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]} }激光线材定向能量沉积热声数据集（Laser-Wire-DED-ThermalAudio-Dataset） ## 数据集描述 本数据集为激光线材定向能量沉积（Laser Wire-Directed Energy Deposition，LW-DED）工艺的实时监测与异常检测提供多模态传感器数据，旨在支撑基于声学与热成像的缺陷检测研究，尤其适用于铝合金（Al7075）沉积过程的监测。 本数据集对应GitHub上的MultiSensor-Monitoring-LW-DED开源仓库：🔗 GitHub 开源仓库 数据集包含LW-DED实验过程中采集的同步热成像与声学录音数据，为探究缺陷形成机理提供了全面的研究资源；其可通过熔池声学信号与热特征，支持基于深度学习的异常检测任务。 实验录制视频可通过以下链接获取：🎥 YouTube 视频链接 ## 核心特性 1. **同步多传感器数据**：采集LW-DED工艺中的热成像与声学信号 2. **熔池声学处理**：包含用于缺陷分析的预处理与原始声学信号 3. **异常检测标注**：包含诸如熔滴滴落异常等带标注的缺陷样本 4. **高保真热成像**：通过Xiris热成像相机采集，并与声学数据实现同步 ## 数据集结构 数据集的目录结构如下： Laser-Wire-DED-ThermalAudio-Dataset/ ├── Annotation/ # 存储带标注的缺陷标注信息 ├── Visualization_demo/ # 结果可视化脚本与演示文件 ├── Raw_Video/ # 未处理的原始视频数据 ├── segmented_videos/ # 经处理的视频片段 ├── segmented_audio/ # 经处理的音频片段 └── Dataset/ # 包含热成像与声学信号的核心数据集 ## 应用场景 本数据集可应用于以下研究方向： 1. 基于机器学习的声热融合缺陷分类 2. LW-DED工艺监测与工艺优化 3. 声热数据融合的深度学习模型训练 4. 金属增材制造中的异常检测 ## 引用方式 若您在研究中使用本数据集，请引用如下文献： bibtex @dataset{chen2024lw_ded, author = {Chen, Lequn}, title = {Laser-Wire-DED-ThermalAudio-Dataset}, year = {2024}, publisher = {Zenodo}, url = {https://zenodo.org/record/[Dataset-ID]} }