Improving the Sensitivity of the Mechanoluminescence Composite through Functionalization for Structural Health Monitoring

Luminescence properties of a green-emitting mechanoluminescent phosphor CaSrGa4O8:xTb3+ without pre-excitation

In this study, both mechanoluminescence (ML) and long persistent luminescence (LPL) characteristics were first observed in CaSrGa4O8 doped with Tb3+ ions, which confirmed that CaSrGa4O8 is a high-quality host for luminescent material research. Notably, the samples show stronger mechanoluminescent intensity with increasing Tb3+ doping. Additionally, the introduction of Tb3+ led to a shift of the thermoluminescence peak towards higher temperatures and a substantial increase in its intensity, suggesting that Tb3+ doping enhances the overall trap concentration and introduces deeper trap energy levels. Presumably, the free carriers in the system recombine upon mechanical stimulation, releasing energy that is transferred to Tb3+ ions. Investigations into the intrinsic structure, matrix effects, and trap evolution of the material confirmed that deep and shallow traps are responsible for the observed ML and LPL phenomena, respectively. The elucidation of the unique luminescent properties of the material provides us with some guidance for the development of new multi-functional luminescent materials.

Snap-through Crack Propagation in Architected Bonded Interfaces Analyzed Using a Mechanoluminescent SAO/E Coating

We investigate the mechanics of crack propagation in architected adhesive joints whose adherends are inspired to the base plate of the barnacle Amphibalanus (=Balanus) amphitrite, and feature an array of buried hollow cylindrical channels located perpendicularly to the direction of crack growth. Selective laser sintering is used to obtain the adherends that are subsequently bonded in the double cantilever beam configuration to ascertain the mechanics of crack growth. Finite element (FE) simulations are deployed to determine the strain energy release rate (ERR) and to elucidate the salient features of the fracture process. It is shown that the channels induce a modulation of the ERR and enable a crack tip shielding mechanism. Besides, FE simulations based on a cohesive zone approach indicate the occurrence of crack pinning/depinning cycles that are validated via experiments. A highlight of the present study is the use of a mechanoluminescent (ML) coating to unravel the evolution of the transient stress field in the crack tip region. The coating comprises an optical epoxy resin loaded with doped strontium aluminate phosphors (SrAl2O4/Eu2+) and converts mechanical energy into light emission with intensity proportional to the magnitude of mechanical stress. By combining the ML emission patterns with the stress distribution obtained from FEA, we unveil interesting details of snap-through cracking in architected bio-inspired adhesive joints.

In situ health monitoring of multiscale structures and its instantaneous verification using mechanoluminescence and dual machine learning

Extensive changes in the legal, commercial and technical requirements in engineering fields have necessitated automated real-time structural health monitoring (SHM) and instantaneous verification. An integrated system with mechanoluminescence (ML) and dual artificial intelligence (AI) modules with subsidiary finite element method (FEM) simulation is designed for in situ SHM and instantaneous verification. The ML module detects the exact position of a crack tip and evaluates the significance of existing cracks with a plastic stress-intensity factor (PSIF; K P ). ML fields and their corresponding K p M L values are referenced and verified using the FEM simulation and bidirectional generative adversarial network (GAN). Well-trained forward and backward GANs create fake FEM and ML images that appear authentic to observers; a convolutional neural network is used to postulate precise PSIFs from fake images. Finally, the reliability of the proposed system to satisfy existing commercial requirements is validated in terms of tension, compact tension, AI, and instrumentation.