Afbeelding: Topology optimization for efficient material use and a holistic, consistent, and an open CAE environment for injection molding and structural solvers create efficient CAE workflows | Copyright: Altair
This is where AI helps to fill in the data gap: AI enables better predictions from measured and simulated data, helping to save costs and efforts when matching simulation and test data. The almost infinite variety of materials and material combinations in thermoplastics, for example, makes it impossible to test all material variants, especially when taking into account external influences such as aging, chemical reaction, or irradiation of the material.
Altair's data automation and AI solution approaches ...
- increase transparency: users can instantly find existing tests performed on other machines, in other labs, or even in other regions.
- simplify accessibility: using the existing Material Data Center, optimized for storing and accessing material data via browser/API.
- enable better predictions: predict new materials for which there is no prior testing.
In addition, Altair® Monarch® automation templates enable the automatic collection of data from multiple sources, such as test machines, laboratories, or suppliers. As a central database, the Material Data Center simplifies the storage of and access to material data through browser-based interfaces. This allows physical material testing to be reduced by up to 30%, resulting in substantial cost savings through faster time to market.
Transparency, easy access, and better predictions
Altair will present its design solutions for the plastics industry at Fakuma, Friedrichshafen, Germany, October 12-16, in Hall B1, Booth B1-1225. Highlights at the booth are: design-related simulation for component and tool development, integrative simulation, the Altair® Material Data Center™, and solutions from the Altair AI portfolio.
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