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Additively manufactured auxetics (structures exhibiting a negative Poisson’s ratio) offer a unique combination of enhanced mechanical strength and energy absorption. These properties can be further improved through strategic material placement and architectural design. This study investigates the feasibility of fabricating bi-material rotating-square auxetic structures composed of flexible and rigid constituents in their squares and hinges. Rotating-square auxetic structures are manufactured via material extrusion using rigid polylactic acid (PLA) and flexible thermoplastic polyurethane (TPU) to explore the effects of material distribution on mechanical performance and failure characteristics at the macro (i.e., component) and meso (i.e., cell) scales. Baseline tests are conducted to quantify single- and bi-material interfacial strength and failure modes under normal, shear, and combined loading conditions. Upon validation of interface integrity, single- and bi-material auxetic structures are fabricated and tested in uniaxial compression. Relative to the TPU single-material structure, the PLA square-TPU hinge structure provides a 33% increase in structural stiffness, increases energy absorption, delays the global densification strain by 10%, yields a structural Poisson’s ratio at least 0.3 lower than its single-material counterpart through global axial strains of 20%, and demonstrates partial shape recovery. Multiscale experimental analyses supplemented by a kinematic model reveal the rotation-dependent stiffening mechanisms of these structures, highlighting the benefits of flexible hinge materials. Bi-material structures with flexible hinges are shown to have bilinear trends in structural stiffness and energy absorption, not intrinsic to their single-material counterparts. These findings highlight the potential of bi-material design strategies in advancing the functionality and tunability of auxetic structures for the next generation of mechanical metamaterials. 

Production distributed systems provide rich features, but various defects can cause a system to silently violate its semantics without explicit errors. Such failures cause serious consequences. Yet, they are extremely challenging to detect, as it requires deep domain knowledge and substantial manual efforts to write good checkers. In this paper, we explore a novel approach that directly derives semantic checkers from system test code. We first present a large-scale study on existing system test cases. Guided by the study findings, we develop T2C, a framework that uses static and dynamic analysis to transform and generalize a test into a runtime checker. We apply T2C on four large, popular distributed systems and successfully derive tens to hundreds of checkers. These checkers detect 15 out of 20 real-world silent failures we reproduce and incur small runtime overhead. 

Production distributed systems provide rich features, but various defects can cause a system to silently violate its semantics without explicit errors. Such failures cause serious consequences. Yet, they are extremely challenging to detect, as it requires deep domain knowledge and substantial manual efforts to write good checkers. In this paper, we explore a novel approach that directly derives semantic checkers from system test code. We first present a large-scale study on existing system test cases. Guided by the study findings, we develop T2C, a framework that uses static and dynamic analysis to transform and generalize a test into a runtime checker. We apply T2C on four large, popular distributed systems and successfully derive tens to hundreds of checkers. These checkers detect 15 out of 20 real-world silent failures we reproduce and incur small runtime overhead.