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@article{184746,
        author = {Nisha D},
        title = {Intelligent Fault Resilient Manufacturing Framework for Variable Pitch Propeller in Turboprop Engines},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {3492-3511},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184746},
        abstract = {Engines are the powerhouse of an aircraft as it produces thrust, an opposites force which makes the aircraft to move forward. The most commonly used engine is the gas turbine engine which functions based on the Brayton cycle. It consists of five distinct processes namely Inlet, Compressor, Combustion Chamber, Turbine, and Exhaust. Among these, the turbine blades play a crucial role in thrust generation as it should withstand higher temperature. Hence, they are more susceptible for failures due to assorted reasons like thermal fatigue, crack development, creep deformation, corrosion and other physical damages. The physical examination of these defects is a very tiresome process and shall lead to misleading judgment. To alleviate this issue, Artificial Intelligence (AI) based techniques shall be deployed to detect the presence of defects by exploring the health data obtained from the turbine blades. The proposed research work focuses on designing a comprehensive framework using Deep Convolutional Neural Networks (D-CNN) for detecting the faults in engine blades by learning the intricate patterns and trends in the images. As the D-CNN models can manage extensive amounts of data and can be expanded to inspect numerous turbine blades effectively and also models can be upgraded and retrained to identify new types of flaws to adjust various turbine blade designs.},
        keywords = {Gas turbine engine, Faculty detection, AI, Corrosion.},
        month = {September},
        }