Mr. Michael Fischer Profile

Michael Fischer

at Univ of Michigan

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 9 May 2024 Presentation + Paper

LSTM-based fatigue monitoring of fiberglass composites using laser-induced graphene

Boyang Chen, Adam Childress, Jalal Nasser, Michael Fisher, Henry Sodano

Proceedings Volume 12950, 1295008 (2024) https://doi.org/10.1117/12.3010899

KEYWORDS: Material fatigue, Composites, Artificial neural networks, Education and training, Rain, Resistance, Structural health monitoring, Piezoresistivity, Neurons, Graphene

Read Abstract +

This study aims to advance the field of composite material fatigue prognosis by employing Long Short-Term Memory (LSTM) neural networks for in-situ damage progression monitoring under random dynamic loading conditions. A unique approach is adopted, wherein Laser-Induced Graphene (LIG) interlayers are embedded into fiberglass composites. These LIG interlayers are innovative sensors owing to their piezoresistive properties, enabling real-time measurement of fatigue damage monitoring. The crux of this research lies in applying LSTM neural networks, specifically designed to handle time-series data, making them ideal for modeling the stochastic and unpredictable nature of fatigue loading in composite materials. Contrasting the performance of LSTM with traditional Multilayer Perceptrons (MLP), it is observed that LSTM yields superior prediction accuracy in estimating the remaining useful life (RUL) of LIG interlayered fiberglass composites. By utilizing predefined electrical resistance damage parameters, the LSTM algorithm correlates the rate of fatigue damage buildup to the impending decline in mechanical performance. This research establishes that integrating piezoresistive LIG interlayers with LSTM neural networks culminates in a robust, reliable, and closed-loop system for structural fatigue monitoring and lifecycle prediction in composite materials subjected to random dynamic loading.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years