Mistras Group
Smart Robotic Cells Powered by Physics-Informed AI Advance Manufacturing
Over the last few years, data-driven artificial intelligence has delivered impressive results in applications such as recommendation engines (e.g., books and movies), playing games, facial recognition, text translation, text synthesis, and fraud detection. This type of AI uses vast data to train the system. In many applications, a large amount of data is readily available such as images on Instagram, and large amounts of text on Wikipedia pages can be easily generated like a computer playing a game against itself. In contrast, collecting high-quality data in manufacturing applications takes significant time and incurs high costs. Unfortunately, a purely data-driven approach for AI is not a scalable model in most manufacturing applications, and we need to look for alternative methods.

What is Physics-Informed AI?

Manufacturing has a lot of known models and valuable process knowledge. Rediscovering these models and knowledge using a purely data-driven approach does not make sense. On the other hand, all known models make simplifying assumptions to reduce complexity and are, therefore, approximate. We need AI that exploits the known models and uses a data-driven approach to augment the models and knowledge based on experimental data to fill the missing gaps. This type of approach is called physics-informed AI. It enforces known physics-based process models or knowledge as a constraint in the AI system to ensure that it does not learn anything that contradicts existing information. For example, the system can enforce a constraint that increasing pressure on the sanding tool will increase the deflection of the sanded part. We donít need to conduct many tests to learn this trend. If the measured data contradicts this constraint, the sensor is likely malfunctioning or the part/tool is not correctly clamped.

How Does Physics-Informed AI Work?

On the one hand, the physics-informed AI approach restricts the solution space, making the problem much more tractable. Consider the problem of predicting process output based on the input. If the output is expected to increase with an increase in the input, then the underlying model space is limited, and a smaller amount of data can train it. We donít need to consider arbitrarily complex models. On the other hand, this requires more complex representations and associated solution generation methods to handle constraints to produce acceptable computational performance. We cannot train a simple neural network with observed input and output data. In this case, there is no guarantee that it would preserve the process constraint if the output used during training is noisy.

Read the full blog post at GreyMatter-Robotics.com.

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