金属<strong>3D</strong>打印数字化制造研究进展

doi:10.11900/0412.1961.2023.00416

金属学报

2024, Vol. 60

Issue (5): 569-584 DOI: 10.11900/0412.1961.2023.00416

综述

本期目录 | 过刊浏览

金属3D打印数字化制造研究进展

刘壮壮¹^,²^,³(

), 丁明路¹^,², 谢建新¹^,²^,³

1 北京科技大学新材料技术研究院材料先进制备技术教育部重点实验室北京 100083
2 北京科技大学新材料技术研究院现代交通金属材料与加工技术北京实验室北京 100083
3 北京科技大学新材料技术研究院北京材料基因工程高精尖创新中心北京 100083

Advancements in Digital Manufacturing for Metal 3D Printing

LIU Zhuangzhuang¹^,²^,³(

), DING Minglu¹^,², XIE Jianxin¹^,²^,³

1 Key Laboratory for Advanced Materials Processing (MOE), Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
2 Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
3 Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China

引用本文:

刘壮壮, 丁明路, 谢建新. 金属3D打印数字化制造研究进展[J]. 金属学报, 2024, 60(5): 569-584.
Zhuangzhuang LIU, Minglu DING, Jianxin XIE. Advancements in Digital Manufacturing for Metal 3D Printing[J]. Acta Metall Sin, 2024, 60(5): 569-584.

全文: PDF(2427 KB) HTML

摘要:

数字化制造将传统的制造过程转化为数字模型，实现对整个制造流程的智能控制，进而快速生产出满足要求的产品。金属3D打印是一个具有多物理场强耦合作用、过程强时变扰动、内禀关系非线性以及多变量与多目标等特点的复杂物理过程，实现金属3D打印全流程的数字化控制，有望解决当前3D打印零件质量一致性和性能稳定性低的瓶颈问题，推动高质量3D打印技术的发展。本文首先分析了金属3D打印的技术特征和数字化制造的基本内涵，随后从3D打印过程数据在线监测、数字化仿真、物理与信息系统交互3个方面综述了金属3D打印数字化制造的研究进展，最后讨论了数字化制造在金属3D打印领域的未来研究重点，展望了发展前景。

关键词 ： 3D打印, 增材制造, 机器学习, 数字化制造

Abstract：

Digital manufacturing revolutionizes conventional manufacturing processes into digital models, enabling intelligent control over the entire production process to rapidly create products tailored to specific requirements. Metal three-dimensional (3D) printing, a complex physical process, is characterized by strong multiphysics interactions, highly time-varying disturbances, intrinsic nonlinear relationships, and multiple variables and objectives. Achieving full-process digital control in metal 3D printing has the potential to overcome current bottlenecks, such as inconsistent part quality and unstable performance, thereby advancing high-quality 3D printing technology. This work investigates metal 3D printing characteristics and fundamental digital manufacturing principles. It subsequently provides an overview of research progress in the digital manufacturing of metal 3D printing, encompassing three critical aspects: online monitoring of the 3D printing process, digital simulation, and the interaction between physical and information systems. Finally, the work discusses the future research focus of digital manufacturing in metal 3D printing, offering insights into its development prospects.

Key words： 3D printing additive manufacturing machine learning digital manufacturing

收稿日期: 2023-10-18

ZTFLH:

TG14

基金资助:国家重点研发计划项目(2022YFB4600302);国家自然科学基金项目(52090041);国家自然科学基金项目(52104368)

通讯作者: 刘壮壮，liuzhuangzhuang@ustb.edu.cn，主要从事增材制造数字化与过程形性智能调控研究

Corresponding author: LIU Zhuangzhuang, associate professor, Tel: (010)62332253, E-mail: liuzhuangzhuang@ustb.edu.cn

作者简介: 刘壮壮，男，1987年生，副教授，博士

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图1 激光粉末床熔融(L-PBF)工艺示意图[13]

表1 金属3D打印过程在线监测常用传感器

图2 工业相机监测L-PBF粉末床铺粉质量(绿色表示零件轮廓，其他颜色代表不同缺陷)[48]

图3 L-PBF成型仓内安装的激光轮廓仪示意图[49]

图4 L-PBF成形件表面温度分布在线监控和识别到的缺陷[50]

图5 比色测温仪测量L-PBF过程激光与粉末作用区域表面辐射强度示意图[51]

图6 L-PBF过程单道次扫描实验中样品的截面图像及与声发射传感器波形的对应关系[53]

图7 熔池监测设备及熔池辐射强度分布二维信号处理过程示意图[54]

Purpose	Model	Feature	Application
Calculation of heat, mass, and momentum transfer	Part scale heat conduction model	Fourier heat conduction equation is solved either analytically in 1D or 2D or numerically in 3D	Temperature fields; fusion zone geometry; cooling rates
	Part scale heat transfer and fluid flow	Solves 3D transient conservation equations of mass, momentum, and energy	Temperature and velocity fields; fusion zone geometry; cooling rates; solidification parameters; lack of fusion
	Part scale volume of fluid and level set methods	Tracks the free surface of the molten pool; computationally intensive; accumulates errors and the calculated deposit shape and size often do not agree well with experiments	3D deposit geometry; temperature and velocity fields; cooling rates; solidification parameters
	Powder-scale models	Involves free surface boundary conditions treating thermodynamics, surface tension, phase transitions, and wetting; small timescale and length scale, computationally intensive	Temperature and velocity fields; track geometry; lack of fusion; spatter; surface roughness
Microstructure, nucleation, and grain growth prediction	TTT-based, CCT-based, and JMA-based models	Based on phase transformation kinetics during cooling; widely used for simulating phase transformations in steels and common alloys; high computational efficiency	Solid-state phase transformation kinetics
	Monte Carlo method	A probabilistic approach of grain orientation change; provides grain size distribution with time; high computational efficiency	Grain growth; solidification structure; texture
	Cellular automata	Simulates growth of grain and subgrain structure during solidification; medium accuracy and computational efficiency	Solidification structure; grain growth; texture
	Phase field model	Simulates microstructural features and properties by calculating an order parameter based on free energy that represents the state of the entire microstructure; computationally intensive	Nucleation; grain growth; evolution of phases; precipitate formation; solid-state phase transformation
Calculation of residual stresses and distortion	FEA-based thermomechanical models	Calculation of residual stresses and distortion FEA-based thermomechanical models solves 3D constitutive equations considering elastic, plastic, and thermal behavior; many software packages exist, and these are easy to implement and can handle intricate geometries; adaptive grid and inherent strain method are often used to increase calculation speed	Evolution of residual stress; strains; distortion; delamination; warping

表2 金属增材制造常用数值模拟总结[58]

图8 高温计监测L-PBF成形过程中熔池的辐射数据[77]

图9 机器学习模型预测的高致密度参数窗口[79]

图10 熔融沉积(FDM)多参数闭环控制系统示意图[85]

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