LEArning to Print – towards data-driven real-time predictions for additive manufacturing

September 1, 2022
In Progress
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Laser Powder Bed Fusion (LPBF) is the dominating additive manufacturing process for high-performance metal components. This process builds 3D parts by repeatedly spreading and selectively melting a thin layer of powder. In each layer, the 2D cross section of the geometry is melted by a moving laser spot and then solidifies and bonds to the underlying layer. For a part of ~100 mm height, roughly 1000-2000 layers are needed. On each layer, the laser spot follows a certain trajectory, i.e., a set of hundreds of scan vectors that melt the powder on suitable points of the layer. The quality of the printed part strongly depends on its geometry and the scan strategy. Nowadays, since no method is available to find an optimal scan pattern for a given part geometry, scan strategies are found empirically. However, for a technology capable of producing single parts from digital data, it is prohibitive to count on experience and trial-and-error optimization. A plethora of numerical process models has been developed to simulate the process. When entire parts are simulated fast, simplified approaches are used, that cannot account for the scan strategy. High-fidelity (HF) simulations of the interaction of the laser and the material are feasible, but only for a limited number of scan vectors. They can neither be used to simulate entire layers, nor for entire 3D geometries. A source of information not yet exploited rigorously are data that can be monitored during the process. High resolution optical and thermal imaging as well as pyrometry allow to measure the temperature and the melt pool shape, and create several hundred GB of data per hour of printing. These data code the thermal history at each point of the top layer as a function of the scan vector sequence and laser settings, but they are subject to systematic measurement errors and noise. In this project, we address the research questions whether it is possible (1) to generate fast predictive process models from a combination of experimentally measurable process data and HF numerical simulations to enhance the resolution and accuracy of the experimental data and (2) to identify geometrical features and scan vector patterns in shapes to be printed that are prone to developing defects along with strategies to avoid such defects. We thus would like to develop an ‘LPBF learning process’, which is fully in line with the scope of the call.



SDSC Team:
Konstantinos Tatsis
Nathanaël Perraudin
Luis Salamanca
Fernando Perez-Cruz

PI | Partners:

ETH Zurich, Computational Mechanics Group:

  • Prof. Laura De Lorenzis
  • Dr. Manav Manav

More info



The Laser Powder Bed Fusion (LPBF) 3D printing process faces a significant challenge in accurately inputting the correct laser commands to achieve the desired object shape with the desired properties. While simulations exist, their computational complexity restricts their practical usability. The aim of this project it to enhance the LPBF 3D printing process with the use of data-driven surrogate models, which can replace the computationally expensive simulators for the prediction of the melting pool properties.

Proposed Approach / Solution

The proposed solution is based on the development of Deep Learning (DL) surrogate models using High Fidelity (HF) simulation data, as shown in Fig. 1. These models will be used for space and time predictions during the LPBF process, as well as for the optimization of the scanning process, with the aim of minimising thermal concentrations and delivering the target object with the desired microstructure properties.


One of the key promises of additive manufacturing in general, and LPBF specifically, is the possibility to manufacture new and highly individualized designs with fine and complicated structures, which have not been possible to manufacture traditionally at a rather low cost. As such, the integration of Deep Learning (DL) models in the loop will enhance the LPBF process and will enable the manufacturing of complicated designs.

Figure 1: Main project idea: Generation of a surrogate model from data generated from different parts.



Additional resources


  1. Yap, C. Y., Chua, C. K., Dong, Z. L., Liu, Z. H., Zhang, D. Q., Loh, L. E., & Sing, S. L. (2015). Review of selective laser melting: Materials and applications. Applied physics reviews, 2(4), 041101.
  2. Mozaffar, M., Liao, S., Xie, X., Saha, S., Park, C., Cao, J., & Gan, Z. (2022). Mechanistic artificial intelligence (mechanistic-AI) for modeling, design, and control of advanced manufacturing processes: Current state and perspectives. Journal of Materials Processing Technology, 302, 117485.


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