Government Engineering College-Rewa is an engineering college established by the state government at Rewa in Madhya Pradesh (MP), India in 1964.This is the second oldest government engineering college in Madhya Pradesh.
As-received Al–7Si LM25 alloy and its 10 wt.
The management of power flow in renewable energy-based charging stations is a critical challenge due to the variability in energy sources such as solar and wind, as well as the fluctuating demand from electric vehicles (EVs). In this paper, charging station having solar-based renewable energy system is being implemented, which is fed by the constant input irradiation level of 1000 W/m2. The artificial intelligence-based algorithms for power flow distribution are designed feeding EV battery load. Each algorithm brings unique capabilities, from the simplicity and fast convergence of PC_GWO to the exploratory strength of PC_MFO and the predictive and adaptive power of LRC_MFANN. The assessment is carried out by studying the behavior of the DC link voltage, power delivered at the EV load terminals and station battery. As inferred from the results, superior stability in the DC link voltage, LRC_MFANN is found to be the most effective algorithm for ensuring consistent and reliable power delivery to the station’s battery, minimizing fluctuations and enhancing overall performance.
Lightweight materials with improved mechanical and tribological performance are increasingly required for advanced engineering and automotive applications. In this study, Al-based hybrid composites reinforced with SiC and TiO2 particles were fabricated through an ultrasonic-assisted casting process to investigate the influence of reinforcement content on microstructure, mechanical properties, and wear behavior. SEM analysis revealed relatively well-integrated particle dispersion and noticeable grain refinement with increasing reinforcement fraction, while XRD confirmed aluminum as the dominant phase along with SiC and TiO2 without undesirable phase formation. Mechanical testing showed significant improvements in strength and hardness. The ultimate tensile strength increased from 198 to 305 MPa ( 54
This paper delineates the development of two machine learning models: the first for the classification of fruits into distinct categories using the comprehensive Fruits 360 dataset, and the second for the determination of ripeness levels within a specific category, exemplified by bananas. The Fruits 360 dataset, encompassing over 90,000 images of 131 fruit and vegetable types, provides a robust foundation for the initial classification model. In contrast, the banana ripeness dataset, with its focus on various stages of banana maturity, enables the second model to discern between unripe, ripe, and overripe states with remarkable accuracy. The architecture of the models is based on convolutional neural networks (CNNs). It is meticulously trained and validated to achieve high precision in both fruit categorization and ripeness detection. The results of this study not only demonstrate the efficacy of the proposed models but also highlight the transformative potential of machine learning in automating and enhancing agricultural processes.
This study focuses on modeling the reliability and failure rates of two distinct groups of heavy-duty diesel engines (HDDE) with identical capacities, deployed in Excavators. Utilizing Relia-soft Weibull++ software, individual graphs are generated to juxtapose the reliability and failure rate trends of each HDDE group. Various probability distribution models are applied for this analysis. The objective is to determine, within a specific time interval, which group exhibits superior reliability. This assessment is facilitated by comparative analysis of reliability and failure rate plots. By discerning the failure patterns of these engines, a tailored maintenance strategy can be formulated to enhance workshop efficiency, thereby ensuring optimal equipment availability. Time to failure (TTF) data sourced from workshop maintenance logs underpins this investigation.
