AI Applications in Mechanical Engineering Design, Manufacturing, and System Optimization

Course Description:

This course provides a comprehensive examination of artificial intelligence applications within mechanical engineering, emphasizing practical implementation within real-world engineering environments. Foundational concepts are introduced to establish an understanding of machine learning, deep learning, computer vision, and optimization algorithms relevant to mechanical systems. The course then explores AI-assisted mechanical design and simulation processes, including generative design, topology optimization, finite element analysis acceleration, multiphysics modeling, materials prediction, and digital twin technologies. Manufacturing applications are examined through machine learning–driven process optimization, quality control automation, robotics coordination, and production scheduling improvements.

Learning Objectives:

Upon successful completion of this course, the participant will be able to:

1. Define fundamental artificial intelligence concepts and terminology relevant to mechanical engineering applications, including machine learning, deep learning, computer vision, and optimization methods.
2. Explain how artificial intelligence technologies are applied in mechanical engineering design and simulation processes, including generative design, topology optimization, finite element analysis support, multiphysics modeling, and digital twin applications.
3. Identify machine learning applications used in manufacturing and production systems, including process optimization, quality control automation, robotics coordination, and production scheduling.
4. Describe the use of artificial intelligence in predictive maintenance and reliability engineering, including condition monitoring, fault detection, remaining useful life estimation, and maintenance planning optimization.
5. Explain how artificial intelligence supports thermal system analysis and energy optimization, including HVAC performance modeling, energy demand forecasting, and equipment efficiency improvement.
6. Evaluate data quality requirements, validation procedures, uncertainty considerations, and reliability factors associated with AI-assisted mechanical engineering analyses.
7. Assess professional liability, ethical obligations, cybersecurity risks, documentation requirements, and standards of care associated with the use of artificial intelligence in mechanical engineering practice.
8. Apply best practices for implementing artificial intelligence technologies within mechanical engineering organizations, including technology selection, workflow integration, workforce training, governance frameworks, and risk management strategies while maintaining engineering judgment and accountability.