Enhancing FBG Sensing in the Industrial Application by Optimizing the Grating Parameters Based on NSGA-II

Area of Science:

• Photonics and Optical Sensing
• Materials Science and Engineering
• Computational Optimization

Background:

• Fiber Bragg Grating (FBG) sensors offer advantages like small size and immunity to electromagnetic interference, enhancing measurement accuracy.
• Key performance parameters for FBG sensors, reflectivity and bandwidth, vary significantly across industrial applications.
• Optimizing these multi-objective parameters simultaneously presents a considerable challenge.

Purpose of the Study:

• To develop and present an optimization method for FBG sensor parameters.
• To align FBG sensor manufacturing specifications with diverse industrial application requirements.
• To address the challenge of optimizing conflicting objectives like reflectivity and bandwidth.

Main Methods:

• Implementation of the nondominated sorting genetic algorithm II (NSGA-II) for multi-objective optimization.
• Translating industrial needs for bandwidth and reflectivity into manufacturing parameters (grating length, modulation refractive index).
• Utilizing MATLAB for the optimization process and validation against existing literature.

Main Results:

• The proposed NSGA-II based method effectively determines optimal grating parameters.
• The optimization successfully bridges the gap between application requirements and FBG sensor manufacturing.
• Validation confirms the method's capability in achieving desired FBG sensor performance.

Conclusions:

• The presented optimization approach provides a robust solution for tailoring FBG sensors to specific industrial demands.
• This method enhances the practical applicability of FBG technology in various measurement scenarios.
• Accurate FBG sensor parameter optimization leads to improved performance and reliability in industrial applications.