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Updated: Jun 13, 2026

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Dynamic Ensemble Learning with Transfer Learning for Fatigue Performance Prediction in Ni-Based Superalloys.

Jiaxing Yang¹, Fenglou Du¹, Haopeng Lv¹

¹Hebei Short Process Steelmaking Technology Innovation Center, School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

Materials (Basel, Switzerland)

|June 12, 2026

Summary

This summary is machine-generated.

This study introduces a novel machine learning framework for predicting Ni-based superalloy fatigue performance, overcoming data scarcity. The dynamic ensemble and transfer learning approach significantly improves prediction accuracy for fatigue stress and life.

Keywords:

Ni-based superalloys fatigue performance prediction feature alignment transfer learning

Related Experiment Videos

Last Updated: Jun 13, 2026

Surrogate Model Development for Digital Experiments in Welding

Surrogate Model Development for Digital Experiments in Welding

Published on: March 28, 2025

Area of Science:

Materials Science
Mechanical Engineering
Data Science

Background:

Predicting fatigue performance in Ni-based superalloys is challenging due to limited data and poor generalization of standard machine learning models.
Existing methods struggle with the scarcity of high-quality fatigue data, hindering reliable material design and performance assessment.

Purpose of the Study:

To develop an advanced machine learning framework for accurate fatigue performance prediction in Ni-based superalloys, addressing data limitations.
To enhance the generalization capability of predictive models by integrating dynamic ensemble and transfer learning techniques.

Main Methods:

A dynamic weighted error feedback ensemble algorithm (DWELA) was developed to optimize base regressor weights in real-time, improving tensile property prediction (R² from 0.90 to 0.95).
A feature alignment transfer learning (FATL) strategy was employed to transfer knowledge from a tensile dataset to a fatigue dataset, aligning shared features and fine-tuning domain-specific ones.
The combined framework, ETFPM, was trained on 1025 tensile and 622 fatigue samples.

Main Results:

The DWELA improved tensile property prediction R² to 0.95, outperforming the best single model.
The ETFPM model achieved R² of 0.93 for fatigue stress and 0.81 for fatigue life on independent samples, surpassing the best fatigue-trained single model (SVR R² of 0.89 and 0.72).
Twenty candidate alloys were successfully screened using the predictive model.

Conclusions:

The proposed framework offers a practical and effective solution for fatigue performance prediction in data-limited scenarios for Ni-based superalloys.
The novel DWELA and FATL strategies demonstrate significant improvements in prediction accuracy and model generalization.
Further experimental validation is recommended to confirm the broader applicability of the developed methodology.