C-Y Liu, O. Chong, A. Chatterjee, Y. Yang, O. Chong, Y. Li
Arizona State University,
United States
Keywords: high-tech manufacturing, physical systems, operational efficiency, spatial constraints, autonomous engineering design framework
Summary:
In high-tech manufacturing, physical systems are integral to maintaining operational efficiency and safety but face significant challenges from spatial constraints and multifunctional requirements, leading to frequent pipeline and electrical routing conflicts. To combat these disruptions and enhance system resilience, this paper proposes an innovative Autonomous Engineering Design Framework that harnesses artificial intelligence (AI) and large language model (LLM) to optimize system integration and conflict resolution. This framework is meticulously designed to support automated and dynamic design adaptations, allowing physical systems to respond efficiently to changing operational demands and environmental conditions. It can integrate advanced conflict resolution algorithms and leverage IoT technology for real-time system monitoring and adaptation, thus significantly reducing downtime and enhancing overall system efficiency. The framework employs a modular approach, where components can be independently updated or replaced, facilitating ongoing enhancements and scalability without disrupting existing operations. The framework's illustrates the transformative potential of AI and LLM in engineering design processes, emphasizing the need for continuous technological integration to shield high-tech manufacturing environments against complex and evolving challenges. Future research will focus on expanding the framework's application across various industries and continuously enhancing its capabilities to address new and emerging challenges. This ongoing development will explore the integration of newer AI methodologies and predictive analytics, aiming to further refine the framework's adaptiveness and effectiveness in dynamic industrial settings.