Anis Assad Neto , Bruna Sprea Carrijo , João Guilherme Romanzini Brock , Fernando Deschamps , Edson Pinheiro de Lima
{"title":"Digital twin-driven decision support system for opportunistic preventive maintenance scheduling in manufacturing","authors":"Anis Assad Neto , Bruna Sprea Carrijo , João Guilherme Romanzini Brock , Fernando Deschamps , Edson Pinheiro de Lima","doi":"10.1016/j.promfg.2021.10.060","DOIUrl":null,"url":null,"abstract":"<div><p>Preventive maintenance interventions are scheduled in industrial systems to prevent machine failures and breakdowns, which are associated with the incurrence of repair, unavailability, and quality-related costs. The execution of such interventions, however, generally represents a penalty to a manufacturing system’s production throughput due to machine interruption requirements. By the use of a digital twin architecture, we develop a decision support system to schedule preventive maintenance interventions with the aim of minimizing production throughout penalties via the exploitation of real-time opportunities such as supply shortages, momentary machine idleness or machine breakdowns. The decision support system has its application demonstrated by a case in a furniture manufacturer in the State of Santa Catarina – Brazil.</p></div>","PeriodicalId":91947,"journal":{"name":"Procedia manufacturing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2351978921002572/pdf?md5=38431966ee069d2e6d74e735d719d42d&pid=1-s2.0-S2351978921002572-main.pdf","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2351978921002572","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Preventive maintenance interventions are scheduled in industrial systems to prevent machine failures and breakdowns, which are associated with the incurrence of repair, unavailability, and quality-related costs. The execution of such interventions, however, generally represents a penalty to a manufacturing system’s production throughput due to machine interruption requirements. By the use of a digital twin architecture, we develop a decision support system to schedule preventive maintenance interventions with the aim of minimizing production throughout penalties via the exploitation of real-time opportunities such as supply shortages, momentary machine idleness or machine breakdowns. The decision support system has its application demonstrated by a case in a furniture manufacturer in the State of Santa Catarina – Brazil.