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<p>This study systematically optimizes Laser Powder Bed Fusion (LPBF) process parameters for the fabrication of 17–4 PH stainless steel composites reinforced with Al<sub>2</sub>O<sub>3</sub> foam, integrating Artificial Neural Network (ANN) modeling and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization. A high-precision fiber laser with an 80&#xa0;µm spot size was employed, operating within laser power ranges of 225–300 W, scanning speeds of 800–2000&#xa0;mm/s, and layer heights of 20–40&#xa0;µm. The influence of Al<sub>2</sub>O<sub>3</sub> foam volume fractions (5–15%) and particle sizes (3–10&#xa0;µm) on mechanical and tribological performance was evaluated. At the optimized conditions such as laser power of 289.50 W, scanning speed of 1433.33&#xa0;mm/s, layer height of 30&#xa0;µm, Al<sub>2</sub>O<sub>3</sub> foam infill of 13.15%, and Al<sub>2</sub>O<sub>3</sub> particle size of 6.93&#xa0;µm, the compressive strength reached 704&#xa0;MPa, deviating 2.22% from the NSGA-II predicted value of 720&#xa0;MPa. The wear rate was minimized to 9.3&#xa0;mg/km, with a 3.13% deviation from the predicted 9.6&#xa0;mg/km, while the coefficient of friction (COF) was reduced to 0.57, closely matching the predicted 0.55 within 3.51% error. The ANN model exhibited high predictive accuracy, with Mean Absolute Errors (MAE) of 39.72&#xa0;MPa for compressive strength, 1.82&#xa0;mg/km for wear rate, and 0.05 for COF, demonstrating its capability in capturing nonlinear dependencies between process parameters and material responses. The multi-objective NSGA-II framework successfully generated Pareto-optimal solutions, with all experimental validation tests exhibiting deviations below 5% from predicted values, underscoring the robustness of the integrated ANN-NSGA-II methodology.</p>

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Optimizing LPBF process parameters and Al2O3 reinforcement in 17–4 PH stainless steel composites using ANN and NSGA-II

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • P. Hariharasakthisudhan,
  • Sathish Kannan,
  • K. Logesh

摘要

This study systematically optimizes Laser Powder Bed Fusion (LPBF) process parameters for the fabrication of 17–4 PH stainless steel composites reinforced with Al2O3 foam, integrating Artificial Neural Network (ANN) modeling and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) optimization. A high-precision fiber laser with an 80 µm spot size was employed, operating within laser power ranges of 225–300 W, scanning speeds of 800–2000 mm/s, and layer heights of 20–40 µm. The influence of Al2O3 foam volume fractions (5–15%) and particle sizes (3–10 µm) on mechanical and tribological performance was evaluated. At the optimized conditions such as laser power of 289.50 W, scanning speed of 1433.33 mm/s, layer height of 30 µm, Al2O3 foam infill of 13.15%, and Al2O3 particle size of 6.93 µm, the compressive strength reached 704 MPa, deviating 2.22% from the NSGA-II predicted value of 720 MPa. The wear rate was minimized to 9.3 mg/km, with a 3.13% deviation from the predicted 9.6 mg/km, while the coefficient of friction (COF) was reduced to 0.57, closely matching the predicted 0.55 within 3.51% error. The ANN model exhibited high predictive accuracy, with Mean Absolute Errors (MAE) of 39.72 MPa for compressive strength, 1.82 mg/km for wear rate, and 0.05 for COF, demonstrating its capability in capturing nonlinear dependencies between process parameters and material responses. The multi-objective NSGA-II framework successfully generated Pareto-optimal solutions, with all experimental validation tests exhibiting deviations below 5% from predicted values, underscoring the robustness of the integrated ANN-NSGA-II methodology.