Bulnes et al., 2011 - Google Patents

A Technique for Clustering Individual Defects from Images of Steel Strips with Periodical Defects.

Bulnes et al., 2011

View PDF
Document ID
481796224676909611
Author
Bulnes F
Garcia D
Molleda J
Publication year
Publication venue
MVA

External Links

Snippet

The steel strips produced in steel-making plants, are used as raw material in many other industries, so quality control is an essential aspect. One factor that indicates the quality of a steel strip is the number of defects, such as holes or scratches, on its surface. This paper …
Continue reading at www.mva-org.jp (PDF) (other versions)

Similar Documents

Publication Publication Date Title
JP2010524695A (en) Method for detecting and classifying surface defects in continuously cast slabs
CN104914111A (en) Strip steel surface defect on-line intelligent identification and detection system and detection method
CN103221807A (en) Rapid processing and detection of non-uniformities in web-based materials
Park et al. Development of a finite element analysis program for roller leveling and application for removing blanking bow defects of thin steel sheet
JP5441824B2 (en) Manufacturing condition determination system for metal strip materials
CN103842920B (en) The method of the procedure parameter of checking manufacture process
Hong et al. Filter-PCA-based process monitoring and defect identification during climbing helium arc welding process using DE-SVM
Bulnes et al. A Technique for Clustering Individual Defects from Images of Steel Strips with Periodical Defects.
Kriegesmann et al. Design optimization of composite cylindrical shells under uncertainty
TWI461246B (en) Method for the classification of defects and running of lamination cylinder grinding
JP7517369B2 (en) METHOD FOR DETERMINING STEEL PLATE PROFILE, METHOD FOR SETTING PROCESS, MANUFACTURING METHOD, AND METHOD FOR GENERATING STEEL PLATE PROFILE DETERMINATION MODEL
Bahadirov et al. The effect of roller pressure and feed rate on hides squeezing
Lishchenko et al. Comparison of measured surface layer quality parameters with simulated results
Bulnes et al. Vision-based technique for periodical defect detection in hot steel strips
Schulte et al. Model-based control of the strip roughness in cold rolling
Mohammed et al. Optimized fuzzy c-means clustering methods for defect detection on leather surface
Bulnes et al. Periodic defects in steel strips: Detection through a vision-based technique
KR102161348B1 (en) Apparatus and Method for Determining Descaling Method Based on Image of Slab
Feyzioğlu et al. Detection of Defects in Rolled Stainless Steel Plates by Machine Learning Models
BULNES et al. PERIODIC DEFECTS IN STEEL STRIPS
Vaidelienė et al. The use of Haar wavelets in detecting and localizing texture defects
Spinola et al. Image processing for surface quality control in stainless steel production lines
Bulnes et al. Detection of periodical patterns in the defects identified by computer vision systems
Hamidi et al. A novel method for detecting the type of surface defects of hot rolled steel strip using the convolutional neural network
Ordieres-Meré et al. Advanced predictive quality control strategy involving different facilities