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Browse AllThis study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence
The aerospace industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major OEMs are moving towards fully model-based development processes, with plans to deliver requirements exclusively in the form of models. It is no longer sufficient to manage requirements using traditional document-based approaches; instead, organizations must adopt tools and processes that enable the consumption, interpretation, and implementation of model-based requirements. However, MBSE itself does not ensure that the requirements defined within the model are complete or consistent. Without rigorous validation techniques, even well-structured models can carry forward poorly defined or conflicting requirements — leading to errors that propagate throughout the development lifecycle. This work proposes an approach that integrates formal methods into MBSE workflows by enabling completeness and consistency checks of SysML-based requirements
For brake and clutch components of aircraft vehicles which require higher mechanical strength and wear resilient, light-weight aluminium composites were developed infusing solid lubricant. In this study, hybrid composites were developed using powder metallurgy route with aluminum alloy AA356 and various amounts of zirconium oxide (ZrO2) (0, 5, 10, 15, and 20 wt.%) as reinforcements. A solid lubricant hexagonal boron nitride (hBN) at a fixed 5 wt.% is considered. Following the appropriate ASTM guidelines, the specimens were mechanically characterized by measuring their density, porosity, micro-hardness, compression strength, impact strength, and flexural strength, among other properties. The findings showed that the composites' mechanical and physical behaviour were greatly affected by the inclusion of ZrO2. Porosity increased as a result of particle clustering and interfacial voids, while density increased gradually as ceramic content increased. Consistently increasing ZrO2 addition