#6754. Improved roughness measurement method using fiber Bragg gratings and machine learning

October 2026publication date
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Journal’s subject area:
Engineering
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Abstract:
An improved roughness measurement system is proposed that uses fiber Bragg gratings (FBGs) and machine learning. One sensor is a stylus profiler fabricated from FBG and silicone, and the other is a strain sensor fabricated from FBG. Eight statistical strain features of FBGs are extracted for intelligent sensing. Experimental results clearly showed that most strain features changed monotonically with the roughness and could be used individually to measure roughness. Roughness prediction and classification were realized with a polynomial regression algorithm using principal component analysis and a decision tree, respectively. The polynomial regression algorithm had a better performance compared with support vector regression. Corresponding optimized mean square error and coefficient of determination for the roughness prediction were 0.0035 µm and 0.9950, respectively. The macro-averaged F1 score for roughness classification after optimization was 0.98932.
Keywords:
Bragg gratings; Fiber optics; Machine learning; Surface roughness

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