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Related Experiment Video

Updated: Feb 21, 2026

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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Published on: February 27, 2013

Fiber-based in-situ surface characterization for laser material processing.

Yujie Yang, Thomas Weyrauch

|February 20, 2026

Summary

This summary is machine-generated.

This study introduces a fiber-based laser line-scan confocal optical system for in-situ surface characterization. This system enables near real-time process control in laser material processing by measuring surface roughness and distinguishing features.

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Last Updated: Feb 21, 2026

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Area of Science:

Optics and Photonics
Materials Science
Laser Technology

Background:

In-situ surface characterization is crucial for real-time process control in laser material processing.
Traditional methods may lack the speed or resolution required for dynamic adjustments.
Protecting optical components from harsh processing environments is a significant challenge.

Purpose of the Study:

To explore the use of a fiber-based laser line-scan confocal optical system for in-situ surface characterization.
To enable near real-time process control in laser material processing.
To investigate the feasibility of using deep neural networks for surface analysis.

Main Methods:

Development of a fiber-based laser line-scan confocal optical system operating at a low numerical aperture.
Implementation of data processing using a deep neural network for surface analysis.
Experimental demonstration of a method for measuring height differences in samples.

Main Results:

The system allows for in-situ surface characterization during laser material processing.
Feasibility of determining surface roughness and distinguishing surface features was demonstrated.
A proof-of-concept method for measuring height differences was successfully developed.

Conclusions:

The developed fiber-based laser line-scan confocal optical system shows promise for real-time process control.
Deep neural network analysis aids in characterizing surface roughness and features.
The system provides a viable approach for in-situ monitoring in laser material processing.