International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 08 | Aug 2025
p-ISSN: 2395-0072
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The Role of Artificial Intelligence in Jet Engine Development and Lifecycle Management Sujith Kumar Tammali, Anasuri Chanikya, Venkatareddy Chimalamarri Cyient.Ltd ---------------------------------------------------------------------***--------------------------------------------------------------------the aerospace sector. Gas turbines, the foundation of Abstract - The increasing integration of artificial
contemporary aircraft power, demand extraordinary accuracy, dependability, and maintainability. Deterministic simulations, manual design loops, and fixed-interval maintenance are examples of traditional development techniques that are frequently labor-intensive, expensive, and slow. Data-driven methods that automate, speed up, and optimize engineering and support activities are now made possible by advances in AI, which are driven by a rise in sensor data and processing power. Important uses include digital twin-based assembly planning with AR/VR technologies, AI-enhanced additive manufacturing with realtime defect detection, and generative design and surrogate modeling for quicker engine development. AI in service facilitates real-time anomaly detection, predictive maintenance, and remaining usable life estimation. Additionally, it enhances aftermarket operations by anticipating spare components, creating service bulletins, and automating diagnostics.
intelligence (AI) into aerospace systems has brought about significant changes in the design, development, and lifecycle management of gas turbine engines. This paper offers a comprehensive analysis of AI applications in engine lifecycles, encompassing conceptual design, manufacturing, assembly, maintenance, and aftermarket operations. AI expedites computational workflows throughout the design phase using generative design [1], surrogate modeling, and inverse analysis techniques, enabling faster iterations and improved optimization. By facilitating automated defect detection, adaptive parameter control, and real-time process monitoring, artificial intelligence (AI) enhances manufacturing productivity and part quality, particularly in additive manufacturing (AM). AI-assisted digital twins and immersive AR/VR technologies are utilized during engine building to improve adherence to complex assembly procedures, enable virtual planning, and reduce human error. During the maintenance and operation phase, artificial intelligence (AI) makes predictive maintenance possible by analyzing sensor data, estimating remaining usable life [5] [13], and identifying issues early on. It also makes it easier to create and administer Cleaning, Inspection, and Repair (CIR) manuals automatically, which makes maintenance more dependable and efficient. Artificial intelligence (AI) improves the aftermarket by generating service bulletins, predicting replacement components, and enhancing failure diagnostics through data mining, natural language processing, and machine learning models [4] [12]. These characteristics permit improved fleet reliability, reduced downtime, and increased engine availability. The paper highlights important case studies and technological frameworks that demonstrate the usefulness of integrating AI in the gas turbine industry. By integrating recent advancements and identifying emerging trends, this paper emphasizes the critical role of AI in providing intelligent, flexible, and digitally connected propulsion systems for the next generation of aerospace applications.
With an emphasis on real-world applications, case studies, and upcoming prospects for intelligent propulsion systems, this paper examines the present status of AI integration across the design, manufacturing, assembly, maintenance, and aftermarket support of gas turbine engines.
2. METHODOLOGY / RESEARCH APPROACH Based on a thorough analysis of contemporary research, case studies, and industrial applications of artificial intelligence (AI) in gas turbine engine design and lifecycle management, this paper uses a qualitative and analytical research methodology. The study identifies important trends, frameworks, and applications of AI technology in the aerospace industry by combining data from peer-reviewed journals, technical reports, white papers, and industry publications. Relevance, citation impact, and practical usefulness in fields including digital manufacturing, predictive maintenance, aftermarket service, and computational design were taken into consideration while choosing sources.
Key Words: Aerospace, Lifecycle, AR/VR technologies, Artificial intelligence
Beginning with AI applications in the design stage and moving through manufacturing, assembly, health monitoring, maintenance, and aftermarket services, the paper is organized to follow the historical lifecycle of a gas turbine engine. The paper identifies and analyzes particular AI approaches at each step of the lifecycle, such as digital twin
1.INTRODUCTION The application of artificial intelligence (AI) throughout the gas turbine engine lifespan is causing a major upheaval in
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