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Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential because it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protective measures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that uses principal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air using meteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnataka state, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration and meteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model, based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performance was satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R.

Heat pipe is used to extract heat generated from a surface and applicable to cooling of the many components. Some of the main applications of heat pipes are space crafts, computer systems, permafrost cooling, heat exchangers and thermal storage sub systems. In this work, an experiment will be carried out with an indirect heat composite pipe located with different orientation, heat input and mass flow. Thermal resistance, heat transfer coefficient and thermal efficiency are determined, which are applicable for laptop cooling and solar heating. Heat input 25 to 150 watts, with orientation angles 0, 30, 45, 90 degrees, mass flow rate are 0.01kg/sec, 0.02kg/sec and 0.03kg/sec are considered.

In the research work, AISI 431 grade stainless steel were chosen to investigate the effects of vacuum annealing processes on reducing hardness and enhancing ductility. Although stainless steel of martensitic type are well known for their high hardness, wear resistance and they have limited applications due to high hardness properties. Chosen for this research work, vacuum annealing were carried out to temperatures of 740°C, 840°C and 940°C. The samples were prepared cylindrically to a dimension of 40 mm length and 10 mm diameter. An untreated material was kept aside for comparison.Vacuum annealing involves heating the material in a controlled environment to eliminate impurities and relieve internal stresses, resulting in reduced hardness and improved ductility. To evaluate the effect of wear on annealed material, a pin-on-disc wear test apparatus were used. The wear behavior of the annealed specimens were compared to the untreated material. It includes both the treated and untreated samples, were examined by using a scanning electron microscope (SEM) to obtain images of microstructure. The results of this research work contribute to the understanding of the annealing effects on martensitic stainless steels and provide insights into optimizing their properties for specific applications.

Aim of current study is to utilize different sustainable artificial intelligence (AI) tools to check the influence of test factors on erosion wear. Bottom ash is taken as erodent at different solid concentration while brass is considered as base material. The parameters involved are rotational speed (N), solid concentration (CW), and testing time duration (T). According to experimental results and analysis based on different AI tools , it is abundantly found that erosion wear have a significant dependency on parameters such as N, CW, T and the order of maximum erosion was found as N > CW >T. The rate of rotation speed (N) has been identified as the factor that has the greatest impact on the degree to which erosion wear occur. 3D analysis has been conducted for the maximum and minimum erosion wear condition. In order to verify the accuracy, four distinct methods are utilized; nonetheless, the accuracy of the regression analysis has been found more promising when compared to that of the Ridge, lasso and neural network methodologies.

PMMA Composites have become the most popular materials due to its excellent strength-to-weight ratio, graphite reinforced for engineering applications. Researchers have used a variety of reinforcements to enhance the mechanical and tribological characteristics of various polymer matrixes, including carbon fiber, glass fiber, and carbon-based nanoparticles, graphite, alumina, silica, silicon carbide, titanium oxide, bacterial cellulose, Nano-cellulose, and zirconium oxide.This project focuses on the characteristics of composites made with graphite reinforcements that are based on polymethyl methacrylate. Die casting was used to manufacture this reinforced composite. To enhance the mechanical qualities (impact strength and hardness), tribological parameters (slide distance, normal load, and sliding velocity are employed for wear rate). Therefore, in this study, greater focus has been placed on on PMMA-based composites with graphite reinforcement.

When the two materials are in contact in motion, wear rate of the materials taken an important role. Aluminium alloys used in many applications like automobile components, aerospace components, home appliances, machine tool components. Advanced aluminium alloys are used in aerospace components. In this project wear studies of advanced aluminium alloys are carried using pin-on-disc machine three processing parameters considered are Load, Time and Speed. Twenty seven samples ae been tested to find wear rate of aluminium alloys. A Taguchi method is applied to find optimization process parameters which are having minimum wear rate. Aluminium alloy 6082 has a medium strength and good corrosion resistance. It is the strongest alloy

The growing demand for high performance engine technology in the two-wheeler market segment has pushed the average speed of the motorcycle to a higher limit, which has already raised the danger of motorcycle accidents leading to serious knee and lower tibia injuries. Vehicle designers, testers, and validators undergo rigorous design, testing, and validation processes based on the highest criteria for strength, weight reduction, enhanced fuel efficiency, etc., but with the fewest requirements for a vehicle’s safety in an accident. The design of a leg guard for the popular Indian Pulsar 150 motorcycle. By changing the shape and material, a thorough investigation of strength is performed. We select three different materials, such as SS202, SS347, and SS440C. The leg guard for the pulsar was built, and its stress and deformation were assessed. The optimal configuration is selected from a total of six variants using the least weight and stress deformation.

Air pollution, especially particulate matter (PM) pollution, has a significant impact on India. PM pollution is due to roadside dust, fossil fuel use, vehicular population, and industrial emissions. PM10 forecasting model development is essential because it permits the experts and the citizens to take appropriate actions to restrict their exposure and execute protective measures to improve air quality. This study aimed to develop a specialized computational intelligence methodology that uses principal component (PC) based artificial neural networks (ANN). The model was used to forecast PM10 in ambient air using meteorological data. This application is demonstrated for monitoring data from the urban area of Belagavi city of Karnataka state, India. Principal component analysis (PCA) was applied to understand the interactions between PM10 concentration and meteorological data. The analysis found that the PCANN model is better than the principal component regression (PCR) model, based on using various performance indexes (MAE, MSE, MAPE, RMSE, R, and R2). The PM10 predictive model performance was satisfactory, with a MAPE of 0.069. The overall predictive capability of PM10 was 89.59% in terms of R. [ABSTRACT FROM AUTHOR]