Physics-Informed Neural Networks (PINNs) for Quality Management 

This course equips quality professionals with the knowledge and skills of Physics-Informed Neural Networks (PINNs). PINNs offer a revolutionary approach that combines the power of neural networks with the fundamental laws of physics. You'll explore the core principles of PINNs, delve into their applications for quality control tasks involving physical systems, and gain the skills to leverage them for improved analysis and prediction capabilities. This empowers you to gain deeper insights into complex quality control problems, optimize processes, and ultimately enhance product quality. 

• Explain the concept of Physics-Informed Neural Networks (PINNs) and their ability to integrate physical laws into the learning process of neural networks.

• Understand the core mathematical framework of PINNs, including the loss function that incorporates both data fitting and physical constraint terms.

• Identify the key benefits of using PINNs for quality control tasks compared to traditional neural networks, particularly for problems involving physical systems (e.g., heat transfer, fluid flow).

• Explore real-world applications of PINNs in quality management, such as predicting product behavior under various conditions, optimizing manufacturing processes based on physical principles, and anomaly detection in sensor data with a physics-based understanding.

• Utilize a user-friendly platform or library (e.g., PyTorch) to implement and train a simple PINN model on a quality control scenario involving a physical system (e.g., simulating heat dissipation in a product).

• Evaluate the strengths and limitations of PINNs, including challenges in data requirements and model interpretability.

• Discuss the future potential of PINNs in quality control and their impact on various quality management practices, such as real-time monitoring and control of physical processes.

• Develop a high-level plan for exploring the potential application of PINNs to a specific quality control challenge within your company, considering the type of physical system involved and the expected benefits.

1. Unveiling the Power of Physics-Informed Learning:

• Highlighting the limitations of data-driven approaches in quality control for physical systems and introducing PINNs as a solution.

• Delving into the core concept of PINNs, exploring how they incorporate physical laws represented by partial differential equations (PDEs) into the neural network architecture.

• Case Study 1: Analyzing a real-world scenario of using a PINN model to predict the temperature distribution within a manufactured product, enabling optimization of the cooling process for improved quality.

• Exploring different quality control challenges within your company that involve physical systems and discussing how PINNs could be potentially applied.

• Hands-on Session 1: Utilizing a user-friendly platform or library (e.g., PyTorch) to set up a simple PINN model for a basic physical phenomenon (e.g., heat transfer) relevant to quality control.

• Hands-on Session 2: Training the PINN model developed in Session 1 on a quality control-related dataset involving a physical system (e.g., sensor data from a manufacturing process).

• Understanding how to interpret the predictions made by the trained PINN model in the context of the specific quality control challenge.

• Advanced applications of PINNs in quality control, such as inverse problems (e.g., identifying material properties based on sensor data) or control optimization using PINNs. 

• 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 fluid mechanics, biomechanics, and physiological modeling concepts