CNC Machined Parts Burr Recognition Dataset
V1.0Latest Update: | 2025-10-16 | Samples: | 20000 records |
Samples: | 20000 records | ||
File Size: | 3.5G | Format: | JPG/PNG/JSON |
Format: | JPG/PNG/JSON | ||
Data Domain: | Image | Holder: |  MOBIUSI INC |
Holder: | MOBIUSI INC | ||
Industry Scope: | Quality Inspection | Manufacturing | Process Improvement | ||
Applications: | Image Classification | Object Detection | ||
Brief Introduction
In the current industrial sector, the quality control of CNC machined parts faces significant challenges due to the increasing complexity of products and the demand for higher precision. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the technical challenge of automating burr detection in machined parts to improve efficiency and accuracy. The dataset consists of images captured from various CNC machining processes under controlled lighting conditions. Quality control measures include multiple rounds of annotation, consistency checks among annotators, and expert reviews to ensure high-quality labels. The data is stored in JPG format organized by folders categorized by burr types and quality scores.
Sample Examples
| Image | File Name | Resolution | Target Type | Burr Presence | Burr Location | Burr Size | Surface Condition | Lighting Conditions | Shooting Angle | Burr Severity | Workpiece Size | Workpiece Material |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
 | 8a3a875218c6193cff73d004cbb47b17.jpg | 1067*800 | threaded shaft | present | top edge | approximately 1mm | smooth | bright | front slightly overhead | moderate | approximately 50mm | aluminum |
 | de8a3af674928fa1ee9246c73e0db106.jpg | 2620*2620 | square plate | present | bottom edge | approximately 1mm | smooth | bright | overhead | moderate | approximately 100mm x 50mm | steel |
 | d2eaf3cbd72f14365dea0fa5f994aaa8.jpg | 800*1422 | Metal Plate | Present | Image edge and around holes | Approximately 1mm | Rough | Dim | Front view | Moderate | Approximately 50mm | Steel |
 | 18d03c7a5f24bdbf8ec7e14133273a0b.jpg | 2560*2428 | Plate | Present | Bottom right corner | Approximately 2mm | Rough | Bright | Top view | Moderate | Undetermined | Aluminum |
Data Structure
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_type | string | The type of CNC machined parts in the image, such as bolts, gears, etc. |
| burr_presence | boolean | Whether there are burrs in the image |
| burr_location | string | Description of the location of the burr in the image, such as top left, middle right, etc. |
| burr_size | float | The size of the burr in millimeters |
| surface_condition | string | Description of the machining quality and state of the workpiece surface, such as smooth, rough, etc. |
| lighting_condition | string | Description of lighting conditions during image capture, such as bright, dim, etc. |
| capture_angle | string | Angle at which the image is taken, such as frontal, overhead, etc. |
| burr_severity | string | Classification of burr severity, such as slight, moderate, severe, etc. |
| object_size | float | Actual size of the workpiece, measured in millimeters. |
| object_material | string | Material of the workpiece, such as aluminum, steel, copper, etc. |
Compliance Statement
| Item | Content |
|---|---|
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China’s Data Security Law / EU GDPR / supports enterprise data access logs |
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