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IEMS 490: AI In Manufacturing: Data Analytics


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Prerequisites

Calculus and Linear Algebra Familiarity with programming (Python recommended), Background in manufacturing processes is helpful but not required. Graduate standing or permission of instructor

Description

Also ME 447

This course provides a comprehensive introduction to machine learning (ML) and artificial intelligence (AI) techniques in manufacturing. Many key challenges in modern manufacturing, including demand forecasting, process monitoring, predictive modeling, quality control, and design optimization, are inherently data driven. ML and AI can make these tasks smarter and more effective. Throughout the course, we will explore a broad spectrum of ML and AI tools to address these challenges, with an emphasis on both practical application and an intuitive understanding of the underlying methodological principles. By the end of the semester, you will have developed the skills necessary to tackle complex manufacturing data analytics problems and contribute to AI for manufacturing research.

Course Topics

  • Unsupervised learning for process–structure–property characterization
  • Supervised learning in manufacturing predictive models
  • Multi-modal learning and multi-sensor integration
  • Computer vision for metrology and surface reconstruction
  • Vision foundation models for image-based in-situ monitoring

Prerequisites

  • Calculus and Linear Algebra
  • Familiarity with programming (Python recommended)
  • Background in manufacturing processes is helpful but not required