Muhammad Arif Mahmood, Asif Ur Rehman, Marwan Khraisheh
{"title":"关于 AISI 316L 不锈钢增材制造中可印刷性地图智能框架的开发。","authors":"Muhammad Arif Mahmood, Asif Ur Rehman, Marwan Khraisheh","doi":"10.1089/3dp.2023.0016","DOIUrl":null,"url":null,"abstract":"<p><p>In this work, we propose a methodology to develop printability maps for the laser powder bed fusion of AISI 316L stainless steel. Regions in the process space associated with different defect types, including lack of fusion, balling, and keyhole formation, have been considered as a melt pool geometry function, determined using a finite element method model containing temperature-dependent thermophysical properties. Experiments were performed to validate the printability maps, showing a reliable correlation between experiments and simulations. The validated simulation model was then applied to collect the data by varying laser scanning speed, laser power, powder layer thickness, and powder bed preheating temperature. Following this, the collected data were used to train and test the adaptive neuro-fuzzy interference system (ANFIS)-based machine learning model. The validated ANFIS model was used to develop printability maps by correlating the melt pool characteristics to the defect types. The smart printability maps produced by the proposed methodology can be used to identify the processing window to attain defects-free components, thus attaining dense parts.</p>","PeriodicalId":54341,"journal":{"name":"3D Printing and Additive Manufacturing","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442379/pdf/","citationCount":"0","resultStr":"{\"title\":\"On the Development of Smart Framework for Printability Maps in Additive Manufacturing of AISI 316L Stainless Steel.\",\"authors\":\"Muhammad Arif Mahmood, Asif Ur Rehman, Marwan Khraisheh\",\"doi\":\"10.1089/3dp.2023.0016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this work, we propose a methodology to develop printability maps for the laser powder bed fusion of AISI 316L stainless steel. Regions in the process space associated with different defect types, including lack of fusion, balling, and keyhole formation, have been considered as a melt pool geometry function, determined using a finite element method model containing temperature-dependent thermophysical properties. Experiments were performed to validate the printability maps, showing a reliable correlation between experiments and simulations. The validated simulation model was then applied to collect the data by varying laser scanning speed, laser power, powder layer thickness, and powder bed preheating temperature. Following this, the collected data were used to train and test the adaptive neuro-fuzzy interference system (ANFIS)-based machine learning model. The validated ANFIS model was used to develop printability maps by correlating the melt pool characteristics to the defect types. The smart printability maps produced by the proposed methodology can be used to identify the processing window to attain defects-free components, thus attaining dense parts.</p>\",\"PeriodicalId\":54341,\"journal\":{\"name\":\"3D Printing and Additive Manufacturing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11442379/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"3D Printing and Additive Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1089/3dp.2023.0016\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/6/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MANUFACTURING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"3D Printing and Additive Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1089/3dp.2023.0016","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"ENGINEERING, MANUFACTURING","Score":null,"Total":0}
On the Development of Smart Framework for Printability Maps in Additive Manufacturing of AISI 316L Stainless Steel.
In this work, we propose a methodology to develop printability maps for the laser powder bed fusion of AISI 316L stainless steel. Regions in the process space associated with different defect types, including lack of fusion, balling, and keyhole formation, have been considered as a melt pool geometry function, determined using a finite element method model containing temperature-dependent thermophysical properties. Experiments were performed to validate the printability maps, showing a reliable correlation between experiments and simulations. The validated simulation model was then applied to collect the data by varying laser scanning speed, laser power, powder layer thickness, and powder bed preheating temperature. Following this, the collected data were used to train and test the adaptive neuro-fuzzy interference system (ANFIS)-based machine learning model. The validated ANFIS model was used to develop printability maps by correlating the melt pool characteristics to the defect types. The smart printability maps produced by the proposed methodology can be used to identify the processing window to attain defects-free components, thus attaining dense parts.
期刊介绍:
3D Printing and Additive Manufacturing is a peer-reviewed journal that provides a forum for world-class research in additive manufacturing and related technologies. The Journal explores emerging challenges and opportunities ranging from new developments of processes and materials, to new simulation and design tools, and informative applications and case studies. Novel applications in new areas, such as medicine, education, bio-printing, food printing, art and architecture, are also encouraged.
The Journal addresses the important questions surrounding this powerful and growing field, including issues in policy and law, intellectual property, data standards, safety and liability, environmental impact, social, economic, and humanitarian implications, and emerging business models at the industrial and consumer scales.