Modeling process-structure-property relationships for additive manufacturing

doi:10.1007/s11465-018-0505-y

Front. Mech. Eng.

2018, Vol. 13

Issue (4) : 482-492 https://doi.org/10.1007/s11465-018-0505-y

REVIEW ARTICLE

Modeling process-structure-property relationships for additive manufacturing

Wentao YAN, Stephen LIN, Orion L. KAFKA, Cheng YU, Zeliang LIU, Yanping LIAN, Sarah WOLFF, Jian CAO, Gregory J. WAGNER, Wing Kam LIU(

)

Department of Mechanical Engineering, Northwestern University, Evanston, IL 60201, USA

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Abstract

This paper presents our latest work on comprehensive modeling of process-structure-property relationships for additive manufacturing (AM) materials, including using data-mining techniques to close the cycle of design-predict-optimize. To illustrate the process-structure relationship, the multi-scale multi-physics process modeling starts from the micro-scale to establish a mechanistic heat source model, to the meso-scale models of individual powder particle evolution, and finally to the macro-scale model to simulate the fabrication process of a complex product. To link structure and properties, a high-efficiency mechanistic model, self-consistent clustering analyses, is developed to capture a variety of material response. The model incorporates factors such as voids, phase composition, inclusions, and grain structures, which are the differentiating features of AM metals. Furthermore, we propose data-mining as an effective solution for novel rapid design and optimization, which is motivated by the numerous influencing factors in the AM process. We believe this paper will provide a roadmap to advance AM fundamental understanding and guide the monitoring and advanced diagnostics of AM processing.

Keywords additive manufacturing thermal fluid flow data mining material modeling

Corresponding Author(s): Wing Kam LIU

Just Accepted Date: 15 January 2018 Online First Date: 26 February 2018 Issue Date: 31 July 2018

Cite this article:

Wentao YAN,Stephen LIN,Orion L. KAFKA, et al. Modeling process-structure-property relationships for additive manufacturing[J]. Front. Mech. Eng., 2018, 13(4): 482-492.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-018-0505-y
https://academic.hep.com.cn/fme/EN/Y2018/V13/I4/482

Fig.1 Basic principles of (a) LENS and (b) EBSM. In the LENS process, a continuous stream of powder is delivered to the focal point of a laser, at the melt pool; in EBSM a bed of powder is spread before being selectively melted. Figures reused with permissions from Refs. [²,³]

Fig.2 A data-driven multi-scale multi-physics modeling framework

Fig.3 Framework of multi-scale modeling to link process and structures Figure reused with permission from Ref. [³]

Fig.4 Micro-scale model of electron-atom interactions. (a) Schematic of the model, simulation results of an electron beam (60 kV) irradiating a Ti-6Al-4V substrate: (b) Penetration trajectories, (c) back-scattered electron energy intensity, and (d) absorbed energy distribution in the substrate. Figures reused with permission from Ref. [³]

Fig.5 Meso-scale modeling of EBSM processes from powder spreading to selective melting Figure reused with permission from Ref. [³]

Fig.6 (a) The schematic and (b) simulated fused zone of the 2-layer-2-track case. The cross section along the scan direction before and after manufacturing are (c) and (d) for y=0.4 mm, and (e) and (f) for y=0.6 mm, respectively

Fig.7 Schematic for coupling CAFE and KMC methods for modeling grain evolution. A CAFE model will resolve grain nucleation and growth during solidification. KMC methods will be used to further evolve the predicted microstructure from the CAFE model to reproduce the coarsening during cyclic reheating

Fig.8 Comparison of the predicted temperature history at a selected point between handbook-based property assumptions for specific heat and enthalpy change and those predicted by the CALPHAD method

Fig.9 (a) Synthetically generated columnar microstructure; (b) offline data: Plastic strain; (c) clustering results; (d) overall response and comparison to a DNS simulation with FEA; (e, f) contours of plastic strain in direction X, Z on the surface of the microstructural element as computed with SCA

Fig.10 (a) Voxel mesh of AM voids and (b) offline data for SCA simulation of a cluster of voids in SS316L; (c) clusters built from plastic strain; (d) fatigue indicating parameter computed with SCA

Fig.11 Schematic of data-driven material systems design. Figures reused with permission from Ref. [²⁸]

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