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金属基增材制造过程中的多尺度传输现象综述

Review on Multiscale Transport Phenomena in the Metal-Based Additive Manufacturing Process.

作者信息

Liu Miao, Liu Zhongqiu, Li Baokuan, Gan Yong

机构信息

Key Laboratory for Ecological Metallurgy of Multimetallic Mineral (Ministry of Education), Northeastern University, Shenyang, China.

School of Metallurgy, Northeastern University, Shenyang, China.

出版信息

3D Print Addit Manuf. 2024 Aug 20;11(4):1471-1494. doi: 10.1089/3dp.2023.0051. eCollection 2024 Aug.

Abstract

Metal-based additive manufacturing (MAM) method with high freedom and special fabricate technology has presented great universality in the aerospace and biomedicine field. However, a wide range of process parameters in the MAM method challenge the experimental study on the formation and evolution of defects. The numerical simulation presents its excellent accuracy and economy in predicting the evolution of multiphysics phenomena and was hence widely applied. In the current review, the available MAM methods with the fundamental phenomena were reviewed. Based on scales, numerical approaches divided into three categories were discussed and focused on their main prediction objectives and strengths or weaknesses of all the scales. To display the prediction results closer to real physical phenomena, advanced multiscale models coupled with various single-scale models are provided. The high prediction accuracy and computational efficiency enable better parameter control and defect avoidance. As a supplement and development to the physical-driven model, the data-driven model provides a new perspective on MAM methods. Based on the database generated by the physical-driven model and experiment, the data-driven models without calibration of input parameters are shown. In addition, this review discussed the development direction of numerical simulation, aiming to provide a reference for technical research in this field.

摘要

基于金属的增材制造(MAM)方法具有高度的自由度和特殊的制造技术,在航空航天和生物医学领域展现出了极大的通用性。然而,MAM方法中广泛的工艺参数对缺陷形成和演变的实验研究提出了挑战。数值模拟在预测多物理现象演变方面具有出色的准确性和经济性,因此得到了广泛应用。在当前的综述中,对现有的具有基本现象的MAM方法进行了综述。基于尺度,讨论了分为三类的数值方法,并重点关注了它们在所有尺度上的主要预测目标和优缺点。为了使预测结果更接近真实物理现象,提供了与各种单尺度模型相结合的先进多尺度模型。高预测精度和计算效率有助于更好地控制参数和避免缺陷。作为对物理驱动模型的补充和发展,数据驱动模型为MAM方法提供了新的视角。基于由物理驱动模型和实验生成的数据库,展示了无需输入参数校准的数据驱动模型。此外,本综述讨论了数值模拟的发展方向,旨在为该领域的技术研究提供参考。

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