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Updated: Feb 28, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
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Experiments and Numerical Optimization of Water-Jet Guided Laser Diamond Machining Based on the Improved NSGA-III

Mengjian Wang1,2, Jianwei Wang2, Weizhe Wang2

  • 1School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450001, China.

Micromachines
|February 27, 2026
PubMed
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This study optimizes water-jet guided laser (WJGL) machining of diamond using advanced modeling. The new method significantly improves cutting depth, speed, and surface quality, enabling efficient, high-quality diamond processing.

Area of Science:

  • Manufacturing Engineering
  • Materials Science
  • Laser Machining Technology

Background:

  • Water-jet guided laser (WJGL) machining is a precise method for materials like diamond.
  • Optimizing WJGL parameters is crucial for achieving high-quality and efficient machining outcomes.
  • Existing optimization methods may not fully address the complexities and uncertainties in WJGL processes.

Purpose of the Study:

  • To investigate single-factor effects and optimize process parameters for WJGL diamond machining.
  • To develop a robust optimization framework that accounts for parameter interactions and uncertainties.
  • To enhance machining performance indicators such as cutting depth, kerf width, roughness, and speed.

Main Methods:

  • Experimental analysis of single-factor effects of laser energy and control parameters.
Keywords:
GPRNSGA III algorithmdiamonduncertaintywater-jet guided laser

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  • Optimal Latin Hypercube Sampling (OLHD) for experimental design and data collection.
  • Gaussian Process Regression (GPR) surrogate modeling for nonlinear mapping.
  • An improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) with Expected Improvement (EI) based Expected Improvement dominance partition strategy (EIS) for optimization under uncertainty.
  • Main Results:

    • The proposed optimization method demonstrated significantly lower prediction deviations compared to traditional NSGA-III.
    • Optimized parameters led to a 48.21% increase in cutting depth (Nd).
    • Achieved a 1.44% reduction in kerf width (Kw), a 43.09% decrease in line roughness average (Ra), and a 78.40% improvement in cutting speed (Cs).

    Conclusions:

    • The developed optimization approach is effective for WJGL diamond machining.
    • The method successfully addresses parameter interactions and uncertainties, leading to significant performance enhancements.
    • This research offers a viable strategy for achieving high-quality, efficient, and robust diamond machining using WJGL technology.