Materials teams want to find new alloys that outperform existing solutions or fill gaps in the market. They do a lot of expensive experiment, testing, and simulation to identify the best candidates. Can they get there faster at lower cost per new material? With Alchemite you can use the data you have to more effectively target your testing and experiment.
Because this data comes from diverse sources and tests, it tends to be ‘sparse’, causing problems for conventional analysis tools. Alchemite is designed to handle this data, and to work for problems with multiple target parameters. It’s ideal (and proven) for design of new alloys/materials.
Alchemite for alloys and superalloys
With the Alchemite software scientists and engineers can apply powerful deep learning methods to design, characterize, and optimise production of alloys. You could use Alchemite to:
- Validate and clean data from testing, simulation, production, or in-service
- Quickly generate models that provide insight into key property-process relationships
- Identify the best test to do next, saving time and cost when characterising materials
- Design new alloys computationally
- Optimize process parameters