Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

Bornemann, Sarah; Lang, Walter

doi:10.3390/s22124511

Cited by 3 publications

(1 citation statement)

References 18 publications

(20 reference statements)

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“…Here, we show that the outer metal layer of FMLs, usually seen as an impediment for wireless transmission, can be turned into an advantage by structuring it in a way that it can be used as an antenna. We build upon our previous work [3,4] and present measurement results of the first fully integrated system consisting of sensors and wireless sensor nodes inside an FML.…”

Section: Introductionmentioning

confidence: 99%

Toward Material-Integrated Wireless Electronics for SHM in Fiber Metal Laminates

Bornemann,

Rittmeier,

Roloff

et al. 2024

Eurosensors 2023

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Section: Introductionmentioning

confidence: 99%

Toward Material-Integrated Wireless Electronics for SHM in Fiber Metal Laminates

Bornemann,

Rittmeier,

Roloff

et al. 2024

Eurosensors 2023

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Optimising structural health monitoring systems: A 2D numerical investigation on impedance matching for FML with integrated sensors

Rottmann,

Mangalath,

Weber

2024

Proc Appl Math and Mech

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Fibre metal laminates (FML) represent an innovative class of advanced composite materials that integrate the mechanical properties of both metals and fibre‐reinforced composites (FRP). Combining the strength and ductility of metals with the lightweight and high stiffness of FRP and FMLs have emerged as new material compositions for applications in chemical, nuclear, automobile, and aerospace engineering disciplines. Structural health monitoring (SHM) using guided ultrasonic waves (GUW) is the state‐of‐the‐art for non‐destructive testing of thin‐walled structures. When applied to FML, SHM plays a crucial role in monitoring the integrity over time and detecting potential damage such as delamination, fibre breakage, or other structural anomalies. In SHM with GUW, a wave‐field is emitted by actuators. This wave‐field can be affected by damage in the structure, thereby changing its propagation characteristics. Sensors monitor the interaction between damage and GUW, which can be utilized to locate and classify the damage and ascertain the overall health state of the structure. In this study, an advanced integration of measurement hardware, that is, sensors and actuators, within the laminate structure is investigated. Sensor integration into FML allows for improved and more sophisticated monitoring capabilities in comparison to measuring techniques like laser vibrometers, which are limited to measuring displacements on the surface of the structure. However, the integration of sensors and actuators yields the technical difficulty of distorting the wave‐fields and may result in an over‐ or underestimation of the damage. Similar to damage, the distortion of the wave‐field is caused by the changes in acoustic impedance resulting from different material properties. In a previous study, incorporating a functionally graded artificial interphase through acoustic impedance matching between the sensor and host material showed notable and significant outcomes. The current contribution extends the prior graded artificial interphase for an isotropic homogeneous material to an FML structure. This paper presents a comprehensive numerical simulation study on a two‐dimensional model of FML with integrated sensors. The interphases are designed based on impedance matching, which improves signal transmission and reduces disturbing reflections. The conducted investigations hold for several interphase configurations for a wide frequency range. The optimised integration of sensors demonstrates promising results for enhancing the reliability and accuracy of SHM systems. This research serves as a foundation for further experimental validation and the development of advanced sensor‐integrated FML structures with improved monitoring capabilities.

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A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization

Bosse

2024

Algorithms

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Data-driven models used for predictive classification and regression tasks are commonly computed using floating-point arithmetic and powerful computers. We address constraints in distributed sensor networks like the IoT, edge, and material-integrated computing, providing only low-resource embedded computers with sensor data that are acquired and processed locally. Sensor networks are characterized by strong heterogeneous systems. This work introduces and evaluates a virtual machine architecture that provides ML as a service layer (MLaaS) on the node level and addresses very low-resource distributed embedded computers (with less than 20 kB of RAM). The VM provides a unified ML instruction set architecture that can be programmed to implement decision trees, ANN, and CNN model architectures using scaled integer arithmetic only. Models are trained primarily offline using floating-point arithmetic, finally converted by an iterative scaling and transformation process, demonstrated in this work by two tests based on simulated and synthetic data. This paper is an extended version of the FedCSIS 2023 conference paper providing new algorithms and ML applications, including ANN/CNN-based regression and classification tasks studying the effects of discretization on classification and regression accuracy.

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Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates

Cited by 3 publications

References 18 publications

Toward Material-Integrated Wireless Electronics for SHM in Fiber Metal Laminates

Toward Material-Integrated Wireless Electronics for SHM in Fiber Metal Laminates

Optimising structural health monitoring systems: A 2D numerical investigation on impedance matching for FML with integrated sensors

A Virtual Machine Platform Providing Machine Learning as a Programmable and Distributed Service for IoT and Edge On-Device Computing: Architecture, Transformation, and Evaluation of Integer Discretization

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