Invited Review Article: Metal-additive manufacturing - Modeling strategies for application-optimized designs

Abstract

Next generation, additively-manufactured metallic parts will be designed with application-optimized geometry, composition, and functionality. Manufacturers and researchers have investigated various techniques for increasing the reliability of the metal-AM process to create these components, however, understanding and manipulating the complex phenomena that occurs within the printed component during processing remains a formidable challenge-limiting the use of these unique design capabilities. Among various approaches, thermomechanical modeling has emerged as a technique for increasing the reliability of metal-AM processes, however, most literature is specialized and challenging to interpret for users unfamiliar with numerical modeling techniques. This review article highlights fundamental modeling strategies, considerations, and results, as well as validation techniques using experimental data. A discussion of emerging research areas where simulation will enhance the metal-AM optimization process is presented, as well as a potential modeling workflow for process optimization. This review is envisioned to provide an essential framework on modeling techniques to supplement the experimental optimization process.

Keywords: 3D Printing; finite element methods; metal additive manufacturing; residual stress; thermomechanical modeling.

Figure 1:

Classification of additive manufacturing processes and relevant applications. Powder bed fusion (PBF) and directed-energy-deposition (DED) are the two most widely-employed techniques for printing functional metal components. (A) Pure-Copper component manufactured via EBM with internal core structure [127] (B) Porous Nickel-Titanium shape-memory structure enabled via SLM [128] (C) LMD (or DED) repair of SLM-based Inconel 718 fuel burner [39] (D) Porous Ti6Al4V components enabled via SLM technique [129] (E) Structurally-optimized titanium-alloy component enabled via SLM [122] (F) Titanium hip stems manufactured via EBM [130].

Figure 2:

Common defects and failures during metal-AM processing. (A) Pores in EBM Ti6Al4V prior to hot isostatic pressing [41] (B) Cracking and delamination in SLM M2-High Speed Steel [131](C) Microcrack formation and pores induced in SLM Hastelloy X, a Ni-based superalloy [132](D) Pores induced from keyhole laser effect in PBF SS316 stainless steel [44]. (E) Keyhole formation in laser processing of Ti6Al4V [45].

Figure 3:

Schematics of the role of thermomechanical modeling in the manufacturing simulation and parameter optimization process. (A) Block diagram describing the process of parameter optimization for a specific application. (B) A proposed integrated approach to model the thermomechanical response and resulting properties of additively manufactured components on multiple scales [16].

Figure 4:

Thermomechanical modeling outline illustrating the main choices associated with creating a thermal model of AM.

Figure 5:

Dominant heat transfer modes in DED and PBF.

Figure 6:

Examples of thermomechanical modeling fundamentals. (A) A general mesh around the build substrate and the area where the heat source will be applied [58]. (B) A Gaussian distribution showing the increase in focus of the distribution for higher values of 𝑓 [1]. (C) A typical temporal diagram [69]. (D) Distribution of heat within a printed component on the first and third layers. Adapted from [57].

Figure 7:

General programming outline for a 2D, uncoupled-finite difference (FD) numerical analysis scheme for predicting temperatures while a component is printed. (A) Programming block diagram illustrating the logic structure that governs the numerical solver. (B) The computational domain on which the logic structure in (A) applies, where a rectangular mesh is employed. Both images adapted from [55].

Figure 8:

Strategies and outputs of thermomechanical models. (A) Material addition modeling strategies employed by various authors. (B) Stress distributions in a printed structure after the final layer has been printed [57]. (C) Residual stress profile along deposition path of a multi-material structure [133].

Figure 9:

Model validation experimental setups and design. (A) Thermo-mechanical validation station for DED of Ti6Al4V [70]. (B) Thermocouple and strain gauge locations for the setup in (A) [70]. (C) Typical temporal (temperature) curves illustrating a comparison between model and experimental data [76]. (D) Micrograph of an embedded Alumina thermocouple within a track of printed material, measuring temperatures during processing [88]. (E) Schematic of the hole drilling method with the resultant residual stresses shown [94].

Figure 10:

Examples of multi-material additive manufacturing. (A) Ti6Al4V cylinders with copper deposited on top, utilizing a multi-step EBM process. Adapted from [109]. (B) Stainless Steel (SS410) to Ti6Al4V direct-bonding microstructure showing crack perpendicular to interface, fabricated via DED [35]. (C) Bimetallic structure composed of magnetic Stainless Steel (SS430) and non-magnetic stainless steel (SS316), fabrication via DED [36]. (D) Stainless steel (304L) to Inconel 625 with gradient zone fabricated via DED. Adapted from [134].

Figure 11:

Topology optimization process for AM of metals using ABAQUS™ software package. [118].

Figure 12:

Design for manufacturing process workflow for incorporating simulation into the parameter optimization process for advanced components with variable geometry, composition, and functionality.