Intelligent Systems in Engineering: A Framework for the Application of Artificial Intelligence in Mechanical Research

The field of mechanical engineering research is undergoing a paradigm shift driven by integrating Artificial Intelligence (AI) and Machine Learning (ML). Traditional methods, often reliant on computationally expensive simulations and physical experimentation, are being augmented and, in some cases, replaced by data-driven AI models that offer unprecedented speed, accuracy, and insight. This paper explores the transformative application of AI tools across key domains of mechanical research, including materials discovery, design optimization, structural health monitoring, and fluid dynamics. We present a generalized framework for implementing AI solutions, detailing the data acquisition, model selection, and validation processes. Supported by conceptual block diagrams, we illustrate the architecture of AI-driven research pipelines. Finally, we discuss current challenges such as data requirements, model interpretability ("black box" problem), and computational costs, while outlining future directions for this rapidly evolving interdisciplinary field