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
Cite this article:
LIU Zhuangzhuang, DING Minglu, XIE Jianxin. Advancements in Digital Manufacturing for Metal 3D Printing. Acta Metall Sin, 2024, 60(5): 569-584.
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.
Fund: National Key Research and Development Program of China(2022YFB4600302);National Natural Science Foundation of China(52090041);National Natural Science Foundation of China(52104368)
Corresponding Authors:
LIU Zhuangzhuang, associate professor, Tel: (010)62332253, E-mail: liuzhuangzhuang@ustb.edu.cn
Fig.1 Schematic of laser powder bed fusion (L-PBF) process[13]
Type
Object
Advantage
Disadvantage
Profilometer
Profiling of powder bed
Easy installation
High cost
Infrared camera
Thermal radiation of molten pool
Intuitive results with rich information retrieval
High resolution, high equipment cost
Pyrometer
Thermal radiation of molten pool
High accuracy and rapid response
High cost
High-speed camera
Molten pool morphology, thermal images, and geometric dimensions
Lower cost and extended measurement range
Optical filters required
Acoustic emission
Defects
High sensitivity, nondestructive monitoring, and low cost
Lower accuracy in high-noise conditions
Synchrotron X-ray
Dynamic behavior of molten pool
Real-time observation
High cost, complex equipment, limited commercial viability
Spectrometer
Spattering
Wide measurement range
High cost
Table 1 Common sensors for in-situ monitoring in metal 3D printing
Fig.2 Industrial camera monitoring powder bed spreading quality in L-PBF (Green represents part outline, other colors indicate various defects)[48]
Fig.3 Schematic of profilometer installed inside L-PBF building chamber[49]
Fig.4 Online monitoring of surface temperature distribution (a) and detected defects (b) in L-PBF components[50]
Fig.5 Schematic of colorimetric thermometer measuring surface radiative intensity in the laser-powder interaction zone during the L-PBF process[51]
Fig.6 Cross-sectional image of the sample and its corresponding relationship with acoustic emission waveforms in a single scan experiment of the L-PBF process[53]
Fig.7 Molten pool monitoring device (a) and schematics of two-dimensional signal processing for molten pool radiative intensity distribution (b-e)[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
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
Table 2 Summary of common numerical simulation models in metal additive manufacturing[58]
Fig.8 Original radiation data (a), images of the molten pool and spatters used for temperature feature extraction (b, c), and binarized images of the molten pool and spatters used for shape feature extraction (d, e)[77] (Red arrows refer to the Euclidean distances (d) measured from the center of the meltpool to edge pixels and ejecta pixels)
Fig.9 High-density parameters predicted by machine learning model[79]
Fig.10 Schematic of the closed-loop control system[85] (FLC—fuzzy logic controller, DNN—deep neural network, DT—decision tree, LR—logistic regression, RF—random forest, SVM—support vector machine, ML—machine learning)
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