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A benchmark of approaches for closed loop control of melt pool shape in DED

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Abstract

The industrialization of additive manufacturing strongly relies on the robustness of the process. In particular, the more complex Directed Energy Deposition (DED) processes offer great flexibility and higher build-up rates on the one hand, but on the other hand they pose challenges until a part can be printed with the desired properties. This paper adopts and compares different approaches, i.e. linear, sliding mode and model-based controller, for online process regulation of track height and width using a novel industrial sensor setup. Based on single track experiments, control parameters are determined and results of the closed loop analyzed. As a result, the linear controller shows the best performance and robustness. The performance for the track width can even be improved by a Linear Quadratic Gaussian (LQG) controller. Finally, the performance of the linear control strategy is tested with a complex motor cover part. Additionally, a digital twin representation shows the spatial representation of the process parameters and region of interests where thresholds or predefined process rules are violated.

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Abbreviations

DED:

Directed Energy Deposition

CAD:

Computer Aided Design

AM:

additive manufacturing

CNC:

Computer Numerical Control

CMOS:

Complementary Metal Oxide Semiconductor

NC:

Numerical Control

ILC:

Iterative Learning Control

NARX:

Nonlinear Autoregressive Network with Exogenous Inputs

MLP:

Multilayer Perceptron

PI:

Proportional Integral

PID:

Proportional Integral Derivative

SISO:

Single Input, Single Output

MIMO:

Multiple Input, Multiple Output

IDM\(^{{\circledR }}\) :

In-Process Depth Meter\(^{{\circledR }}\)

px:

pixel

FPS:

frames per second

px:

pixel

SLD:

Superluminescent Diode

OP:

operating point

I:

integral

TF:

transfer function

LQG:

Linear Quadratic Gaussian

LQR:

Linear Quadratic Regulator

SVD:

Singular Value Decomposition

MSE:

Mean Square Error

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Funding

This work was funded by the Siemens Aktiengesellschaft (Siemens AG).

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Authors and Affiliations

  1. Siemens AG, Munich, Germany

    Daniel Regulin & Raffaele Barucci

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  1. Daniel Regulin
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  2. Raffaele Barucci
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Contributions

Daniel Regulin (conceptualization, investigation, methodology, funding acquisition); Raffaele Barucci (controller design, parametrization and validation)

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Correspondence to Daniel Regulin.

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Regulin, D., Barucci, R. A benchmark of approaches for closed loop control of melt pool shape in DED. Int J Adv Manuf Technol 126, 829–843 (2023). https://doi.org/10.1007/s00170-023-11042-8

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