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Updated: Jun 15, 2026

Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Interpretable Machine Learning for Evaluating Nanogenerators' Structural Design.

Chi Han¹, Mingyu Jin², Fuying Dong¹

¹Department of Biomedical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States.

|April 7, 2025

Summary

This summary is machine-generated.

Limited battery life in electronics is a major issue. This study introduces a machine learning platform for faster, more accurate triboelectric nanogenerator (TENG) design, enabling sustainable self-powered devices.

Keywords:

interpretable machine learning self-powered systems structural optimization surrogate models triboelectric nanogenerator

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Published on: November 12, 2014

Area of Science:

Materials Science
Electrical Engineering
Computer Science

Background:

Modern electronics face limitations due to short battery life.
Triboelectric nanogenerators (TENGs) offer a self-powered solution for electronics.
Current TENG design evaluation methods are inefficient and costly.

Purpose of the Study:

To develop an automated platform for evaluating TENG structural design.
To overcome the limitations of traditional TENG design assessment.
To accelerate the development of efficient TENG devices.

Main Methods:

Utilized machine learning (ML) techniques for automated evaluation.
Developed an artificial neural network (ANN) surrogate model for performance prediction.
Employed a TreeSHAP interpretable ML model for parameter insights.

Main Results:

The platform provides accurate and reliable performance predictions for TENG structures.
Achieved precise global and local insights into TENG structural parameters.
Demonstrated broad adaptability across multiple TENG designs.

Conclusions:

The integrated platform effectively solves complex TENG structural evaluation problems.
Represents a significant advancement in TENG design methodologies.
Supports the development of sustainable energy solutions for mobile electronics.