Laser-Wire-DED-ThermalAudio-Dataset

Laser-Wire-DED-ThermalAudio-Dataset

Description

The Laser-Wire-DED-ThermalAudio-Dataset provides multi-modal sensor data for real-time monitoring and anomaly detection in Laser Wire-Directed Energy Deposition (LW-DED) processes. This dataset is designed to support research in acoustic-based and thermal-based defect detection, particularly for monitoring aluminum alloy (Al7075) deposition.

This dataset corresponds to the MultiSensor-Monitoring-LW-DED repository on GitHub:🔗 GitHub Repository

The dataset consists of synchronized thermal imaging and acoustic recordings collected during LW-DED experiments, providing a comprehensive resource for investigating defect formation mechanisms. The dataset enables deep learning-based anomaly detection by utilizing melt pool audio signals and thermal features.

A recorded experimental video is available at:🎥 YouTube Video

Key Features

Synchronized Multi-Sensor Data: Captures thermal and acoustic signals from LW-DED processes.

Melt Pool Audio Processing: Includes preprocessed and raw acoustic signals for defect analysis.

Anomaly Detection Labels: Contains labeled defects such as dripping anomalies.

High-Fidelity Thermal Imaging: Captured via Xiris thermal camera and synchronized with acoustic data.

Dataset Structure

The dataset is structured as follows:

Laser-Wire-DED-ThermalAudio-Dataset/
│── Annotation/ # Contains labeled defect annotations
│── Visualization_demo/ # Scripts and visualizations of results
│── Raw_Video/ # Unprocessed video data
│── segmented_videos/ # Processed video segments
│── segmented_audio/ # Processed audio segments
│── Dataset/ # Core dataset containing thermal and acoustic signals

Usage

This dataset can be used for:

Machine learning-based defect classification (audio-thermal fusion)

LW-DED process monitoring and optimization

Deep learning model training for acoustic and thermal data fusion

Anomaly detection in metal additive manufacturing

Citation

If you use this dataset in your research, please cite:

@dataset{chen2024lw_ded,
author = {Chen, Lequn},
title = {Laser-Wire-DED-ThermalAudio-Dataset},
year = {2024},
publisher = {Zenodo},
url = {https://zenodo.org/record/[Dataset-ID]}
}