Q-SYM2 and Automatic Scrap Classification a joint solution for the Circular economy and sustainability of Steel Manufacturing, to ensure the scrap yard operates competitively

Davide Armellini, M. Ometto, Cristiano Ponton
{"title":"Q-SYM2 and Automatic Scrap Classification a joint solution for the Circular economy and sustainability of Steel Manufacturing, to ensure the scrap yard operates competitively","authors":"Davide Armellini, M. Ometto, Cristiano Ponton","doi":"10.1109/IJCNN55064.2022.9892611","DOIUrl":null,"url":null,"abstract":"A hype topic, one that now has become an established idea, is the possibility to increase plant efficiency by gaining and applying a better awareness of how scrap is performing in the melting process. Scrap management becomes the key point in cost reduction since it could comprise up to the 50% of the overall production costs. Technological innovations promise to be the driver to improving raw material management, shortening its acquisition time and reducing the waste during the metallurgical process. Expensive raw materials require a huge involvement of plant resources, and are highly dependent on the human factor. All the quality and logistics decisions belong to the judgment of the operators, increasing the chance of non-conformities (e.g., erroneous classification, material discharged in the wrong location, error loading material in the buckets). To overcome these issues, online classification of the scrap is the keystone. Starting from the arrival of scrap at the plant, through the acceptance of the delivery note and the check-in of the carriers, Automatic Scrap Classification gives support to inbound-scrap control and classification, enabling real-time traceability of the scrap inside the bays. The Quality Control System will benefit from all the details of the material used in production. Danieli Automation implemented the Q-ASC a system that, leveraging Artificial Intelligence (AI) and deep learning techniques, can assist scrap classification procedures through computer vision and automatic scrap recognition. The goal of scrap identification is to localize and assign a specific class label to a given visual sample of scrap or inert/hazardous material. The classification can be conducted using different methodologies based on material shapes or dimensions. Q-ASC is the entry point for the Scrap Yard Management and can be considered as the central data hub for managing the scrap inbound to the plant, connecting all the systems requiring reliable scrap data.","PeriodicalId":106974,"journal":{"name":"2022 International Joint Conference on Neural Networks (IJCNN)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Joint Conference on Neural Networks (IJCNN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IJCNN55064.2022.9892611","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

A hype topic, one that now has become an established idea, is the possibility to increase plant efficiency by gaining and applying a better awareness of how scrap is performing in the melting process. Scrap management becomes the key point in cost reduction since it could comprise up to the 50% of the overall production costs. Technological innovations promise to be the driver to improving raw material management, shortening its acquisition time and reducing the waste during the metallurgical process. Expensive raw materials require a huge involvement of plant resources, and are highly dependent on the human factor. All the quality and logistics decisions belong to the judgment of the operators, increasing the chance of non-conformities (e.g., erroneous classification, material discharged in the wrong location, error loading material in the buckets). To overcome these issues, online classification of the scrap is the keystone. Starting from the arrival of scrap at the plant, through the acceptance of the delivery note and the check-in of the carriers, Automatic Scrap Classification gives support to inbound-scrap control and classification, enabling real-time traceability of the scrap inside the bays. The Quality Control System will benefit from all the details of the material used in production. Danieli Automation implemented the Q-ASC a system that, leveraging Artificial Intelligence (AI) and deep learning techniques, can assist scrap classification procedures through computer vision and automatic scrap recognition. The goal of scrap identification is to localize and assign a specific class label to a given visual sample of scrap or inert/hazardous material. The classification can be conducted using different methodologies based on material shapes or dimensions. Q-ASC is the entry point for the Scrap Yard Management and can be considered as the central data hub for managing the scrap inbound to the plant, connecting all the systems requiring reliable scrap data.
Q-SYM2和自动废料分类是钢铁制造循环经济和可持续性的联合解决方案,以确保废料场的竞争力
一个炒作的话题,现在已经成为一个既定的想法,是通过更好地了解废料在熔化过程中的表现来提高工厂效率的可能性。废料管理成为降低成本的关键,因为它可能占总生产成本的50%。技术创新有望成为改善原材料管理,缩短其获取时间和减少冶金过程中的浪费的驱动力。昂贵的原材料需要大量的植物资源参与,并且高度依赖于人为因素。所有的质量和物流决策都属于操作员的判断,增加了不合格的机会(例如,错误的分类,物料排在错误的位置,物料在桶中装载错误)。为了克服这些问题,废钢在线分类是关键。从废料到达工厂开始,通过接受交货单和承运人的登记,自动废料分类为进站废料控制和分类提供支持,使废料在仓库内的实时可追溯性成为可能。质量控制系统将受益于生产中使用的材料的所有细节。达涅利自动化实施了Q-ASC系统,该系统利用人工智能(AI)和深度学习技术,可以通过计算机视觉和自动废料识别来辅助废料分类程序。废料识别的目标是对废料或惰性/有害材料的给定视觉样品进行定位和分配特定的类别标签。分类可以根据材料的形状或尺寸使用不同的方法进行。Q-ASC是废料场管理的入口点,可以被视为管理进入工厂的废料的中央数据中心,连接所有需要可靠废料数据的系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信