Shivani Mahura

Additive manufacturing (AM) has advanced considerably as a disruptive technology that addresses the increasing demand for multi-functional, multi-material, and geometrically complex components and has begun to reshape areas of both product development and production methods. However, significant challenges related to incompatible material properties, defective parts made from AM processes, and inconsistent use of product build quality still exist. The ability to achieve highly precise AM processes is heavily reliant on monitoring, controlling, and utilizing multiple process variables effectively. In recent years, more advanced methods, including machine learning (ML), big data analytics, and design for additive manufacturing (DfAM), have become more available to address these limitations. Although these methods have been studied significantly, their use in aerospace-specific settings has been limited. In this review, study first provides an in-depth discussion on recent trends in DfAM. The review also considers simulation and modeling tools as enablers to obtain improved geometric fidelity in AM and to increase predictability of AM processes. Other trends include improved automation through the IoT, as well as knowledge-based process planning, also for multi-part cases of production. Lastly, review summarize many of the on-going issues and some future directions for algorithm-driven AM in aerospace and relate to industry 4.0 trends that emphasize intelligent automation, adaptive process control and lifecycle management, which will improve efficiency, reliability, and scale-up of AM technologies in aerospace and other advanced engineering applications.

Floods are really harmful surprise elements of nature, and as such, making short-term flood prediction very accurate is a major requirement of early-warning systems done in such a way as to prevent human and property losses, economic disruption, etc. Still, they are hard to guess due to the very intricate combination of hydrological, climatic, and other environmental factors. The present paper offers an interpretable ensemble machine learning framework for predicting the times of flood events around 1-3 days ahead via the use of hydrology, climate, and environmental indicators together. The method offers great help through data preprocessing, feature normalisation, and the application of various regression models to cost-continuous flood probabilities estimation. Random Forest and Gradient Boosting algorithms are used to find and improve prediction accuracy through capturing non-linear relationships, while a hybrid ensemble method combines the advantages of individual models. Decision-making is made simplistic by the conversion of probabilistic outputs into binary flood alerts at a predetermined fixed threshold. The framework is executed and tested in a MATLAB-Simulink setting, and the analysis confirms its readiness for real-time operations. The results from the experiments indicate that the learning through the ensemble approach has significantly improved the prediction reliability and interpretability as compared to single model techniques.