{"title":"通过高斯过程回归在激光定向能量沉积过程中传递多种材料之间的熔池知识","authors":"Kun-Hao Huang, Nandana Menon, Amrita Basak","doi":"10.1007/s00366-024-02029-4","DOIUrl":null,"url":null,"abstract":"<p>Laser-directed energy deposition (L-DED) enables the creation of near-net-shape parts with location-specific materials, repair of machine components, and addition of features to existing parts. However, gathering sufficient experimental L-DED data to establish process maps is challenging especially when expensive materials are being investigated. Despite the interest in data-driven modeling for developing such maps, few studies have considered reusing knowledge across multiple materials including uncertainty quantification (UQ). To address this, knowledge transfer methods based on Gaussian process (GP) are proposed. Melt pool data for SS316L and IN718 are used to emulate data-rich and data-scarce conditions, respectively. Three avenues are explored: (i) mixing the data of both materials to train a single GP regression model (the mixed-input model), (ii) relation-based transfer learning (RB-TL) model, and (iii) multi-fidelity GP-based transfer learning (MFGP-TL) model. Results show that the mixed-input model outperforms the baseline or no-transfer model under data-deficient conditions. Compared to the baseline model, the RB-TL model exhibits a general improvement in accuracy and confidence while consuming the least computation time among all proposed models. The MFGP-TL model achieves the best performance, which is only half the error and standard deviation observed for the RB-TL model, albeit resulting in longer computation times. Finally, the proposed transfer learning models, when used on experimental data obtained from the literature, show 22–31% and 24–40% improvement over the baseline model for IN718 and IN625, respectively. This work, therefore, facilitates data- and cost-effective UQ-based knowledge transfer in reconstructing process maps in L-DED.</p>","PeriodicalId":11696,"journal":{"name":"Engineering with Computers","volume":null,"pages":null},"PeriodicalIF":8.7000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Transferring melt pool knowledge between multiple materials in laser-directed energy deposition via Gaussian process regression\",\"authors\":\"Kun-Hao Huang, Nandana Menon, Amrita Basak\",\"doi\":\"10.1007/s00366-024-02029-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Laser-directed energy deposition (L-DED) enables the creation of near-net-shape parts with location-specific materials, repair of machine components, and addition of features to existing parts. However, gathering sufficient experimental L-DED data to establish process maps is challenging especially when expensive materials are being investigated. Despite the interest in data-driven modeling for developing such maps, few studies have considered reusing knowledge across multiple materials including uncertainty quantification (UQ). To address this, knowledge transfer methods based on Gaussian process (GP) are proposed. Melt pool data for SS316L and IN718 are used to emulate data-rich and data-scarce conditions, respectively. Three avenues are explored: (i) mixing the data of both materials to train a single GP regression model (the mixed-input model), (ii) relation-based transfer learning (RB-TL) model, and (iii) multi-fidelity GP-based transfer learning (MFGP-TL) model. Results show that the mixed-input model outperforms the baseline or no-transfer model under data-deficient conditions. Compared to the baseline model, the RB-TL model exhibits a general improvement in accuracy and confidence while consuming the least computation time among all proposed models. The MFGP-TL model achieves the best performance, which is only half the error and standard deviation observed for the RB-TL model, albeit resulting in longer computation times. Finally, the proposed transfer learning models, when used on experimental data obtained from the literature, show 22–31% and 24–40% improvement over the baseline model for IN718 and IN625, respectively. This work, therefore, facilitates data- and cost-effective UQ-based knowledge transfer in reconstructing process maps in L-DED.</p>\",\"PeriodicalId\":11696,\"journal\":{\"name\":\"Engineering with Computers\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.7000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering with Computers\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00366-024-02029-4\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Mathematics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering with Computers","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00366-024-02029-4","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
Transferring melt pool knowledge between multiple materials in laser-directed energy deposition via Gaussian process regression
Laser-directed energy deposition (L-DED) enables the creation of near-net-shape parts with location-specific materials, repair of machine components, and addition of features to existing parts. However, gathering sufficient experimental L-DED data to establish process maps is challenging especially when expensive materials are being investigated. Despite the interest in data-driven modeling for developing such maps, few studies have considered reusing knowledge across multiple materials including uncertainty quantification (UQ). To address this, knowledge transfer methods based on Gaussian process (GP) are proposed. Melt pool data for SS316L and IN718 are used to emulate data-rich and data-scarce conditions, respectively. Three avenues are explored: (i) mixing the data of both materials to train a single GP regression model (the mixed-input model), (ii) relation-based transfer learning (RB-TL) model, and (iii) multi-fidelity GP-based transfer learning (MFGP-TL) model. Results show that the mixed-input model outperforms the baseline or no-transfer model under data-deficient conditions. Compared to the baseline model, the RB-TL model exhibits a general improvement in accuracy and confidence while consuming the least computation time among all proposed models. The MFGP-TL model achieves the best performance, which is only half the error and standard deviation observed for the RB-TL model, albeit resulting in longer computation times. Finally, the proposed transfer learning models, when used on experimental data obtained from the literature, show 22–31% and 24–40% improvement over the baseline model for IN718 and IN625, respectively. This work, therefore, facilitates data- and cost-effective UQ-based knowledge transfer in reconstructing process maps in L-DED.
期刊介绍:
Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.