Work

Detecting Defect Copper Parts Based on Machine Vision

Year: 2024

Type: article

Abstract: The quality detection of copper alloys plays a crucial role in enhancing the factory’s economic and production efficiency, particularly in addressing surface defects and ensuring component size and specification accuracy. This paper proposes a deep learning-based quality detection method for detecting the defect on the surfaces of copper alloy components, encompassing both surface defect detection and external dimensional quality assessment. For defect detection, the method achieves an accuracy of 94% with an average detection time of 29 ms. In dimensional quality detection, the accuracy reaches 96%, with an average detection time of 3 seconds. Validation confirms that this deep learning-based method significantly improves the factory’s detection efficiency.

Source: IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

DOI: 10.1587/transfun.2024eap1109

Authors: Zhenhai TAN, Yun YANG, Xiaoman WANG, Fayez ALQAHTANI

Institutions: Nanchang University, Nanchang Hangkong University, King Saud University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: bronze

Funder: King Saud University

Award: G5757904404