Physics-Informed Network Models: a Data Science Approach to Metal Design
Source abstract
Abstract
Abstract Functional graded materials (FGM) allow for reconciliation of conflicting design constraints at different locations in the material. This optimization requires a priori knowledge of how different architectural measures are interdependent and combine to control material performance. In this work, an aluminum FGM was used as a model system to present a new network modeling approach that captures the relationship between design parameters and allows an easy interpretation. The approach, in an un-biased manner, successfully captured the expected relationships and was capable of predicting the hardness as a function of composition.
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Research context
OpenAlex topics: Machine Learning in Materials Science · Anodic Oxide Films and Nanostructures · Thermal properties of materials
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References (39)
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