Engineering Artificial Intelligence: Framework, Challenges, and Future Direction
This provides a foundational framework for researchers and practitioners in engineering domains to develop domain-specific AI solutions, though it is incremental in synthesizing existing concepts.
The paper tackles the lack of systematic methodologies in applying AI to engineering problems by proposing the 'ABCDE' elements and an eight-layer ecosystem framework to guide development, aiming to advance strategic implementation for next-generation solutions.
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and eight future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.