LatticeML: A data-driven application for predicting the effective Young Modulus of high temperature graph based architected materials

arXiv:2404.09470v1 Announce Type: new
Abstract: Architected materials with their unique topology and geometry offer the potential to modify physical and mechanical properties. Machine learning can accelerate the design and optimization of these materials by identifying optimal designs and forecasting performance. This work presents LatticeML, a data-driven application for predicting the effective Young's Modulus of high-temperature graph-based architected materials. The study considers eleven graph-based lattice structures with two high-temperature alloys, Ti-6Al-4V and Inconel 625. Finite element simulations were used to compute …

abstract application arxiv cs.ai cs.hc cs.lg data data-driven design designs forecasting geometry graph machine machine learning materials math.oc modulus optimization performance physics.app-ph topology type work young