Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Published on Oct 1, 2021in Additive manufacturing7.002
· DOI :10.1016/J.ADDMA.2021.102089
D.R. Gunasegaram1
Estimated H-index: 1
(CSIRO: Commonwealth Scientific and Industrial Research Organisation),
A. Barnard1
Estimated H-index: 1
(ANU: Australian National University)
+ 4 AuthorsL. Ladani1
Estimated H-index: 1
(ASU: Arizona State University)
Abstract null null Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. We argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.
#1Grace C. Y. Peng (NIH: National Institutes of Health)H-Index: 8
#2Mark Alber (UCR: University of California, Riverside)H-Index: 41
Last. Ellen Kuhl (Stanford University)H-Index: 71
view all 12 authors...
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where c...
36 CitationsSource
#1Qiming Zhu (UIUC: University of Illinois at Urbana–Champaign)H-Index: 3
#2Zeliang Liu (Ansys)H-Index: 13
Last. Jinhui Yan (UIUC: University of Illinois at Urbana–Champaign)H-Index: 19
view all 3 authors...
The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling, which is an indispensable step to derive the process-structure-property relationship. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled data-sets (big data), which can be either obtained by experiments or first-principle simulati...
4 CitationsSource
#1Tarasankar DebroyH-Index: 78
#2T. MukherjeeH-Index: 18
Last. John O. MilewskiH-Index: 13
view all 5 authors...
Additive manufacturing enables the printing of metallic parts, such as customized implants for patients, durable single-crystal parts for use in harsh environments, and the printing of parts with site-specific chemical compositions and properties from 3D designs. However, the selection of alloys, printing processes and process variables results in an exceptional diversity of microstructures, properties and defects that affect the serviceability of the printed parts. Control of these attributes u...
23 CitationsSource
#1N.S. Johnson (Colorado School of Mines)H-Index: 1
#2P. S. Vulimiri (University of Pittsburgh)H-Index: 2
Last. Aaron P. Stebner (Colorado School of Mines)H-Index: 19
view all 7 authors...
Abstract In metals additive manufacturing (AM), materials and components are concurrently made in a single process as layers of metal are fabricated on top of each other in the near-final topology required for the end-use product. Consequently, tens to hundreds of materials and part design degrees of freedom must be simultaneously controlled and understood; hence, metals AM is a highly interdisciplinary technology that requires synchronized consideration of physics, chemistry, materials science,...
14 CitationsSource
#1Aniruddha Gaikwad (NU: University of Nebraska–Lincoln)H-Index: 4
#2Brian Giera (LLNL: Lawrence Livermore National Laboratory)H-Index: 8
Last. Prahalada Rao (NU: University of Nebraska–Lincoln)H-Index: 20
view all 6 authors...
Abstract Laser Powder Bed Fusion (LPBF) is the predominant metal additive manufacturing technique that benefits from a significant body of academic study and industrial investment given its ability to create complex geometry parts. Despite LPBF’s widespread use, there still exists a need for process monitoring to ensure reliable part production and reduce post-build quality assessments. Towards this end, we develop and evaluate machine learning-based predictive models using height map-derived qu...
4 CitationsSource
#1Li ZhangH-Index: 19
#1Li ZhangH-Index: 65
view all 8 authors...
6 CitationsSource
#1Lening Wang (VT: Virginia Tech)H-Index: 3
#2Xiaoyu Chen (VT: Virginia Tech)H-Index: 3
Last. Ran Jin (VT: Virginia Tech)H-Index: 15
view all 5 authors...
Abstract Finite element analysis (FEA) has been widely adopted to identify potential defects in additive manufacturing (AM) processes. For personalized product realization, it is necessary to validate a number of heterogeneous product and process designs before or during manufacturing by using FEA. Multi-fidelity FEA simulations can be readily implemented with different capabilities in terms of simulation accuracy. However, due to its complexity, high-fidelity FEA simulation is time-consuming an...
5 CitationsSource
#1Daniel Powell (Lancaster University)H-Index: 1
#2Allan Rennie (Lancaster University)H-Index: 11
Last. Neil Burns (Lancaster University)H-Index: 3
view all 4 authors...
Abstract To ensure the financial viability of powder-based additive manufacturing technologies, the recycling of powders is common practice. This paper shows the lifecycle of metal powder in additive manufacturing, investigating powder manufacture, powder usage, mechanisms of powder degradation and the usage of end-of-life powder. Degradation of powders resulting from repeated reuses was found to be a widespread problem; components produced from heavily reused powders are typically of a lower qu...
16 CitationsSource
#1Hermann BaumgartlH-Index: 7
#2Josef TomasH-Index: 3
Last. Markus MerkelH-Index: 13
view all 4 authors...
Additive manufacturing of metal components with laser-powder bed fusion is a very complex process, since powder has to be melted and cooled in each layer to produce a part. Many parameters influence the printing process; however, defects resulting from suboptimal parameter settings are usually detected after the process. To detect these defects during the printing, different process monitoring techniques such as melt pool monitoring or off-axis infrared monitoring have been proposed. In this wor...
27 CitationsSource
Metal additive manufacturing (AM) provides a platform for microstructure optimization via process control, but establishing a quantitative processing-microstructure linkage necessitates an efficient scheme for microstructure representation and regeneration. Here, we present a deep learning framework to quantitatively analyze the microstructural variations of metals fabricated by AM under different processing conditions. The principal microstructural descriptors are extracted directly from the el...
3 CitationsSource
Cited By2
#1Shah ZebH-Index: 3
#2Aamir MahmoodH-Index: 12
Last. Mohsen GuizaniH-Index: 82
view all 6 authors...
By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry~4.0 promotes integrating cyber-physical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects...
#1D.R. Gunasegaram (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 1
#1Dayalan Gunasegaram (CSIRO: Commonwealth Scientific and Industrial Research Organisation)H-Index: 11
Last. Tarasankar Debroy (PSU: Pennsylvania State University)H-Index: 78
view all 4 authors...