cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.8kf23y
title: Occurrence Download

description: A dataset containing 672 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Nepal, Bhutan, Bangladesh, Pakistan, Afghanistan)",
 "TaxonKey is Taractrocera ceramas (Hewitson, 1868)"
 ]
}

 The dataset includes 672 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026888-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qmnfhj
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudobarbus calidus (Barnard, 1938)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((30.5701 -24.0715,25.5395 -24.0715,25.5395 -28.5786,30.5701 -28.5786,30.5701 -24.0715))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026889-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.15050300
title: LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition

description: LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition

The LDED-FusionNet Dataset is a comprehensive multisensor dataset designed for audio-visual fusion in Laser-Directed Energy Deposition (LDED) processes. This dataset supports research in defect detection, quality monitoring, and multimodal machine learning models for metal additive manufacturing.

This dataset corresponds to the LDED-FusionNet GitHub repository:🔗 GitHub Repository

It has been used in multiple peer-reviewed publications focusing on feature-level fusion of acoustic and visual data, including:



Multisensor Fusion-Based Digital Twin for Localized Quality Prediction in Robotic LDED (RCIM 2023).

In-situ Defect Detection in LDED with Machine Learning and Multi-Sensor Fusion (JMST 2024).

Inference of Melt Pool Visual Characteristics in LAM Using Acoustic Signal Features and Robotic Motion Data (ICCAR 2024).


Key Features



Synchronized Audio-Visual Data: High-fidelity coaxial melt pool images and multi-domain acoustic signals recorded during LDED processing.

Cross-Modality Feature Fusion: Enables research on acoustic-vision feature relationships for process quality monitoring.

Defect Detection Labels: Ground truth annotations for melt pool stability, defect types, and process variations.

Robotic Motion Data: Captures spatial dependency of process dynamics, providing tool-center-point (TCP) motion logs.

Benchmark for Machine Learning: Supports feature extraction, deep learning models, and anomaly detection.


Usage

This dataset is useful for:



Developing AI models for real-time defect detection in LDED

Exploring acoustic-visual correlations for quality prediction

Improving multisensor fusion techniques in metal additive manufacturing

Benchmarking ML algorithms for process monitoring and control


Citation

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

@dataset{chen2024lded_fusionnet,
  author    = {Chen, Lequn},
  title     = {LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition},
  year      = {2024},
  publisher = {Zenodo},
  url       = {https://zenodo.org/record/[Dataset-ID]}
}
DOI: 10.5281/zenodo.15050299
title: LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition

description: LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition

The LDED-FusionNet Dataset is a comprehensive multisensor dataset designed for audio-visual fusion in Laser-Directed Energy Deposition (LDED) processes. This dataset supports research in defect detection, quality monitoring, and multimodal machine learning models for metal additive manufacturing.

This dataset corresponds to the LDED-FusionNet GitHub repository:🔗 GitHub Repository

It has been used in multiple peer-reviewed publications focusing on feature-level fusion of acoustic and visual data, including:



Multisensor Fusion-Based Digital Twin for Localized Quality Prediction in Robotic LDED (RCIM 2023).

In-situ Defect Detection in LDED with Machine Learning and Multi-Sensor Fusion (JMST 2024).

Inference of Melt Pool Visual Characteristics in LAM Using Acoustic Signal Features and Robotic Motion Data (ICCAR 2024).


Key Features



Synchronized Audio-Visual Data: High-fidelity coaxial melt pool images and multi-domain acoustic signals recorded during LDED processing.

Cross-Modality Feature Fusion: Enables research on acoustic-vision feature relationships for process quality monitoring.

Defect Detection Labels: Ground truth annotations for melt pool stability, defect types, and process variations.

Robotic Motion Data: Captures spatial dependency of process dynamics, providing tool-center-point (TCP) motion logs.

Benchmark for Machine Learning: Supports feature extraction, deep learning models, and anomaly detection.


Usage

This dataset is useful for:



Developing AI models for real-time defect detection in LDED

Exploring acoustic-visual correlations for quality prediction

Improving multisensor fusion techniques in metal additive manufacturing

Benchmarking ML algorithms for process monitoring and control


Citation

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

@dataset{chen2024lded_fusionnet,
  author    = {Chen, Lequn},
  title     = {LDED-FusionNet Dataset: Multisensor Audio-Visual Data for Laser-Directed Energy Deposition},
  year      = {2024},
  publisher = {Zenodo},
  url       = {https://zenodo.org/record/[Dataset-ID]}
}
DOI: 10.5281/zenodo.15049617
title: AM Surface Defect Point Cloud Dataset

description:  



AM Surface Defect Point Cloud Dataset

Description

The AM Surface Defect Point Cloud Dataset provides on-machine laser-scanned point cloud data for monitoring and correcting surface deviations in additive manufacturing (AM) parts. This dataset is a valuable resource for researchers and engineers working on in-process quality control, surface defect identification, and adaptive repair strategies in metal AM.

This dataset is associated with the AM Surface Defect repository on GitHub:🔗 GitHub Repository

It also relates to the following publication:📄 Surface Defect Identification and Adaptive Correction in AM

The dataset contains several representative point cloud samples captured from real-world manufactured AM parts. These data samples facilitate the development of advanced algorithms for surface defect detection and adaptive correction.



Key Features



On-Machine Laser Scanning: Point cloud data collected directly from real AM components.

Surface Deviation Detection: Captures dimensional deviations and surface defects.

Adaptive Repair Strategy: Supports automatic toolpath generation for defect correction.

High-Fidelity 3D Point Cloud Data: Enables precise defect analysis and in-situ monitoring.

Compatible with Point Cloud Processing Tools: Usable in Open3D, PCL, and machine learning frameworks.




Usage

This dataset can be used for:



Point cloud-based defect detection and classification

Dimensional deviation analysis for AM quality control

AI-driven surface defect identification and adaptive correction

Toolpath generation for AM repair processes

Training deep learning models for point cloud processing




Citation

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

@dataset{chen2024am_pointcloud,
  author    = {Chen, Lequn},
  title     = {AM Surface Defect Point Cloud Dataset},
  year      = {2024},
  publisher = {Zenodo},
  url       = {https://zenodo.org/record/[Dataset-ID]}
}
DOI: 10.5281/zenodo.15049618
title: AM Surface Defect Point Cloud Dataset

description:  



AM Surface Defect Point Cloud Dataset

Description

The AM Surface Defect Point Cloud Dataset provides on-machine laser-scanned point cloud data for monitoring and correcting surface deviations in additive manufacturing (AM) parts. This dataset is a valuable resource for researchers and engineers working on in-process quality control, surface defect identification, and adaptive repair strategies in metal AM.

This dataset is associated with the AM Surface Defect repository on GitHub:🔗 GitHub Repository

It also relates to the following publication:📄 Surface Defect Identification and Adaptive Correction in AM

The dataset contains several representative point cloud samples captured from real-world manufactured AM parts. These data samples facilitate the development of advanced algorithms for surface defect detection and adaptive correction.



Key Features



On-Machine Laser Scanning: Point cloud data collected directly from real AM components.

Surface Deviation Detection: Captures dimensional deviations and surface defects.

Adaptive Repair Strategy: Supports automatic toolpath generation for defect correction.

High-Fidelity 3D Point Cloud Data: Enables precise defect analysis and in-situ monitoring.

Compatible with Point Cloud Processing Tools: Usable in Open3D, PCL, and machine learning frameworks.




Usage

This dataset can be used for:



Point cloud-based defect detection and classification

Dimensional deviation analysis for AM quality control

AI-driven surface defect identification and adaptive correction

Toolpath generation for AM repair processes

Training deep learning models for point cloud processing




Citation

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

@dataset{chen2024am_pointcloud,
  author    = {Chen, Lequn},
  title     = {AM Surface Defect Point Cloud Dataset},
  year      = {2024},
  publisher = {Zenodo},
  url       = {https://zenodo.org/record/[Dataset-ID]}
}
DOI: 10.5281/zenodo.15043817
title: Laser-Wire-DED-ThermalAudio-Dataset

description:  

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]}
}
DOI: 10.5281/zenodo.15043818
title: Laser-Wire-DED-ThermalAudio-Dataset

description:  

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]}
}
DOI: 10.15468/dl.rf4jvq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudobarbus calidus (Barnard, 1938)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((27.1332 -20.158,25.5395 -23.2724,25.5395 -24.0715,30.5701 -24.0715,30.5701 -20.3186,29.6496 -19.6123,28.947 -19.4003,28.1821 -19.4609,27.6072 -19.7214,27.1332 -20.158))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026890-250525065834625/datasets/export for details.
DOI: 10.5281/zenodo.14039054
title: HybridCAD: A Comprehensive Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models

description: The HybridCAD dataset is a novel resource tailored for hybrid additive-subtractive feature recognition in Computer-Aided Design (CAD) models, uniquely combining features from both manufacturing processes. Building on the MFCAD and MFCAD++ datasets, HybridCAD introduces additive manufacturing features alongside traditional subtractive ones, enabling the exploration and development of machine learning models for more complex, hybrid manufacturing applications. This dataset is especially suited for automatic feature recognition (AFR) research and model training in hybrid manufacturing contexts.

Dataset Composition

The dataset consists of 8,938 Boundary Representation (B-Rep) CAD models distributed into three main directories:





STEP Files:



Contains CAD models in STEP format, each representing a distinct part with hybrid manufacturing features.

Generated using PythonOCC CAD software, these CAD files serve as the foundation for feature recognition tasks.





Feature Labels:



File: feature_labels.txt

Provides unique label IDs for each B-Rep face in every CAD model, denoting the manufacturing feature name it belongs to.

Each CAD model face is labelled according to one of 29 hybrid additive-subtractive features, allowing for accurate and detailed feature recognition.




 





Hierarchical B-Rep Graphs:



Stored in HDF5 format for structured access to B-Rep data.

Detailed structural information is available in h5_structure.txt, explaining the hierarchical arrangement of B-Rep graphs.




Dataset Splits

The dataset is split into training, validation, and testing sets as follows:



Training Set: 6,256 samples (70%)

Validation Set: 1,342 samples (15%)

Testing Set: 1,340 samples (15%)


Hybrid Manufacturing Features

HybridCAD includes a diverse range of additive and subtractive manufacturing features, expanding beyond the subtractive-only features of MFCAD++. This inclusion enables the exploration of hybrid manufacturing processes and the recognition of a broader feature set in CAD models. The complete feature list includes:

Label    Feature0    Chamfer1    Through hole2    Triangular passage3    Rectangular passage4    6-sided passage5    Triangular through slot6    Rectangular through slot7    Circular through slot8    Rectangular through step9    2-sided through step10    Slanted through step11    O-ring12    Blind hole13    Triangular pocket14    Rectangular pocket15    6-sided pocket16    Circular end pocket17    Rectangular blind slot18    Vertical circular end blind slot19    Horizontal circular end blind slot20    Triangular blind step21    Circular blind step22    Rectangular blind step23    Round24    Extrude cylinder25    Extrude rectangle26    Extrude triangle27     Extrude hexagon28     Extrude pentagon29      Stock
DOI: 10.5281/zenodo.14039055
title: HybridCAD: A Comprehensive Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models

description: The HybridCAD dataset is a novel resource tailored for hybrid additive-subtractive feature recognition in Computer-Aided Design (CAD) models, uniquely combining features from both manufacturing processes. Building on the MFCAD and MFCAD++ datasets, HybridCAD introduces additive manufacturing features alongside traditional subtractive ones, enabling the exploration and development of machine learning models for more complex, hybrid manufacturing applications. This dataset is especially suited for automatic feature recognition (AFR) research and model training in hybrid manufacturing contexts.

Dataset Composition

The dataset consists of 8,938 Boundary Representation (B-Rep) CAD models distributed into three main directories:





STEP Files:



Contains CAD models in STEP format, each representing a distinct part with hybrid manufacturing features.

Generated using PythonOCC CAD software, these CAD files serve as the foundation for feature recognition tasks.





Feature Labels:



File: feature_labels.txt

Provides unique label IDs for each B-Rep face in every CAD model, denoting the manufacturing feature name it belongs to.

Each CAD model face is labelled according to one of 29 hybrid additive-subtractive features, allowing for accurate and detailed feature recognition.




 





Hierarchical B-Rep Graphs:



Stored in HDF5 format for structured access to B-Rep data.

Detailed structural information is available in h5_structure.txt, explaining the hierarchical arrangement of B-Rep graphs.




Dataset Splits

The dataset is split into training, validation, and testing sets as follows:



Training Set: 6,256 samples (70%)

Validation Set: 1,342 samples (15%)

Testing Set: 1,340 samples (15%)


Hybrid Manufacturing Features

HybridCAD includes a diverse range of additive and subtractive manufacturing features, expanding beyond the subtractive-only features of MFCAD++. This inclusion enables the exploration of hybrid manufacturing processes and the recognition of a broader feature set in CAD models. The complete feature list includes:

Label    Feature0    Chamfer1    Through hole2    Triangular passage3    Rectangular passage4    6-sided passage5    Triangular through slot6    Rectangular through slot7    Circular through slot8    Rectangular through step9    2-sided through step10    Slanted through step11    O-ring12    Blind hole13    Triangular pocket14    Rectangular pocket15    6-sided pocket16    Circular end pocket17    Rectangular blind slot18    Vertical circular end blind slot19    Horizontal circular end blind slot20    Triangular blind step21    Circular blind step22    Rectangular blind step23    Round24    Extrude cylinder25    Extrude rectangle26    Extrude triangle27     Extrude hexagon28     Extrude pentagon29      Stock