Machine Learning and Automation in Concurrent Engineering

K. Vijayakumar
{"title":"Machine Learning and Automation in Concurrent Engineering","authors":"K. Vijayakumar","doi":"10.1177/1063293X221108831","DOIUrl":null,"url":null,"abstract":"In the past few years, Science has played an impressive role in providing solutions to various real-life problems. The current growth in the domain of science, technology and computing has helped the human community to live life with a better ambience. The enhanced occupation helps humans, access a wide variety of recent facilities, which further helps to enhance their lifestyle and their work atmosphere. One of the major contributors to this enhancement is Concurrent Engineering (CE), which focuses on time optimization, all the while maintaining the quality of a developing product. Thus, it provides optimal solutions to challenges faced in our day-to-day life. Concurrent Engineering is implemented through CAD, Resource Management, Digital simulation and Process planning along with improved efficiency and flexibility. Likewise, Machine Learning (ML) is also another domain which plays a crucial function in improving the lifestyle of human community. The ML algorithms and methodologies allow the development of models by systems, to learn and train from input datasets, and generate results based on the provided inputs. The implementation of the same improves efficiency, productivity and decisionmaking capabilities. When ML methodologies support CE, the overall capability and accuracy, of the system is powered up. Thus, it helps humankind to improve the current facilities and Technologies.","PeriodicalId":10680,"journal":{"name":"Concurrent Engineering","volume":"30 1","pages":"133 - 134"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Concurrent Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1063293X221108831","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

Abstract

In the past few years, Science has played an impressive role in providing solutions to various real-life problems. The current growth in the domain of science, technology and computing has helped the human community to live life with a better ambience. The enhanced occupation helps humans, access a wide variety of recent facilities, which further helps to enhance their lifestyle and their work atmosphere. One of the major contributors to this enhancement is Concurrent Engineering (CE), which focuses on time optimization, all the while maintaining the quality of a developing product. Thus, it provides optimal solutions to challenges faced in our day-to-day life. Concurrent Engineering is implemented through CAD, Resource Management, Digital simulation and Process planning along with improved efficiency and flexibility. Likewise, Machine Learning (ML) is also another domain which plays a crucial function in improving the lifestyle of human community. The ML algorithms and methodologies allow the development of models by systems, to learn and train from input datasets, and generate results based on the provided inputs. The implementation of the same improves efficiency, productivity and decisionmaking capabilities. When ML methodologies support CE, the overall capability and accuracy, of the system is powered up. Thus, it helps humankind to improve the current facilities and Technologies.
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
并行工程中的机器学习和自动化
在过去的几年里,科学在为各种现实问题提供解决方案方面发挥了令人印象深刻的作用。当前科学、技术和计算领域的发展帮助人类社会生活在一个更好的环境中。增强的职业有助于人类获得各种各样的最新设施,这进一步有助于改善他们的生活方式和工作氛围。这种增强的主要贡献者之一是并发工程(Concurrent Engineering, CE),它关注于时间优化,同时保持开发产品的质量。因此,它为我们日常生活中面临的挑战提供了最佳解决方案。并行工程通过CAD、资源管理、数字仿真和工艺规划实现,提高了效率和灵活性。同样,机器学习(ML)也是另一个在改善人类社区生活方式方面发挥关键作用的领域。机器学习算法和方法允许系统开发模型,从输入数据集学习和训练,并根据提供的输入生成结果。同样的实现可以提高效率、生产力和决策能力。当机器学习方法支持CE时,系统的整体能力和准确性将得到提升。因此,它有助于人类改善现有的设施和技术。
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
Sensitivity study of process parameters of wire arc additive manufacturing using probabilistic deep learning and uncertainty quantification Retraction Notice Decision-making solutions based artificial intelligence and hybrid software for optimal sizing and energy management in a smart grid system Harness collaboration between manufacturing Small and medium-sized enterprises through a collaborative platform based on the business model canvas Research on the evolution law of cloud manufacturing service ecosystem based on multi-agent behavior simulation
×
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