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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
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Embedded FBG Sensor Based Impact Identification of CFRP Using Ensemble Learning.

Jun Li1, Yinghong Yu1, Xinlin Qing1

  • 1School of Aerospace Engineering, Xiamen University, Xiamen 361102, China.

Sensors (Basel, Switzerland)
|March 6, 2021
PubMed
Summary
This summary is machine-generated.

This study embeds fiber Bragg grating (FBG) sensors in carbon fiber reinforced plastics (CFRPs) for impact monitoring. Ensemble learning models combining support vector regression and neural networks show superior impact identification capabilities.

Keywords:
BP neural networkFBG sensorsSVRcomposite structuresensemble learningimpact identification

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Area of Science:

  • Materials Science
  • Mechanical Engineering
  • Aerospace Engineering

Background:

  • Composite structures, widely used in aircraft, face significant threats from impacts.
  • Accurate and reliable impact monitoring is crucial for ensuring the structural integrity and safety of aircraft.

Purpose of the Study:

  • To develop an advanced impact identification model for composite structures.
  • To embed fiber Bragg grating (FBG) sensors in carbon fiber reinforced plastics (CFRPs) for real-time strain monitoring.
  • To evaluate the effectiveness of ensemble learning models for impact detection.

Main Methods:

  • Fiber Bragg grating (FBG) sensors were embedded in unidirectional CFRPs during manufacturing.
  • An ensemble learning model combining support vector regression (SVR) and a back propagation (BP) neural network was developed.
  • The model was trained and tested using hundreds of impact events, with strain responses recorded by FBG sensors.

Main Results:

  • The developed ensemble learning model demonstrated enhanced capability in impact identification compared to single neural network models.
  • The study explored the influence of the time of arrival (ToA) on the neural network's performance.
  • Strain monitoring using embedded FBG sensors provided data related to elastic modulus and resin state.

Conclusions:

  • Ensemble learning models offer a more robust approach to impact identification in CFRP structures.
  • FBG sensor integration provides a viable method for monitoring the health of composite materials.
  • Further research can optimize the ensemble model and investigate other sensing technologies for impact detection.