Paolo Catti , Michalis Ntoulmperis , Vittoria Medici , Milena Martarelli , Nicola Paone , Wilhelm van de Kamp , Nikolaos Nikolakis , Kosmas Alexopoulos
{"title":"通过金属棒的温度曲线分析进行生产质量控制:钢铁部件使用案例","authors":"Paolo Catti , Michalis Ntoulmperis , Vittoria Medici , Milena Martarelli , Nicola Paone , Wilhelm van de Kamp , Nikolaos Nikolakis , Kosmas Alexopoulos","doi":"10.1016/j.procir.2024.10.036","DOIUrl":null,"url":null,"abstract":"<div><div>Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.</div></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":"","citationCount":"0","resultStr":"{\"title\":\"Quality control in manufacturing through temperature profile analysis of metal bars: A steel parts use case\",\"authors\":\"Paolo Catti , Michalis Ntoulmperis , Vittoria Medici , Milena Martarelli , Nicola Paone , Wilhelm van de Kamp , Nikolaos Nikolakis , Kosmas Alexopoulos\",\"doi\":\"10.1016/j.procir.2024.10.036\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.</div></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\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Procedia CIRP\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2212827124011727\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827124011727","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quality control in manufacturing through temperature profile analysis of metal bars: A steel parts use case
Non-uniform heating during metal bar hot forming may impact its straightness. In this study, an infrared non-destructive inspection system is proposed to acquire steel temperature profiles in runtime which should correlate to straightness deviations. Additionally, a machine learning algorithm detects outliers to identify oxides on the metal, which in turn is correlated to process parameters. This allows for proactive temperature adjustment to mitigate the risk based on historical profiles. The proposed approach has been tested in a use case coming from the steel industry.