Research Areas

Our research is motivated by the intellectual challenges in fracture and statistical mechanics of architected materials, ordered/disordered materials, and heterogeneous materials by design. We are also interested in the deign-manufacturing synergy, particularly how advancing the robotics in additive fabrication (i.e., feedback control and path planning) under uncertainty and unknown conditions can help push the boundaries in the design of complex materials (i.e., architected). Our research uses experiment, simulation, and theory to understand the mechanics of intrinsically brittle architected materials with toughening mechanisms inspired by biological materials. At the robotics-material interface, our group develops advanced manufacturing methods such as autonomous robotic additive manufacturing and closed-loop control, laser processing, multi-material direct-ink-writing, that can enable experimental realization of advanced architected composite materials. At the mechanics-material interface our group develops numerical frameworks that allow for engineering damage-resistant materials or previously unexplored designs, such as coupled phase-field and cohesive zone model. We use statistical mechanics approaches to understand, quantify, and use disorder as a design parameter to engineer the solid, pore, or defects in brittle construction materials. This approach enables a statistically meaningful control over the arrangement and the structure-property relationship. Our research is supported by several NSF programs including Engineering Civil Infrastructure, Advanced Manufacturing, CAREER.

Fracture Mechanics Architected Cement-based Materials using Experiment, Simulation, and Theory

Engineering the mechanics of cement-based materials through design, experimentation, and simulation of single- or multi-materials compositions allows for the development of advanced infrastructure components. In this theme, we develop experimental and numerical methods to understand and engineer the fracture mechanics of architected brittle materials such as cement-based layered and complex composites. Recent advancements in architected materials have shown promising applications across fields, including civil and aerospace materials and structures. Architected multi-materials, such as hard-hard materials with weak interface (e.g., 3D-printed concrete) and hard-soft material assemblies with tunable properties, can be realized given the advancement in additive manufacturing techniques. These materials exhibit complex failure mechanisms, such as crack deflection, penetration, and bridging, due to the often heterogeneous or non-uniform arrangement of materials of different constitutive properties. These mechanisms are crucial in determining key bulk characteristics like fracture toughness and strength in these composite materials. One important challenge in capturing fracture behavior in hard-hard and soft-hard assemblies is the existence of interface regions that require special considerations in numerical simulation. To address this challenge, we present a numerically robust constitutive framework for modeling fracture behavior in architected materials composed of alternating soft elastomers and hard cement paste in bilayer assemblies. The framework utilizes the phase-field approach to capture crack propagation within the bulk of the hyperplastic soft and elastic hard materials. Furthermore, to understand the contribution of the interface to the fracture energy potential, the constitutive relationships are further supplemented with a Cohesive Zone Model (CZM). The unified framework is implemented in the user-element subroutine (UEL) within Abaqus and incorporates a large-deformation extension of the PPR CZM. The coupled phase-field CZM framework allows for the prediction of complex cracking phenomena in architected multi-materials (e.g., deflection, penetration, and bridging). It also allows for accurate predication of fracture in hard-hard materials (bilayers) with an interface of a given orientation as validated against LEFM theory. Furthermore, we illustrate the effectiveness of the proposed framework in capturing the toughening mechanisms anticipated in soft-hard assemblies enabled through the intelligent design of such materials architecture and advanced additive manufacturing processes. The combined experimental and numerical approaches allow for exploring and designing toughening mechanisms in a wide range of architected single- or multi-material assemblies enabled by advanced additive manufacturing processes. The numerical approaches can apply to a wide range of natural (layered rocks) and engineered materials (layered composites, additively manufactured materials) that contain interface which can determine the faith of the overall mechanical response.

Performance of Heterogeneous Cement-based Materials from a Statistical Mechanics and Disorder Perspective

Advanced manufacturing or digital fabrication processes with cement-based materials have the ability to purposefully construct the arrangement of the matter (e.g., solid, pore). These heterogeneities can range from weak interfaces and layered defects in layered materials, such as those induced in additively manufactured concrete, to laser-engraving of flaws on the surface or bulk arrangement of layered hard-hard or hard-soft composites. The processing-induced heterogeneities in the bulk and interfaces of layered materials represent a major challenge to resulting mechanical performance. However, exploiting the heterogeneity also presents an opportunity for tailoring the mechanics and durability of digitally fabricated materials. The active engineering of heterogeneity in brittle materials requires a robust design framework that can first quantify information about the arrangements of the matter. Statistical mechanics is a key approach that we focus on, which provides a powerful tool to capture and quantify order in arrangements of heterogeneous natural or engineered (e.g., bio-inspired) architected materials and, therefore, provides a unique lens toward the computational design of materials and structures. However, these powerful methods to quantify a spectrum of order-to-disorder arrangements have not been widely used in the domain of engineering design of materials and arrangement of cement-based materials across scales. Quantifying heterogeneity through the lens of disorder using statistical mechanics parameters (e.g., radial distribution function, g2(r), translational order parameter (T), and orientational order parameter (Q), enables pathways to design. These approaches can quantitatively characterize the degree of disorder for describing the representation of the architected arrangement of materials in lieu of otherwise “periodicity” classification and misperceived disorder parameters (perturbation and Voronoi tessellation methods). This approach can quantify heterogeneity in layered, 3D-printed, and architected materials with complex arrangements. It allows the design of purposeful arrangements and meta-materials and engineering the mechanical and functional performance. It can also formalize a quantifiable metric into the design of advanced materials and further exploit the capabilities offered by the growing digital fabrication and advanced manufacturing methods. The ability to design for heterogeneity can help address the failure, fracture, damage, and long-term durability of conventional and novel cement-based materials and structures.

Robotics and Advanced Manufacturing

Robotic additive manufacturing provides a pathway to generate complex and customized materials and structures across scales. However, the methods and path generation algorithms used in additive manufacturing have limited capacity to unleash the autonomy that resides in using robotic fabrication. Key approaches that our research focuses on are advancing the control over the object (material, component) and autonomous path planning methods. Extrusion-based additive manufacturing with suspensions (e.g., concrete) is one of the main approaches in digital fabrication. We develop pressure-based feedback control systems that deal with material uncertainty at an upstream level in the extrusion-based robotic fabrication process to address the reliability and robustness of the manufacturing process. We also develop autonomous path-planning algorithms that handle uncertainty and unknown conditions and little prior information. Through advancing the robotics in additive manufacturing, the ability to carry out fabrication more autonomously and reliably is enhanced, while new and more complex designs are facilitated. We have advanced the manufacturing processes and methods for fabrication in a wide range of scales including multi-material cementitious-polymeric additive manufacturing (at desktop scale), laser-processing brittle materials (at the desktop scale), developing automatic path (g-code) generation algorithms for wide range of complex designs (e.g., vascular channels, interlocking bodies) using grasshopper (at desktop scale), mid-scale syringe-based extrusion process with pressure sensing (at benchtop scale with Scara, 1.3m), large-scale robotic additive manufacturing with industrial robots using a single-component (with ABB IRB 4600, 1.65m/60kg payload) and two-component extrusion (with ABB IRB 6700, 2.85m/150 kg payload – on 8m track), integration of control of material’s continuous extrusion parameters (pressure, flow rate) with the robot’s toolpath parameters, sensing of the relevant parameters (pressure, flow rate, temperature) throughout the process.