An adapation Framework for Industry 4.0 Responsive Production Systems

M. Mabkhot, N. Lohse, P. Ferreira
{"title":"An adapation Framework for Industry 4.0 Responsive Production Systems","authors":"M. Mabkhot, N. Lohse, P. Ferreira","doi":"10.1109/INDIN51400.2023.10218127","DOIUrl":null,"url":null,"abstract":"One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness and adaptation enable coping with new market requirements or introducing new products, as demonstrated by the challenges posed by COVID-19. Despite the economic and sustainability benefits, practitioners often only consider adapting their systems for limited cases. They rely on simple, intuitive estimates of adaptation metrics and do not optimize these milestone decisions, leading to missed opportunities. Currently, there are no reliable methods for measuring system adaptability, quantifying the effort required to adapt it from one state to another, or optimizing the adaptation decision. This paper proposes a comprehensive adaptation framework based on complexity index quantification, graph network estimation, and multi-objective optimization. The framework outlines three approaches for estimating efforts in adapting the physical system design, upgrading control software, and optimizing the adaptation decision. The application on a lab-sized cell demonstrates the framework’s ability to estimate the adaption processes metric and generate a set of optimized cell states. The application reveals potentials for improvement and extension of the three approaches.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN51400.2023.10218127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness and adaptation enable coping with new market requirements or introducing new products, as demonstrated by the challenges posed by COVID-19. Despite the economic and sustainability benefits, practitioners often only consider adapting their systems for limited cases. They rely on simple, intuitive estimates of adaptation metrics and do not optimize these milestone decisions, leading to missed opportunities. Currently, there are no reliable methods for measuring system adaptability, quantifying the effort required to adapt it from one state to another, or optimizing the adaptation decision. This paper proposes a comprehensive adaptation framework based on complexity index quantification, graph network estimation, and multi-objective optimization. The framework outlines three approaches for estimating efforts in adapting the physical system design, upgrading control software, and optimizing the adaptation decision. The application on a lab-sized cell demonstrates the framework’s ability to estimate the adaption processes metric and generate a set of optimized cell states. The application reveals potentials for improvement and extension of the three approaches.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
工业4.0响应式生产系统的适应框架
工业4.0的主要目标之一是创建更具响应性的系统。正如2019冠状病毒病带来的挑战所证明的那样,响应能力和适应能力能够应对新的市场需求或推出新产品。尽管经济和可持续发展的好处,从业者往往只考虑调整他们的系统为有限的情况。它们依赖于对适应度量的简单、直观的估计,而不优化这些里程碑决策,从而导致错失机会。目前,没有可靠的方法来测量系统适应性,量化从一种状态调整到另一种状态所需的努力,或优化适应决策。提出了一种基于复杂度指标量化、图网络估计和多目标优化的综合自适应框架。该框架概述了三种方法,用于评估适应物理系统设计、升级控制软件和优化适应决策的努力。在实验室大小的单元上的应用程序演示了框架估计适应过程度量并生成一组优化的单元状态的能力。应用显示了这三种方法的改进和扩展潜力。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Technical Debt Management in Industrial ML - State of Practice and Management Model Proposal Measuring the Robustness of ML Models Against Data Quality Issues in Industrial Time Series Data 5G packet delay considerations for different 5G-TSN communication scenarios Non-Interventional Precise TC Assessment for Enhancing Consumer Energy Flexibility Model-based Automation of TSN Configuration for Industrial Distributed Systems
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1