Surface roughness estimation of shot blasted steel bars using machine vision

被引：0

作者：

Lydén, S

Kälviäinen, H

Nykänen, J

机构：

[1] Lappeenranta Univ Technol, Dept Informat Proc, FIN-53851 Lappeenranta, Finland

[2] Imatra Steel Imatra, FIN-55100 Imatra, Finland

来源：

INTELLIGENT ROBOTS AND COMPUTER VISION XXII: ALGORITHMS, TECHNIQUES, AND ACTIVE VISION | 2004年 / 5608卷

关键词：

shot blasting; steel bars; surface roughness; image processing; machine vision; differential evolution;

D O I：

10.1117/12.571435

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

During the manufacturing process steel bars are cleaned of roll scale by shot blasting, before further processing the bars by drawing. The main goal of this project is to increase the automation of the shot blasting process by machine vision. For this purpose a method is needed for estimating the surface roughness and other anomalies from the steel bars from digital images after the shot blasting. The goal of this method is to estimate if the quality of shot blasting is sufficient considering the quality of the final products after the drawing. In this project a method for normalising the images is considered and several methods for estimating the actual roughness level are experimented. During the experiments a best method was one where the roughness levels are calculated directly from the images as if the images were similar to other measuring sources and the grey-level values in the images represent the deviation on the bar surface. This at least separates the different samples.

引用

页码：278 / 289

页数：12