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Dataset . 2025
License: CC BY
Data sources: Datacite
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Dataset . 2025
License: CC BY
Data sources: Datacite
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Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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HybridCAD++: Expanded Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models

Authors: Khan, Muhammad Tayyab; Chen, Lequn;

HybridCAD++: Expanded Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models

Abstract

The HybridCAD++ dataset is a significantly expanded version of the HybridCAD dataset, offering a larger volume of CAD models and a more comprehensive set of hybrid additive-subtractive manufacturing features. This dataset includes additional feature classes, bringing the total to 36, and contains over 161,000 samples—making it a unique and robust resource for machine learning applications in hybrid manufacturing feature recognition. Key Differences from HybridCAD Increased Dataset Volume: HybridCAD++ features a total of 161,441 CAD models, significantly larger than the original HybridCAD dataset. Expanded Feature Classes: This dataset includes 36 feature labels, with newly added classes. This increase in feature variety enhances the dataset's applicability to complex hybrid manufacturing scenarios. Dataset Composition The dataset includes three primary components: STEP Files: Each CAD model is stored in STEP format and includes labeled B-Rep faces for hybrid manufacturing feature recognition. The CAD models were generated programmatically using PythonOCC, ensuring consistent quality and scalability. Feature Labels: File: feature_labels.txt Contains label IDs for each hybrid additive-subtractive feature across B-Rep faces in each CAD model. With 36 unique feature classes, this file allows precise mapping of CAD model faces to specific hybrid features. Hierarchical B-Rep Graphs: Stored in HDF5 format, these graphs provide structured access to B-Rep data, as detailed in h5_structure.txt. Dataset Splits The dataset is divided into three subsets, structured for effective model training and evaluation: Training Set: 113,008 samples (70%) Validation Set: 32,288 samples (20%) Testing Set: 16,145 samples (10%) Full Feature Label List This comprehensive list includes both subtractive and additive manufacturing features, with added classes for more intricate hybrid manufacturing applications: 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 Elliptical/Oval blind hole30 Elliptical/Oval through hole31 Slot hole32 Obround boss33 5-sided passage34 5-sided pocket35 Cylinder with hole36 Stock

Keywords

FOS: Computer and information sciences, Computer and information sciences, Additive manufacturing, Subtractive manufacturing, Mechanical Engineering, Design for Additive Manufacture, FOS: Mechanical engineering, Machining, Manufacturing engineering, Manufacturing, Artificial Intelligence, Computer-Aided Design, CAD

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average