|About this Abstract
||Materials Science & Technology 2019
||Advanced Manufacturing, Processing, Characterization and Modeling of Functional Materials
||Computational Design of Additively Manufactured Compositionally Graded Alloys
||Tanner Kirk, Olga Eliseeva, Richard Malak, Raymundo Arroyave, Ibrahim Karaman
|On-Site Speaker (Planned)
The manufacturing of Functionally Graded Materials (FGMs) has been revolutionized by the layer-by-layer compositional control of Directed Energy Deposition (DED) processes. However, compositional grading between metal alloys is often complicated by the presence of deleterious phases in the interlayers. This work presents a computational design methodology that leverages CALPHAD modeling and robotic path planning algorithms to plan gradient paths between alloys. The methodology is capable of planning gradients that avoid deleterious phases in multicomponent systems that would be difficult or impossible to visualize. Furthermore, gradient paths can be optimized to minimize thermal stress gradients or to improve performance via user-defined property metrics. Extensions that incorporate the uncertainties of the models and the processing conditions are also explored. Case studies are presented that demonstrate the method’s effectiveness in the discovery of optimal gradients. Experimental results are also shown that validate the methodology via the manufacturing and characterization of designed gradients.