Study on anti-interference detection of machining surface defects under the influence of complex environment

Wei Chen (), Bin Zou (), Ting Lei (), Qinbing Zheng (), Chuanzhen Huang (), Lei Li () and Jikai Liu ()
Additional contact information
Wei Chen: Shandong University
Bin Zou: Shandong University
Ting Lei: Shandong University
Qinbing Zheng: Shandong University
Chuanzhen Huang: Yanshan University
Lei Li: Shandong University
Jikai Liu: Shandong University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 5, 853-874

Abstract: Abstract When detecting surface defects in a complex industrial cutting environment, the defects are easily polluted and covered by interfering factors (chips or coolant residues). The defect of the surface images with interference factors is a novel problem in the existing studies, and it is also a difficulty in the detection field. Hence, this paper proposes a high-precision anti-interference detection method for surface defects under the influence of complex environment. The detection method provides a new research idea, which is divided into three main processes: interference regions location, interference regions repair, defect detection. The regions affected by interference factors are adaptively located through the proposed Efficient Channel Attention Network (ECANet)-DeeplabV3 + network model. The mean Pixel Accuracy (mPA) and mean Intersection over Union (mIoU) of ECANet-DeeplabV3 + network model for interference factor identification are 98.37% and 95.46%, respectively. The Criminisi algorithm is improved from priority, finding the best matching block, and searching regions. Directional repair based on the improved Criminisi algorithm is performed on the identified interfering regions removing the interfering factors in the image, which is the research core. Then, defect detection is performed on the repaired image using the improved superpixel technology. At the same time, the defect detection results provide a variety of surface defect information for the cutting staff, including defect types, the number of pixels in different defect regions, and the area ratio of different defect regions. This information improves predictive maintenance and surface quality control.

Keywords: Surface defect detection; Interference factors; Improved Criminisi algorithm; Predictive maintenance (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10845-023-02276-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02276-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-023-02276-0

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().