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Temperature Dependent Deformation

Temperature Dependent Deformation

In a nonhomogeneous rod made up of steel and brass, restrained at both ends and subjected to a temperature change, several steps are involved in calculating the stress and compressive load. Due to the problem's static indeterminacy, one end support is disconnected, allowing the rod to experience the temperature change freely. Next, an unknown force is applied at the free end, triggering deformations in the rod's steel and brass portions. These deformations are then calculated and added together...

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Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on

Pankhi Kashyap¹, Kajal Shivgan¹, Sheetal Patil¹

¹Department of Electrical Engineering (EE), IIT Bombay, Mumbai, 400076, India.

Scientific Reports

|February 15, 2024

Summary

This summary is machine-generated.

This study introduces TinyML for lightweight machine learning models in guided wave structural health monitoring (GW-SHM). This enables accurate, on-device damage detection in composite structures, overcoming cloud limitations.

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Area of Science:

Materials Science
Artificial Intelligence
Structural Engineering

Background:

Deep learning models enhance ultrasonic guided wave structural health monitoring (GW-SHM) but require significant computational resources.
Cloud-dependent deployment limits the scalability of current GW-SHM systems due to processing and connectivity demands.
Environmental factors and damage complexity introduce data heterogeneity, challenging traditional SHM approaches.

Purpose of the Study:

To develop a lightweight machine learning (ML) solution for GW-SHM deployable on edge devices.
To enable efficient and cost-effective structural health monitoring without constant cloud connectivity.
To demonstrate the feasibility of TinyML for real-time damage detection in composite structures.

Main Methods:

Leveraged the TinyML framework to create lightweight ML models for embedded systems.
Developed an unsupervised learning framework for damage detection, specifically targeting disbond and delamination in composite sandwich structures.
Validated the approach using finite element simulations and experimental data across a temperature range of 0-90°C.
Implemented a fully integrated system on a Xilinx Artix-7 FPGA for data acquisition, control, and edge-inference.

Main Results:

The lightweight ML model achieved reasonably high accuracy despite using limited features.
The system successfully detected small-sized defects with improved sensitivity on an edge device.
Demonstrated effective online GW-SHM capabilities through edge deployment.
The TinyML approach proved robust across varying temperatures (0-90°C).

Conclusions:

TinyML offers a viable alternative for deploying GW-SHM systems on resource-constrained edge devices.
The proposed unsupervised learning framework enables effective online damage detection in composite structures.
This research overcomes the scalability limitations of cloud-based ML solutions for SHM.
The integrated FPGA solution facilitates practical, real-time structural health monitoring.