), using variables like total hourly vehicles, percentage of heavy vehicles, vehicle speed, road width, temperature, and humidity. The ANN model outperformed the regression model, achieving an r of 0.956, R² of 0.9139, MSE of 1.74, RMSE of 1.32, MAPE% of 1.41, and an accuracy (± 1 dBA) of 83.87%. In contrast, the regression model had an r of 0.905, R² of 0.8187, MSE of 3.57, RMSE of 1.89, MAPE% of 2.20, and an accuracy (± 1 dBA) of 48.39%. ANN is a highly effective tool for modelling traffic noise. The study’s outcomes could serve as valuable resources for noise modelling consultants and urban planners mapping traffic noise in mid-sized urban areas.

This study is inclined towards exploring the microstructure and crystallinity of medical grade polyamide 12 (PA12) and the way it is being influenced by Selective Laser Sintering (SLS) parameters, with a major focus on application towards biomedical. The key goal of this study is to get the best input setting, to enhance both the mechanical as well as the biocompatibility of PA12 with the help of morphological outputs. For getting the desired output, samples were fabricated through varying Laser wattage, Laser travel speed, and Deposition thickness in the SLS input settings. XRD, EDS, and FESEM analyses were carried out on the sintered samples to determine crystallinity, elemental composition, and surface morphology, respectively. The fabricated samples fetch the best result of surface roughness (2.5–3.8 µm) and XRD, supporting the biocompatibility, at a Laser wattage of 30 W, a Laser travel speed of 750 mm/s, and 100 µm layer thickness. The findings indicate that careful Laser wattage and Laser travel speed adjustment significantly affect surface quality, crystallinity, and strength. While a moderate heat input improves overall performance and excessive heat can compromise the surface and lower crystallinity.

Starting from revealing the mathematical conceptual dilemmas of classical measurement theory and based on rigorous mathematical concepts, this study deduces the correct mathematical principles of measurement error theory. In contrast to the mathematical cognition of classical measurement theory, this study regards the measured values as constants and the true values of the measurands as variables and strictly distinguishes the differences between constant equations and variable equations, thus deriving a new measurement concept system without error categories, redefining the concept of measurement uncertainty, and improving measurement quality. This study negates the conceptual logical system of classical measurement theory, leading to a global change in human measurement concepts.

Electrical faults in power system may cause unstable power delivery and a higher risk of power outages. Consequently, precise identification, measurement, and classification of faults are crucial for efficient maintenance and optimal operation of power system to uphold uninterrupted power supply. Hence, this article presents the identification, measurement, and classification of various types of faults at different location of power system network (PSN). These different kinds of faults are measured in terms of inception angle. The performance parameters like accuracy, total harmonic distortion (THD), mean squared error (MSE) are found to be inappropriate with existing methods for identifying the faults. The existing methods also takes more data for computation and analysis. In this paper, combination of the symbolic and fuzzy logic controller (FLC) is proposed which is known as advanced fuzzy-symbolic strategy (AFSS) which surpass the issues of the existing methods. The effectiveness of the method is tested on modified IEEE 9 bus system. The computer simulation results and performance parameters like accuracy (6.55%), THD (3.02%), MSE (6.55%), are found to be better with AFSS in comparison to FLC for the identification, measurement, and classification of different kind of faults at different locations of PSN. The regression line also converges faster with AFSS in contrast to FLC.

Accurate and traceable measurement of renewable resource data is essential for reliable distributed generation (DG) planning. This study presents a metrology-aware co-optimization framework that simultaneously allocates wind turbine (WT) and photovoltaic (PV) units while scheduling demand response (DR) under quantified measurement uncertainty. Wind speed and solar irradiance are measured using IEC 61400-12-1–compliant anemometers and ISO 9060-classified pyranometers, respectively, with their expanded uncertainties (U95) propagated through the corresponding WT and PV power output models. The planning problem is formulated as a mixed-integer second-order cone programming model, minimizing active power losses in a 33-bus radial distribution network while satisfying network, voltage, and DR constraints. Monte Carlo simulations (1000 trials) reveal that incorporating measurement uncertainty alters the optimal DG siting in 14% of cases and restricts total loss variability to ± 1.8%, thereby confirming the robustness of the proposed scheme. Compared with a benchmark case that excludes DR and metrological considerations, the framework achieves a 29.6% reduction in losses and a 22.4% improvement in renewable utilization. Sensitivity analysis further indicates that higher DR participation mitigates uncertainty impacts and supports deployment of up to three DG units (3.6 MW each) before diminishing returns emerge. Overall, the results demonstrate (i) the critical role of DR in strengthening renewable integration and (ii) the necessity of rigorous uncertainty quantification and traceable calibration in measurement-driven power system optimization.

The carbon content [organic carbon (OC), elemental carbon (EC), water-soluble organic carbon (WSOC), total carbonaceous matter (TCM), primary organic carbon (POC) and secondary organic carbon (SOC)] of respirable particulate matter (PM₁₀) was  estimated at the eastern Himalayan region (Darjeeling) of India during 4 consecutive winter seasons (W-I: December 2018–February 2019; W-II: December 2019–February 2020; W-III: December 2020–February 2021; and W-IV: December 2021–February 2022) to examine their concentration, sources and environmental impact. During four consecutive winter seasons, the mean concentrations of PM₁₀, OC, EC, WSOC, and TCM were estimated as 60 ± 20 µg m⁻³, 6.0 ± 1.9 µg m⁻³, 2.7 ± 1.1 µg m⁻³, 3.4 ± 1.3 µg m⁻³, and 12.5 ± 4.1 µg m⁻³, respectively. The overall mean carbonaceous aerosols (CAs) mass concentration of PM₁₀ was estimated to be ~ 21% of PM₁₀ during winters [W-I (CAs: 23.9%), W-II (CAs: 19.7%), W-III (CAs: 19.6%), and W-IV (CAs: 20.3%)]. Results showed the non-significant variations (at p ≤ 0.05) in mass concentrations of PM₁₀, OC, EC, TCM, POC, and SOC among all winters season. The relationship between OC, EC, & WSOC and their weight ratios (OC/EC, OC/WSOC, EC/TC) suggested that fossil fuel combustion [(FFC; including vehicular fuel combustion (VFC)] and biomass burning (BB) are the major sources of CAs at Darjeeling. In the present case, the mean effective carbon ratio (ECR) is computed as 0.33 (range: 0.04–0.76; ECR < 1) which demonstrates the abundance of POC species more than the SOC and indicates the warming effects of CAs over the study site. The air mass backward trajectory analysis indicates that CAs approaching to the sampling site of Darjeeling primarily originated from the Nepal, Bhutan, Tibet, the IGP, the Thar Desert and Pakistan.