The mechanical behavior of materials is governed by mechanisms spanning multiple length scales, ranging from atomic arrangements at the nanoscale, through grain structures at the microscale, to bulk material responses at the macroscale. Given the hierarchal nature of material structures, we use the multiscale mechanics framework, integrating both advanced experimental and computational techniques, to understand the fundamental mechanisms across different length scales that collectively determine engineering properties like strength and ductility. Through this understanding, we develop physics-based models that bridge the gap between microstructural features and macroscopic mechanical properties, allowing us to design new materials through control of their structure and to more accurately predict their behavior.

The ductile fracture behavior of metals depends on the stress state under which they are loaded, and the size and distribution of defects. To efficiently and safely use these materials, predictive models that describe the failure of these materials under realistic complex loading states are needed. Our lab uses a combined experimental-computational approach to investigate and model large deformation and fracture behavior. Experimentally, we use a variety of sample geometries and load frames, including a custom-built dual-actuator mechanical test frame, to measure the multiaxial deformation behavior of a range of metal alloys, from advanced high strength steels to additively manufactured alloys. We couple these experiments with computational simulations to develop plasticity and fracture models for these materials. To validate these models for real-world applications, we test them on complex structures, and seek agreement between experiments and the simulations.​

In additive manufacturing (AM) of metals, 3D components are built in a layer-by-layer fashion using different processes including powder-based, laser-based processes of powder bed fusion (PBF) and directed energy deposition (DED).  In both processes, a 3-dimensional component is sliced into 2-dimensional layers.  Powder feedstock is delivered to a location within the 2D layer, melted with a laser, and fuses to the layer below upon cooling and solidification.  AM technology allows for the fabrication of design-driven components rather than manufacturing process-dependent geometries. However, an understanding of the mechanics of materials made by AM is required for the adoption of AM in load-bearing applications.  Our group works to understand the multiaxial mechanical behavior of materials made by AM through combined experimental and computational methods. 

Our group uses machine learning to identify process-structure-property relationships in additively manufactured materials.  In particular, we use ML, data-driven models, and feature importance analyses to identify the linkages among metrics that describe the processing, micro- /meso-structure, and mechanical properties in additively manufactured materials.  We also apply computer vision and deep learning to identify how in process signals are indicative of location-specific mechanical properties.  Our methods allow for the real-time prediction of mechanical properties during sample fabrication, with application to process diagnosis and control during AM.

Compositionally functionally graded materials (FGMs) are materials with spatially tailored compositions, and thus, properties within a single component. FGMs can be fabricated using directed energy deposition (DED) additive manufacturing (AM) in the fractions of two or more metal feedstocks deposited during fabrication are varied. Our group studies the design and fabrication of metallic FGMs through considering both printability and mechanical properties using computational simulations and experimental characterization. We use thermodynamic simulations to calculate the predicted phase formation and hot cracking susceptibility as a function of composition to assess printability and predict the properties (e.g., mechanical) as a function of composition. These criteria and properties are then used to design and fabricate FGMs with spatially tailored properties.

Alloys currently used in additive and advanced manufacturing processes are primarily limited to those designed for welding and casting, thus, they are not optimized for the complex thermal histories seen in fusion-based AM of relatively rapid solidification followed by repeated thermal cycling with the addition of layers. Understanding and designing advanced alloys for AM requires knowledge of non-equilibrium microstructure as a function of alloying elements and thermal history. We use computational methods informed and validated by experimental methods to develop alloys specifically tailored for AM.