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Optimizing material and process parameters in laser engineered net shaping using liquid neural networks.

Haijiang Tian^1,2, Ismail Saad³, Tianshu Chen²

¹Faculty of Engineering, Universiti Malaysia Sabah, Jalan UMS, Kota Kinabalu, 88400, Sabah, Malaysia.

Scientific Reports

|May 13, 2026

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This summary is machine-generated.

This study presents a Liquid Neural Network (LNN) for optimizing Laser Engineered Net Shaping (LENS) parameters. The physics-informed AI achieves high accuracy, improving material properties and enabling defect-minimized additive manufacturing.

Area of Science:

Additive Manufacturing
Artificial Intelligence
Materials Science

Background:

Laser Engineered Net Shaping (LENS) is a key additive manufacturing process.
Optimization of LENS parameters is crucial for material properties and defect reduction.
Existing methods often lack physical interpretability and adaptive learning capabilities.

Purpose of the Study:

To introduce a physics-based Liquid Neural Network (LNN) framework for LENS parameter optimization.
To enhance physical interpretability and generalization through integrated physical constraints.
To demonstrate adaptive learning under thermal stress for improved process control.

Main Methods:

Developed a Liquid Neural Network (LNN) combining neural differential equations and dynamic liquid weights.

Keywords:

CUI—Cladding Uniformity Index LENS—Laser Engineered Net Shaping LNN—Liquid Neural Network PR—Polynomial Regression RF—Random Forest RL—Reinforcement Learning RSM—Response Surface Methodology

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Incorporated physical constraints from Fourier's law and thermo-mechanical relationships into the training process.

Trained the model on 90 samples across three alloys (Ni60A, Stellite6, In625) for LENS optimization.

Main Results:

Achieved high prediction accuracy (R² > 0.9) for key quality measures on the test set.
Demonstrated effective multi-objective optimization, improving microhardness (+12.5%) and reducing dilution (18%).
Showcased improved cladding consistency (CUI = 0.97) and potential for defect-minimized, energy-efficient manufacturing.

Conclusions:

The physics-based LNN framework enables accurate prediction and optimization of LENS parameters.
This approach facilitates defect-minimized, energy-efficient additive manufacturing.
The study lays the groundwork for closed-loop control and digital twin integration in advanced laser cladding.