Smart Reconfigurable Manufacturing: Literature Analysis

Xingyu Li , Ragu Athinarayanan , Baicun Wang , Wei Yuan , Quan Zhou , Martin Jun , Jose Bravo , Robert X Gao , Lihui Wang , Yoram Koren
{"title":"Smart Reconfigurable Manufacturing: Literature Analysis","authors":"Xingyu Li ,&nbsp;Ragu Athinarayanan ,&nbsp;Baicun Wang ,&nbsp;Wei Yuan ,&nbsp;Quan Zhou ,&nbsp;Martin Jun ,&nbsp;Jose Bravo ,&nbsp;Robert X Gao ,&nbsp;Lihui Wang ,&nbsp;Yoram Koren","doi":"10.1016/j.procir.2023.09.228","DOIUrl":null,"url":null,"abstract":"<div><p>Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice.</p></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212827123009551/pdf?md5=68e7c8f1eeea354dcf1b6f701078052d&pid=1-s2.0-S2212827123009551-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827123009551","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

Smart manufacturing (SM) enhances the competitiveness of manufacturing companies by promoting automation and overall equipment effectiveness (OEE), targeting to produce 100% qualified products fully automatically. One of the key challenges to the SM initiatives is the continuous demand fluctuations in the specification and quantity, especially when a new product variant comes to the production line. Reconfigurable manufacturing (RM) system provides cost-effective, rapid response to abrupt market changes. It provides a solution by its flexibility in repurposing tools, adding machines, and modifying software to rapidly respond to changing demands at low unit costs. The ability of SM technologies through self-programming and cloud computation may significantly complements RM initiatives. There is increasing evidence that SM and RM may augment each other through their complementary strengths, leading to the new paradigm of smart reconfigurable manufacturing (SRM). To highlight the complementary strengths, this paper investigates the converging trend of RM and SM based on natural language processing, e.g., topic modeling and semantic embedding. Key characteristics and industrial use cases are subsequently summarized to systematically delineate the new SRM paradigm and illustrate its advantages and feasibility in practice.

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
智能可重构制造:文献分析
智能制造(Smart Manufacturing,SM)通过促进自动化和整体设备效率(OEE)来提高制造企业的竞争力,目标是全自动生产 100% 的合格产品。智能制造计划面临的主要挑战之一是规格和数量方面的持续需求波动,尤其是当生产线上出现新的产品变体时。可重构制造(RM)系统能以经济高效的方式快速应对突如其来的市场变化。它可以灵活地重新利用工具、增加机器和修改软件,以较低的单位成本快速响应不断变化的需求。SM 技术通过自我编程和云计算的能力可以极大地补充 RM 计划。越来越多的证据表明,智能可重构技术和智能可重构制造技术可以通过优势互补相互促进,从而形成智能可重构制造(SRM)的新模式。为了突出优势互补,本文研究了基于自然语言处理(如主题建模和语义嵌入)的 RM 和 SM 的融合趋势。随后总结了关键特征和工业用例,系统地描述了新的可重构制造范式,并说明了其在实践中的优势和可行性。
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
CiteScore
3.80
自引率
0.00%
发文量
0
期刊最新文献
Editorial Preface Off-axis monitoring of the melt pool spatial information in Laser Metal Deposition process Machine learning-assisted collection of reduced sensor data for improved analytics pipeline Demand-Oriented Optimization of Machine Tools: a Closed Loop Approach for Safe Exploration in Series Production
×
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