高温铸坯表面缺陷在线检测技术开发及应用

doi:10.13228/j.boyuan.issn1005-4006.20220123

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4032 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the improvement of the requirements of intellectualization in the iron and steel industry, iron and steel enterprises are no longer satisfied with the traditional manual surface defect sampling method after high-temperature slab cooling. In order to realize the non-destructive online detection and quality judgment of slab surface defects in the process of continuous casting, based on the theory of machine vision non-destructive detection, a method of real-time detection of slab defect contour based on CCD and laser scanning is developed in this project. The three-dimensional digital reconstruction of slab surface defect morphology is carried out by using machine vision and graphic processing methods, which effectively realizes the recognition and classification of defect images. Combined with laboratory calibration and field application, the results show that 16 CCD can meet the requirements of full width circumferential detection of high-temperature slab, and defect size detection resolution reaches millimeter level. Through digitization and accurate positioning of the location and depth of defects, it can meet the online detection and monitoring of high-temperature slab defects, and provide a strong support for the traceability of slab product quality.

Key words： casting slab at high temperature surface defect laser triangulation machine vision online detection

Received: 19 July 2022

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	HUANG Jun
	WANG Bao-feng
	ZHANG Xue-yuan
	TENG Fei
	DING Guo

Cite this article:

HUANG Jun,WANG Bao-feng,ZHANG Xue-yuan等. Development and application of on line detection technology for surface defects of high temperature billets[J]. CONTINUOUS CASTING, 2022, 41(6): 61-67.

URL:

http://www.chinamet.cn/Jweb_lz/EN/10.13228/j.boyuan.issn1005-4006.20220123 OR http://www.chinamet.cn/Jweb_lz/EN/Y2022/V41/I6/61

[1]	常运合,吴伟勤,邓俊棕,等.横轧钢板表面裂纹成因分析及控制[J].河北冶金,2021(7):59.
[2]	刘华松. 包晶钢连铸坯表面裂纹与组织控制研究[D]. 北京:北京科技大学,2021.
[3]	张新文,江宏亮,轩康乐,等.热轧圆钢Q345E表面裂纹的研究与控制[J].中国冶金,2021,31(4):44.
[4]	张磊,翟冰钰,王万林.薄板坯连铸及其铸坯表面缺陷的形成机理[J].连铸,2020(4):22.
[5]	孙丽钢,贾生,李鑫.宽厚板连铸坯表面裂纹的产生原因及控制[J].连铸,2020(4):70.
[6]	安航航,韩传基,高仲. 一种连铸铸坯表面质量的在线控制系统及控制方法[P]. 中国:CN102581244A,2012-07-18.
[7]	阳燚. 高温连铸坯表面缺陷无损检测方法研究[D]. 杭州:浙江工业大学,2012.
[8]	吴晓鹏,林介邦,唐辉,等.基于机器视觉的铸坯表面缺陷检测系统的研制[J].武钢技术,2010,48(1):49.
[9]	赵立明.基于激光扫描成像与异源CCD融合的连铸热坯表面缺陷检测方法研究[D].重庆:重庆大学,2014.
[10]	徐科,周茂贵,徐金梧,等.基于线型激光的热轧带钢表面在线检测系统[J].北京科技大学学报,2008(1):77.
[11]	欧阳奇,张兴兰,陈登福,等.高温连铸坯表面缺陷的机器视觉无损检测[J.重庆大学学报(自然科学版),2007(11):27.
[12]	赵扬,刘伟,郭说,等.基于激光声磁技术的钢坯表面裂纹非接触式无损检测研究[J].中国机械工程, 2012(10):724.
[13]	郑嵘.铸坯表面缺陷图像检测方法研究[D].上海:上海交通大学,2013.
[14]	周鹏. 基于多信息融合的高温铸坯表面缺陷在线检测方法[D]. 北京:北京科技大学,2015.
[15]	甘胜丰,雷维新,周成宏.机器视觉表面缺陷检测技术及其在钢铁工业中的应用[M]. 武汉:华中科技大学出版社,2017.
[16]	胡嘉成,王向阳,刘晗.基于深度学习的连铸坯表面缺陷检测[J].上海大学学报(自然科学版),2019,25(4):445.
[17]	黄华贵, 刘迎港, 黄海林,等. 热轧板坯头部翘曲机器视觉检测与BP神经网络预测控制[J]. 中国冶金, 2022, 32(9): 85.
[18]	张付祥, 赵阳, 黄永建, 等. 特钢棒材标记方案设计与信息码识别[J]. 中国冶金, 2020, 30(3): 28.
[19]	王春梅,黄风山,刘咪. 连铸坯端面信息码智能识别系统[J]. 中国冶金, 2019, 29(5): 33.
[20]	徐冬,刘克东,王晓晨,等. 粗轧中间坯镰刀弯在线检测系统的研发与应用[J]. 中国冶金, 2019, 29(2): 61.
[21]	李江昀, 杨志方, 郑俊锋, 等. 深度学习技术在钢铁工业中的应用[J]. 钢铁, 2021, 56(9): 43.
[22]	刘晓明. 多尺度几何变换在金属表面缺陷识别中的应用[D]. 北京:北京科技大学,2020.
[23]	岑若晨. 基于图像特征的连铸钢板缺陷检测与分类研究[D]. 南京:南京理工大学,2020.
[24]	吴伍彬.基于面阵相机的铸坯表面缺陷检测系统[J].福建冶金,2019,48(5):61.
[25]	汤勃,孔建益,伍世虔.机器视觉表面缺陷检测综述[J].中国图象图形学报,2017,22(12):1640.
[26]	韩阳, 董晶, 李杰, 等. 铁尾矿高温熔化行为的视觉解析及应用[J]. 钢铁, 2021, 56(2): 147.
[27]	李小占, 闫书宗, 徐冬, 等. 基于单目线结构光的智能仓库车辆识别系统[J]. 中国冶金, 2022, 32(9): 113.
[28]	许磊, 蒋婷, 陈卫嘉. 铝合金平轧过程中端部自动剪切方法的开发和应用[J]. 中国冶金, 2020, 30(4): 37.
[29]	毛欣翔,刘志,任静茹,等.基于深度学习的连铸板坯表面缺陷检测系统[J].工业控制计算机,2019,32(3):66.
[30]	石军彦. 基于线阵CCD钢板表面缺陷检测系统的研究[D].秦皇岛:燕山大学,2010.
[31]	吴家伟,严京旗,方志宏,等.基于图像显著性特征的铸坯表面缺陷检测[J].智能系统学报,2012,7(1):75.