结合区域距离和循环密度聚类的钢板缺陷归集方法OA

Defects such as dents and scratches frequently occur on the surface of steel plates produced in steel mills due to factors like process and equipment control precision.Manual grinding is typically required to address such defects in actual production operations.In recent years,automated grinding has emerged as a predominant trend to enhance efficiency.However,defects on steel plate surfaces are often characterized by uneven distribution and varied shapes and sizes.In order to effectively collect defect dense areas and improve subsequent grinding efficiency,a new method called RD(Region Distance)+DB-DBSCAN(Double Density-Based Spatial Clustering of Applications with Noise)is introduced in this paper.RD is combined with the DB-DBSCAN algorithm in this approach to effectively clustering surface defects.RD is introduced to resolve the limitation of traditional clustering methods in handling data points with area attributes.The DB-DBSCAN conducts initial clustering using DBSCAN,partitioning the data into density-differentiated partitions.Subsequently,Eps values for each partition were determined through K-distance graphs,followed by local clustering conducted within these regions.Noise points were merged into relevant clusters during the process.The limitations of the traditional DBSCAN algorithm are addressed by the method,enhancing clustering performance in scenarios with uneven data density.Experiments conducted on typical UCI datasets and synthetic datasets demonstrated that the DB-DBSCAN algorithm achieved superior NMI and ARI scores compared to DBSCAN and its extended clustering algorithms across most datasets.Furthermore,experiments performed on steel mill datasets revealed that the RD+DB-DBSCAN method proposed in this study exhibited exceptional clustering performance for handling complex and highly concentrated defect distributions.