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Research Papers

Two-Dimensional Modeling and System Identification of the Laser Metal Deposition Process

[+] Author and Article Information
Patrick M. Sammons

Mechanical and Aerospace Engineering Department,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: pmsd44@mst.edu

Douglas A. Bristow

Mechanical and Aerospace Engineering Department,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: dbristow@mst.edu

Robert G. Landers

Mechanical and Aerospace Engineering Department,
Missouri University of Science and Technology,
Rolla, MO 65409
e-mail: landersr@mst.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received May 27, 2017; final manuscript received September 6, 2018; published online October 19, 2018. Assoc. Editor: Beshah Ayalew.

J. Dyn. Sys., Meas., Control 141(2), 021012 (Oct 19, 2018) (10 pages) Paper No: DS-17-1279; doi: 10.1115/1.4041444 History: Received May 27, 2017; Revised September 06, 2018

Additive manufacturing (AM) processes fabricate parts by adding material in a layer-by-layer fashion. In order to enable closed-loop process control—a major hurdle in the adoption of most AM processes—compact models suitable for control design and for describing the layer-by-layer material addition process are needed. This paper proposes a two-dimensional modeling framework whereby the deposition of the current layer is affected by both in-layer and layer-to-layer dynamics, both of which are driven by the state of the previous layer. The proposed framework can be used to describe phenomena observed in AM processes such as layer rippling and large defects in laser metal deposition (LMD) processes. Further, the proposed framework can be used to create two-dimensional dynamic models for the analysis of layer-to-layer stability and as a foundation for the design of layer-to-layer controllers for AM processes. In the application to LMD, a two-dimensional linear–nonlinear–linear (LNL) repetitive process model is proposed that contains a linear dynamic component, which describes the dynamic evolution of the process from layer to layer, cascaded with a static nonlinear component cascaded with another linear dynamic component, which describes the dynamic evolution of the process within a given layer. A methodology, which leverages the two-dimensional LNL structure, for identifying the model process parameters is presented and validated with quantitative and qualitative experimental results.

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Figures

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Fig. 1

Structure fabricated with constant process parameters (laser power, 600 W; scan speed, 2.54 mm/s; powder flow rate, 3.73 g/min) in a LMD process. White lines are drawn to highlight individual layers.

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Fig. 2

Laser metal deposition process schematic

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Fig. 3

Schematic of nozzle-part interaction zone with substrate standoff dS, part standoff distance dP, bead height δh, and part height h

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Fig. 4

Linear–nonlinear–linear LMD process model schematic

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Fig. 5

Experimental results for identification of powder catchment function, fμ

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Fig. 6

Modeled and experimental powder catchment efficiencies

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Fig. 7

Schematic of witness mark and delayed deposition for alignment of commanded velocity and measured bead height for in-layer kernel identification

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Fig. 8

Commanded identification (top) and validation (bottom) PRBS velocity signals

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Fig. 9

Modeled and measured bead height signals for trial 1 identification (top) and error (bottom)

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Fig. 10

Modeled and measured bead height signals for trial 1 validation (top) and error (bottom)

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Fig. 11

Modeled and measured bead height signals for trial 2 identification (top) and error (bottom)

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Fig. 12

Modeled and measured bead height signals for trial 2 validation (top) and error (bottom)

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Fig. 13

Top view of remelt dynamic process identification substrates before and after deposition for trial 1 (top) and trial 2 (bottom)

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Fig. 14

Side view of remelt dynamic process identification substrates before and after deposition for trial 1 (top) and trial 2 (bottom)

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Fig. 15

Measured and modeled height due to remelt response and measured before deposition substrate height (top) and error (bottom) for trial 1

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Fig. 16

Measured and modeled height due to remelt response and measured before deposition substrate height (top) and error (bottom) for trial 2

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Fig. 17

Photograph of pocket feature and substrate used in model validation

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Fig. 18

Simulation height signals for every second layer for j = 0 to j = 26 (top) and photograph of experimental deposition (bottom) for first validation experiment

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Fig. 19

Simulated powder catchment efficiency for every fourth layer from j = 1 to j = 25

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Fig. 20

Simulation height signals for every second layer for j = 0 to j = 26 (top) and photograph of experimental deposition (bottom) for second validation experiment

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