The training data problem
Every AI and machine-learning model has the same dependency: data. Thousands of labeled experiments. Carefully curated datasets. Years of accumulated results. That works well when you are optimizing a process that has been running for decades.
But what happens when you need to print a material that nobody has printed before?
New titanium alloys, metallic glasses, high-entropy alloys — these materials are appearing faster than anyone can build training datasets for them. An AI model trained on Ti-6Al-4V tells you nothing useful about a novel copper-nickel composite. The patterns simply do not transfer.
Physics does not need a history lesson
A physics-based simulation starts from material properties: density, viscosity, surface tension, thermal conductivity. These are measurable quantities. You can look them up or determine them from a small number of experiments.
From there, the simulation computes what happens when a laser beam hits that material. How the melt pool forms. How it flows. How it solidifies. None of this depends on historical print data. It depends on the laws of thermodynamics and fluid mechanics, which apply to every material equally.
Why this matters for your timeline
If you are developing a process for a new material, the AI path means months of trial-and-error to generate the training data the model needs. The physics path means you can start simulating on day one, using nothing more than the material datasheet.
That is the difference between spending six months building a dataset and getting actionable process parameters in a week.
For established materials with massive datasets, AI can be a useful optimization layer. But for anything genuinely new, physics is the only simulation approach that works from the start.