CNC Machined Parts Burr Recognition Dataset
#Image Classification #Object Detection #Quality Inspection #Manufacturing #Process Improvement
- 20000 records
- 3.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-15
AI Analysis & Value Prop
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.
Dataset Insights
Sample Examples
8a3a8752**.jpg|1067*800|19.22 KB
de8a3af6**.jpg|2620*2620|280.30 KB
d2eaf3cb**.jpg|800*1422|134.30 KB
18d03c7a**.jpg|2560*2428|307.96 KB
Technical Specifications
| 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
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| 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 |
Frequently Asked Questions
- What is the CNC Machined Parts Burr Detection Dataset?
- The CNC Machined Parts Burr Detection Dataset is a target detection dataset specifically designed for identifying burrs on CNC machined parts.
- What are the main applications of the CNC Machined Parts Burr Detection Dataset?
- This dataset is primarily used for industrial inspection and quality control, helping to identify and detect burrs on CNC machined parts.
- What are the benefits of using the CNC Machined Parts Burr Detection Dataset?
- Using this dataset can improve the efficiency of defect detection in CNC parts production and reduce the cost of manual inspection.
- What type of data does the CNC Machined Parts Burr Detection Dataset contain?
- The dataset contains images for object detection, focusing on the identification of burrs in CNC machined parts.
- How is the CNC Machined Parts Burr Detection Dataset applied in industrial inspection?
- In industrial inspection, this dataset can be used to train machine learning models to automatically identify burrs on machined parts, thereby improving production quality.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={CNC Machined Parts Burr Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/705ab7a675eabd1c40ffde767a931c2b?dataset_scene_id=2},
urldate={2025-08-28},
keywords={CNC machining dataset,burr recognition,industrial quality control,image classification dataset},
version={1.0}
}Using this in research? Please cite us.
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