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

IF 8.7 2区 工程技术 Q1 Mathematics Engineering with Computers Pub Date : 2024-08-20 DOI:10.1007/s00366-024-02029-4
Kun-Hao Huang, Nandana Menon, Amrita Basak
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引用次数: 0

摘要

激光定向能量沉积(L-DED)技术可以使用特定位置的材料制造近净成形零件、修复机器部件以及在现有零件上增加特征。然而,收集足够的 L-DED 实验数据来建立工艺图是一项挑战,尤其是在研究昂贵的材料时。尽管人们对通过数据驱动建模来绘制流程图很感兴趣,但很少有研究考虑在多种材料中重复使用知识,包括不确定性量化(UQ)。为解决这一问题,提出了基于高斯过程(GP)的知识转移方法。SS316L 和 IN718 的熔池数据分别用于模拟数据丰富和数据稀缺的条件。探索了三种途径:(i) 混合两种材料的数据来训练一个 GP 回归模型(混合输入模型);(ii) 基于关系的迁移学习(RB-TL)模型;(iii) 基于多保真度 GP 的迁移学习(MFGP-TL)模型。结果表明,在数据不足的条件下,混合输入模型优于基线模型或无迁移模型。与基线模型相比,RB-TL 模型在准确度和置信度方面都有普遍提高,同时在所有建议的模型中耗费的计算时间最少。MFGP-TL 模型的性能最佳,其误差和标准偏差仅为 RB-TL 模型的一半,但计算时间更长。最后,当把所提出的迁移学习模型用于从文献中获得的实验数据时,在 IN718 和 IN625 中,与基线模型相比分别提高了 22-31% 和 24-40%。因此,这项工作有助于在 L-DED 中基于数据和成本效益的 UQ 知识转移中重建流程图。
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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.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
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.
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