Physics of Manufacturing: Assembly and Design Optimization

Related lectures (30)

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Covers the Newton's local method in Python using NumPy for optimization.

Covers the problem statement of assembly, precision requirements, common couplings, stability, and spatial vectors.

Explores optimization, simulation, data analysis, and the importance of considering more than just the mean in engineering systems.

Explores assembly processes, labor costs, joining methods, and automation challenges in manufacturing.

Explores multi-task learning for accelerated chemical reaction optimization, showcasing challenges, automated workflows, and optimization algorithms.

Covers exercises and solutions in translating text problems into linear programming and optimizing decision-making processes.

Discusses stakeholder identification in the product value chain and related engineering specifications.

Discusses the ongoing project of optimizing energy management in a smart grid lab.

Explores laser manufacturing physics and metal shaping processes, including strain hardening and recrystallization.

Covers the optimization with constraints, focusing on the Karush-Kuhn-Tucker (KKT) conditions.