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Machine Learning-Assisted Burst Femtosecond Laser Polishing of Invar Alloy: Process Optimization and Performance

Jiawei Lin1, Donghan Li1, Jinlin Luo1

  • 1State Key Laboratory of Precision Manufacturing for Extreme Service Performance, College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.

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|March 27, 2026
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Summary

This study optimized burst-mode femtosecond laser polishing for Invar alloy, achieving a 52% reduction in surface roughness. A predictive model was developed for efficient ultra-precision polishing in high-end manufacturing.

Keywords:
femtosecond laserinvar alloylaser polishing

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Area of Science:

  • Materials Science
  • Laser Physics
  • Manufacturing Engineering

Background:

  • Invar alloy's surface quality is critical for aerospace and precision instruments.
  • Existing research lacks understanding of multi-parameter synergy in Invar alloy laser polishing.

Purpose of the Study:

  • To investigate the mechanism and multi-parameter synergy of burst-mode femtosecond laser polishing on Invar alloy.
  • To develop an efficient ultra-precision polishing method for Invar alloy.

Main Methods:

  • Utilized burst-mode femtosecond laser polishing with specific parameters (1 MHz repetition rate, 4 sub-pulses/burst).
  • Investigated the effects of energy density, scanning speed, and scanning pitch.
  • Employed amplitude spectrum analysis and developed a four-layer Fully Connected Neural Network (FCNN) model.

Main Results:

  • Energy density is the dominant factor, with optimal polishing occurring at 0.47-0.64 J/cm².
  • Achieved a 52% reduction in surface roughness, from 821 nm to 394 ± 50 nm.
  • FCNN model predicted polishing effects with R² = 0.92, enabling rapid parameter optimization.

Conclusions:

  • Clarified the interaction mechanism between burst-mode laser and Invar alloy.
  • Proposed an efficient ultra-precision polishing method for Invar alloy.
  • Laid a theoretical foundation for Invar alloy applications in high-end manufacturing.