Physics-Informed Neural Networks (PINNs) are computationally efficient tools for addressing inverse problems in solid mechanics, but often face accuracy limitations when compared to traditional methods. We introduce a refined PINN approach that rigorously enforces certain physics constraints, improving accuracy while retaining the computational benefits of PINNs. Unlike conventional PINNs, which are trained to approximate (differential) equations, this method incorporates classical techniques, such as stress potentials, to satisfy certain physical laws. The result is a physics-enforced PINN that combines the precision of the Constitutive Equation Gap Method (CEGM) with the automatic differentiation and optimization frameworks characteristic of PINNs. Numerical comparisons reveal that the enforced PINN approach indeed achieves near-CEGM accuracy while preserving the efficiency advantages of PINNs. Validation through real experimental data demonstrates the ability of the method to accurately identify material properties and inclusion geometries in inhomogeneous samples.

Existing interface models are often inaccurate when modeling delamination of Fiber-Reinforced Polymer (FRP) structures, because they do not account for the non-local effects resulting from extrinsic failure mechanisms. Indeed, local traction separation laws are valid only for simple processes with well-defined delamination fronts. In contrast, non-local bridging events, such as fiber or ligament bridging, can significantly modify the dissipation and depend on the global kinematics of the crack front. We have conceived an approach for modeling delamination of FRP, which enriches the cohesive zone model with a delamination tracking technique, allowing for a comprehensive exploration of the diverse non-local features of interlaminar fracture. In our methodology, the evolving delamination front is identified, followed by the quantification of the effects from multiple mechanisms related to the non-local extrinsic dissipation. By simulating two experimental scenarios featuring with evolving configuration of delamination front, we highlight the robustness of this approach for predicting delamination features in the context of extrinsic energy dissipation. Our approach can be viewed as a versatile toolbox allowing to incorporate the influence of various non-local extrinsic mechanisms.

Metamaterials are a cla b of materials with extraordinary capabilities derived from their engineered structure. In 2017, Frenzel et al. (Science 358, 2017) conceptualized "three-dimensional mechanical metamaterials with a twist,"which convert linear deformation into rotational motion - a mechanical response that surpa bes the bounds of Cauchy continuum mechanics. However, the lack of suitable manufacturing technologies for producing metallic twist metamaterials has precluded their experimental validation and potential applications to date. Herein, 316L-stainle b-steel twist metamaterials are fabricated at various scales using an emerging sintering-based technology - lithography metal manufacturing (LMM) - and their mechanical responses are tested using stereo digital image correlation. Results provide the first empirical evidence of compre bion-induced rotation in steel twist metamaterials and highlight their sensitivity to manufacturing defects.

Multiprocess additive manufacturing (MPAM) unlocks new materials and design spaces where multimaterial components consisting of polymers, metals, and ceramics can be produced as one consolidated part. MPAM enables state-of-the-art 3D-printed electronics and devices with embedded functionality by combining fused deposition modeling with chemical deposition and electroplating processes. However, the metalized plastic devices produced by these processes have a short lifespan because of their poor structural integrity due to the low adhesion at the metal–polymer interface. In this study, we elaborated on the adhesion mechanism at the 3Dprinted metal–polymer interface and identified the MPAM factors that elevated significantly the integrity of metalized plastic components. The effects of the 3D-printed surface texture and surface treatment on the adhesion strength of copper plated on acrylonitrile–butadiene–styrene parts were analyzed. We found that a certain 3D-printed topography modified by quick acid etching created a hierarchically structured interface with superimposed macroscale, microscale, and nanoscale roughness that symbiotically improved the metal–polymer adhesion. These results have practical implications for automated equipment manufacturers and the electronic industry adapting MPAM for the 3D printing of multimaterial components and devices with embedded functionality.

Metamaterials possess properties not found in nature and are expected to revolutionise the design of structural components. However large-scale production of metallic metamaterials remains locked due to the compromise between print size and resolution in existing metal 3D printing methods. We unlock the possibility of 3D printing of stainless steel metamaterials across scales using lithography metal manufacturing, a vat photopolymerisation technology that uses digital light processing (DLP) on metal-filled resin to 3D print a green body for further debinding and sintering in a furnace. Here in, were explore the effects of energy dose on overpolymerisation, minimal feature size, and print resolution as well as the effects of sintering temperature on microstructure, shape stability, and mechanical properties of 3D printed metamaterials. It has become possible to 3D print steel metamaterials with a twist and auxetic metamaterials with micro-scaled structures on a decimetre scale. Our benchmarking experiments demonstrate that lithography metal manufacturing competes with laser powder bed fusion regarding print accuracy, surface roughness, and design freedom and provides a viable solution for translating metallic metamaterials from laboratories to markets.

This paper proposes a data-driven method to predict mechanical responses for structures directly from full-field observations obtained on previously tested structures, with minimal introduction of arbitrary models. The fundamental concept is to directly use raw data, called patches from hereon, comprising displacement fields over large domains, obtained during data harvesting through full-field measurement. These displacement fields have been observed on domains of real structures, and hence are naturally viable solutions from static, kinematic, and constitutive viewpoint. We compile a library of such patches to compute response for new structures. Patches are assembled as pieces of a jigsaw puzzle, similar to how Frankenstein put his monster together from human patches. The approach is illustrated using a traditional beam problem for simplicity. However, the approach is not limited to beam or even solid mechanics, the concept can be applied to predict a wide range of physics and multi-physics phenomena.