The cost of guessing
When engineers develop a new metal printing process, the standard approach is trial-and-error. Print a test piece. Inspect it. Adjust the parameters. Print again. Repeat until the result is acceptable.
Industry data shows that this takes 8 to 12 test prints per new part type on average. Each failed print wastes material, machine time, and engineering hours. At typical rates, development costs can add up to €34,000 to €84,000 per part type.*
What simulation can change
Instead of running every parameter combination on the machine, you run them in simulation first. An overnight run on cloud GPUs can evaluate 25 to 125 different parameter sets: laser power, scan speed, layer height, and hatch spacing in every combination that matters.
The simulation shows what happens in each case: melt pool shape, temperature distribution, solidification behavior, defect formation. You can see which parameters produce stable tracks and which ones lead to porosity or lack of fusion.
A model calculation
Based on industry averages, the following scenario can be derived:
- Test prints could drop from 8-12 to 2-3, because only parameters that simulation has already identified as promising are printed
- Development time could shrink from months to weeks, because the simulation runs overnight instead of over weeks of machine time
- Material waste could drop by up to 75%, because fewer failed prints means less scrap metal
For a company developing 3 new part types per year, this scenario would result in annual savings in the six-figure range.
These numbers are a model calculation, not a validated result. Actual savings depend on material, process type, and part complexity. This is exactly what we aim to validate with real data in our first pilot projects.
The bottom line
Simulation does not eliminate physical testing. You still need validation prints. But instead of using the machine as your search tool, you use it as your confirmation tool. The expensive exploration happens in software. The machine only runs the parameters that simulation has already identified as promising.
That is a fundamentally different way to develop processes.
*Industry averages based on conversations with AM users and published literature.