PINNs

This course is designed to provide a deep understanding of Physics-Informed Neural Networks (PINNs) and their applications in the Manufacturing & Production industries. Participants will learn how to integrate physical laws and domain knowledge into neural network models, enabling them to solve complex problems in manufacturing processes, such as injection molding, additive manufacturing, and process optimization. The course covers the theoretical foundations of PINNs, as well as practical implementation strategies and case studies tailored to the specific challenges and requirements of the Manufacturing & Production industries.

• Understand the theoretical foundations and advantages of Physics-Informed Neural Networks in the context of Manufacturing & Production

• Formulate and implement PINNs for solving PDEs relevant to manufacturing processes

• Incorporate domain knowledge and physical constraints into neural network architectures for manufacturing applications

• Evaluate and interpret the performance of PINN models using appropriate metrics and visualization techniques

• Develop and deploy PINN-based solutions for real-world problems in the Manufacturing & Production industries

1. Introduction to PINNs for Manufacturing & Production

• Overview of PINNs and their advantages over traditional numerical methods in manufacturing

• Mathematical formulation of PINNs for solving PDEs in manufacturing processes

• Comparison of PINNs with other machine learning approaches used in Manufacturing & Production

• Hands-on exercises: Implementing a basic PINN for solving a simple manufacturing-related PDE

2. PINNs for Injection Molding and Polymer Processing

• Governing equations in injection molding and polymer processing (e.g., Navier-Stokes, heat transfer)

• Formulating PINNs for problems in mold filling, cooling, and warpage prediction

• Applying PINNs to optimize injection molding process parameters

• Hands-on exercises: Developing PINN models for specific injection molding case studies

3. PINNs for Additive Manufacturing and 3D Printing

• Governing equations in additive manufacturing (e.g., heat transfer, phase change, residual stress)

• Formulating PINNs for problems in 3D printing process simulation and optimization

• Applying PINNs to predict and control part quality, microstructure, and mechanical properties

• Hands-on exercises: Implementing PINN models for specific additive manufacturing case studies

4. PINNs for Process Optimization and Control

• Formulating PINNs for process optimization and control problems in manufacturing

• Integrating PINNs with traditional optimization techniques (e.g., genetic algorithms, gradient-based methods)

• Applying PINNs to real-time process monitoring and control

• Hands-on exercises: Developing a PINN-based process optimization and control system for a manufacturing use case

5. Deployment and Future Directions in Manufacturing & Production

• Deploying PINN models in production environments for manufacturing applications

• Strategies for model validation, testing, and maintenance in Manufacturing & Production

• Scalability and computational efficiency considerations for industrial-scale PINN deployment

• Future research directions and open challenges in PINNs for Manufacturing & Production industries

• Hands-on exercises: Deploying a PINN model for a manufacturing use case and discussing deployment strategies

• Strong understanding of partial differential equations (PDEs) and numerical methods

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with manufacturing processes and related physics concepts (e.g., fluid dynamics, heat transfer, materials science)