{"title":"卷积神经网络在增材制造中的应用综述","authors":"Mahsa Valizadeh, Sarah Jeannette Wolff","doi":"10.1016/j.aime.2022.100072","DOIUrl":null,"url":null,"abstract":"<div><p>Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.</p></div>","PeriodicalId":34573,"journal":{"name":"Advances in Industrial and Manufacturing Engineering","volume":"4 ","pages":"Article 100072"},"PeriodicalIF":3.9000,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666912922000046/pdfft?md5=b22444dfee1d6626ada2636849dbf787&pid=1-s2.0-S2666912922000046-main.pdf","citationCount":"15","resultStr":"{\"title\":\"Convolutional Neural Network applications in additive manufacturing: A review\",\"authors\":\"Mahsa Valizadeh, Sarah Jeannette Wolff\",\"doi\":\"10.1016/j.aime.2022.100072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.</p></div>\",\"PeriodicalId\":34573,\"journal\":{\"name\":\"Advances in Industrial and Manufacturing Engineering\",\"volume\":\"4 \",\"pages\":\"Article 100072\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2022-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666912922000046/pdfft?md5=b22444dfee1d6626ada2636849dbf787&pid=1-s2.0-S2666912922000046-main.pdf\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Industrial and Manufacturing Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666912922000046\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, INDUSTRIAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Industrial and Manufacturing Engineering","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666912922000046","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
Convolutional Neural Network applications in additive manufacturing: A review
Additive manufacturing (AM) is a promising digital manufacturing approach that has seen recent rapid growth. Despite the fast-growing nature of the technology, AM has been slowed by the qualification and certification due to various defects observed in printed parts. On the other hand, Convolutional Neural Networks (CNN), as a deep learning method, have received a great deal of attention over the last decade and demonstrated excellent performance in dealing with image data. Deep learning is a subset of machine learning and refers to any Artificial Neural Network with more than two hidden layers. This article provides a comprehensive overview of CNN’s application to several aspects of the AM process since the emergence of this field. This review also highlights current challenges and possible solutions to provide a horizon for future studies.