{"title":"Strategic selection of metal-cutting processes for thick steel plates using a hybrid decision methodology","authors":"Anita Kumari and Bappa Acherjee","doi":"10.1088/2631-8695/ad7939","DOIUrl":null,"url":null,"abstract":"Metal-cutting is indispensable in manufacturing, enabling precise component fabrication for industries like construction, automotive, aerospace, and shipbuilding, where accurate, efficient cutting of thick steel plates is crucial. This paper introduces a novel case study to strategically determine the optimal metal-cutting process for thick steel plates utilizing a hybrid MOORA-PSI approach. The use of the hybrid MOORA-PSI method simplifies decision-making by integrating weight assignment and ranking of alternatives. Five prominent metal-cutting processes, including oxygen flame, plasma arc, laser, wire EDM (wire electro-discharge machining), and abrasive water jet cutting, are commonly used for cutting thick steel plates, each with unique capabilities and limitations, and are considered potential alternatives. Eight evaluation criteria, capital cost, running cost, accuracy, edge quality, kerf width, maximum thickness, production flexibility, and production rate, are used to assess these metal-cutting alternatives. Wire EDM ranks as the optimal choice for cutting thick steel plates based on defined evaluation criteria, with laser cutting closely trailing, followed by oxygen flame, abrasive water jet, and plasma cutting successively. The results are validated by comparing them with those of other MCDM approaches and by conducting a Spearman’s rank correlation coefficient test, yielding consistent results. Additionally, sensitivity analysis, employing criteria weight exchange and dynamic variations in the decision-making matrix, further confirms the accuracy and reliability of the findings.","PeriodicalId":11753,"journal":{"name":"Engineering Research Express","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Research Express","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2631-8695/ad7939","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Metal-cutting is indispensable in manufacturing, enabling precise component fabrication for industries like construction, automotive, aerospace, and shipbuilding, where accurate, efficient cutting of thick steel plates is crucial. This paper introduces a novel case study to strategically determine the optimal metal-cutting process for thick steel plates utilizing a hybrid MOORA-PSI approach. The use of the hybrid MOORA-PSI method simplifies decision-making by integrating weight assignment and ranking of alternatives. Five prominent metal-cutting processes, including oxygen flame, plasma arc, laser, wire EDM (wire electro-discharge machining), and abrasive water jet cutting, are commonly used for cutting thick steel plates, each with unique capabilities and limitations, and are considered potential alternatives. Eight evaluation criteria, capital cost, running cost, accuracy, edge quality, kerf width, maximum thickness, production flexibility, and production rate, are used to assess these metal-cutting alternatives. Wire EDM ranks as the optimal choice for cutting thick steel plates based on defined evaluation criteria, with laser cutting closely trailing, followed by oxygen flame, abrasive water jet, and plasma cutting successively. The results are validated by comparing them with those of other MCDM approaches and by conducting a Spearman’s rank correlation coefficient test, yielding consistent results. Additionally, sensitivity analysis, employing criteria weight exchange and dynamic variations in the decision-making matrix, further confirms the accuracy and reliability of the findings.