In industrial production, artificial intelligence is often used to speed up processes or make robotic movements more efficient. Yet the real value lies elsewhere: AI only becomes truly useful for manufacturing when a system can assess physical relationships and base sound decisions on them.
Why physics makes the difference
An algorithm that recognises patterns in data does not automatically deliver usable results on the shop floor. Manufacturing revolves around forces, material properties, temperatures and motion. If a model ignores this physical reality, its conclusions remain superficial. So-called physical AI therefore combines learning systems with knowledge of physical laws.
As a result, these models not only predict what happens but also understand why. That makes them more reliable in situations not exactly represented in the training data — a key advantage in production environments where hardly any two jobs are identical.
Applications in mechanical engineering
In parts of mechanical engineering, this approach already works well. Examples include estimating wear, predicting how materials behave under load, and controlling complex movements where inertia and forces come into play. Concrete cases include:
optimising machining processes based on material properties;
more accurate maintenance by identifying physical wear patterns;
controlling robots that account for dynamic forces.
Robot optimisation is therefore just one application within a much broader development. As sensors become cheaper and computing power grows, manufacturers can hand over more and more processes to models that genuinely grasp physical reality.
What this means for producers
For the manufacturing industry, the challenge lies less in gathering data than in linking that data to solid physical knowledge. Companies that manage to combine engineering experience with AI can make their processes more robust and efficient. Physical AI does not replace technical expertise — it reinforces it.