基于LSTM的智能制造深度学习模型

Babli Mandloi, Ghanshyam Prasad Dubey, Komal Tahiliani
{"title":"基于LSTM的智能制造深度学习模型","authors":"Babli Mandloi, Ghanshyam Prasad Dubey, Komal Tahiliani","doi":"10.1109/ISCON57294.2023.10111964","DOIUrl":null,"url":null,"abstract":"The conventional manufacturing sector makes use of antiquated machinery and time-consuming, error-prone manual procedures, with catastrophic financial consequences for even the smallest of mistakes. As part of the 4.0 revolution, businesses are incorporating IoT and robots into their existing infrastructure. Manufacturing is only one area that has benefited from the widespread use of AI and machine learning. In this research, a deep learning model for intelligent production is presented, and it is based on the long short-term memory technique.","PeriodicalId":280183,"journal":{"name":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A LSTM based Deep Learning Model for Smart Manufacturing\",\"authors\":\"Babli Mandloi, Ghanshyam Prasad Dubey, Komal Tahiliani\",\"doi\":\"10.1109/ISCON57294.2023.10111964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The conventional manufacturing sector makes use of antiquated machinery and time-consuming, error-prone manual procedures, with catastrophic financial consequences for even the smallest of mistakes. As part of the 4.0 revolution, businesses are incorporating IoT and robots into their existing infrastructure. Manufacturing is only one area that has benefited from the widespread use of AI and machine learning. In this research, a deep learning model for intelligent production is presented, and it is based on the long short-term memory technique.\",\"PeriodicalId\":280183,\"journal\":{\"name\":\"2023 6th International Conference on Information Systems and Computer Networks (ISCON)\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 6th International Conference on Information Systems and Computer Networks (ISCON)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISCON57294.2023.10111964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 6th International Conference on Information Systems and Computer Networks (ISCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISCON57294.2023.10111964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

传统的制造业使用陈旧的机器和耗时、容易出错的人工程序,即使是最小的错误也会带来灾难性的经济后果。作为4.0革命的一部分,企业正在将物联网和机器人纳入其现有基础设施。制造业只是从人工智能和机器学习的广泛使用中受益的一个领域。本文提出了一种基于长短期记忆技术的智能生产深度学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A LSTM based Deep Learning Model for Smart Manufacturing
The conventional manufacturing sector makes use of antiquated machinery and time-consuming, error-prone manual procedures, with catastrophic financial consequences for even the smallest of mistakes. As part of the 4.0 revolution, businesses are incorporating IoT and robots into their existing infrastructure. Manufacturing is only one area that has benefited from the widespread use of AI and machine learning. In this research, a deep learning model for intelligent production is presented, and it is based on the long short-term memory technique.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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:604180095
Book学术官方微信