Generative Design In Aerospace And Automotive Structures
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Generative Design in Aerospace and Automotive Structures
Author: Jody Emlyn Muelaner
language: en
Publisher: SAE International
Release Date: 2024-07-23
Semi-automated computational design methods involving physics-based simulation, optimization, machine learning, and generative artificial intelligence (AI) already allow greatly enhanced performance alongside reduced cost in both design and manufacturing. As we progress, developments in user interfaces, AI integration, and automation of workflows will increasingly reduce the human inputs required to achieve this. With this, engineering teams must change their mindset from designing products to specifying requirements, focusing their efforts on testing and analysis to provide accurate specifications. Generative Design in Aerospace and Automotive Structures discusses generative design in its broadest sense, including the challenges and recommendations regarding multi-stage optimizations. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024016
Introduction to Generative Design for Aerospace Applications
This book provides a comprehensive introduction to generative design in the aerospace sector, guiding readers from initial creative concepts to the final stages of manufacturing. It offers a thorough exploration of sustainable production methods, cutting-edge technological advancements, and the principles of generative design. With a focus on artificial intelligence's role in optimizing design and manufacturing, this book also delves into topology optimization, offering valuable insights for both newcomers and experienced professionals in the field. Through practical examples and a structured approach, readers will gain a solid understanding of the innovations shaping the future of aerospace design.
Innovative Machine Learning Applications in the Aerospace Industry
Author: Ponnada, Venkata Tulasiramu
language: en
Publisher: IGI Global
Release Date: 2025-06-17
The aerospace industry evolves with the integration of machine learning (ML) applications. From optimizing flight operations and predictive maintenance to advancing autonomous navigation and air traffic management, ML enables efficiency, safety, and performance. As aerospace systems grow more complex, ML offers the ability to analyze data in real-time, uncover hidden patterns, and support intelligent decision-making. This emerging collaboration between aerospace engineering and AI reshapes traditional practices while opening new frontiers in exploration and innovation. Innovative Machine Learning Applications in the Aerospace Industry explores the potential of machine learning applications, examining its impact on various sectors. It investigates the diverse realms of machine learning applications and their profound implications for the future. This book covers topics such as drone navigation, aerial images, and computer vision, and is a useful resource for business owners, engineers, academicians, researchers, and computer scientists.