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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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A machine vision based defect detection method for coated carbide CNC inserts and its industrial automation

Junqi Hu¹, Shi Chen¹, Sheng Yin²

¹School of Physical Science and Technology, Southwest Jiaotong University, Chengdu, 610031, China.

Scientific Reports

|May 11, 2026

Summary

This study introduces an automated system using the Attention-Augmented Multi-Defect YOLO (A2MD-YOLO) model for real-time detection of surface defects on Computer Numerical Control (CNC) inserts, improving precision and reducing errors.

Keywords:

CNC machine tools Deep learning Metal surface defect detection Small defect detection

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Area of Science:

Manufacturing Engineering
Computer Vision
Materials Science

Background:

Surface defects on Computer Numerical Control (CNC) inserts critically impact machining precision.
Manual inspection methods are inefficient and error-prone, necessitating automated solutions.

Purpose of the Study:

To develop and implement an automated real-time system for detecting surface defects on CNC inserts.
To address challenges in defect detection, including size variation and appearance similarities.

Main Methods:

Creation of a dedicated dataset of CNC tool inserts with annotated defect categories.
Proposal and application of an Attention-Augmented Multi-Defect YOLO (A2MD-YOLO) model tailored for insert defect detection.
Integration of the A2MD-YOLO algorithm into a hardware system for practical implementation.

Main Results:

The A2MD-YOLO model significantly improved detection accuracy, with an increase in [Formula: see text] from 0.529 to 0.571.
The missed detection rate was substantially reduced from 21.4% to 11.8%.
The system demonstrated enhanced detection efficiency and accuracy compared to traditional methods.

Conclusions:

The developed A2MD-YOLO model effectively detects surface defects on CNC inserts in real-time.
Automated inspection systems are crucial for maintaining high machining precision and quality control.
The successful hardware implementation validates the practical applicability of the proposed algorithm.