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Guided learning journeys that build knowledge step by step.
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This learning path guides senior and graduate engineering students through the principles and practical implementation of green and sustainable manufacturing. It covers energy efficiency, waste reduction, and eco-materials, providing a structured approach to integrating environmental considerations into manufacturing processes.
This path takes senior and graduate students from foundational manufacturing systems to advanced Industry 4.0 concepts, covering IoT, digital twins, cyber-physical systems, and data analytics. It emphasizes the integration of physical production with digital technologies and provides a career-oriented perspective on smart manufacturing.
This path covers the fundamental materials science and manufacturing processes for advanced fiber-reinforced composites. Learners will explore the key constituents, processing methods, and quality considerations for producing composite parts, with a focus on layup, autoclave curing, resin transfer molding, and filament winding.
A comprehensive learning path for senior and graduate students to understand semiconductor fabrication processes, covering wafer fabrication, lithography, etching, deposition, and the underlying materials science. The path builds from fundamental principles to advanced process integration and control.
This learning path equips senior engineering students with the knowledge and skills to apply robotics in manufacturing. It covers robot types, kinematics, programming, and integration into manufacturing systems, emphasizing practical applications and system-level thinking.
This learning path provides a comprehensive understanding of Computer-Integrated Manufacturing (CIM), covering its core principles, enabling technologies, and implementation strategies. Learners will explore the integration of manufacturing systems, data communication, databases, and manufacturing execution systems, and apply this knowledge to real-world scenarios.
This learning path equips senior manufacturing engineering students with the knowledge to design and operate flexible manufacturing systems (FMS). It covers FMS components, control architectures, scheduling, and real-world case studies, building from foundational manufacturing concepts to advanced system design and operation.
This learning path equips senior or graduate students with the skills to apply data analytics to manufacturing operations. It covers data collection, analysis, predictive modeling, and visualization, grounded in statistics and programming, with a focus on real-world manufacturing challenges.
This advanced learning path equips senior or graduate students with the skills to apply numerical modeling, simulation, and machine learning to manufacturing problems. It covers essential mathematics, programming, computational mechanics, process modeling, and data-driven optimization, culminating in integrated projects that mirror real-world manufacturing challenges.
This learning path equips graduate students with advanced statistical methods for quality engineering, focusing on design of experiments, robust design, and reliability. It builds from foundational probability and statistics through experimental design, robust parameter design, and reliability analysis, culminating in integrated case studies.