{"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.