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Research Papers

Prescriptive Data-Analytical Modeling of Laser Powder Bed Fusion Processes for Accuracy Improvement

[+] Author and Article Information
He Luan

Epstein Department of Industrial and
Systems Engineering,
University of Southern California,
Los Angeles, CA 90089

Marco Grasso, Bianca M. Colosimo

Department of Mechanical Engineering,
Politecnico di Milano,
Milano 20156, Italy

Qiang Huang

Epstein Department of Industrial and
Systems Engineering,
University of Southern California,
Los Angeles, CA 90089
e-mail: qiang.huang@usc.edu

Manuscript received March 20, 2018; final manuscript received October 1, 2018; published online November 8, 2018. Assoc. Editor: Sam Anand.

J. Manuf. Sci. Eng 141(1), 011008 (Nov 08, 2018) (13 pages) Paper No: MANU-18-1169; doi: 10.1115/1.4041709 History: Received March 20, 2018; Revised October 01, 2018

Laser powder bed fusion (LPBF) has the ability to produce three-dimensional lightweight metal parts with complex shapes. Extensive investigations have been conducted to tackle build accuracy problems caused by shape complexity. For metal parts with stringent requirements, surface roughness, laser beam positioning error, and part location effects can all affect the shape accuracy of LPBF built products. This study develops a data-driven predictive approach as a promising solution for geometric accuracy improvement in LPBF processes. To address the shape complexity issue, a prescriptive modeling approach is adopted to minimize geometrical deviations of built products through compensating computer aided design models, as opposed to changing process parameters. It allows us to predict and control a wide range of shapes starting from a limited set of measurements on basic benchmark geometries. An error decomposition and compensation scheme is developed to decouple the influence from different error components and to reduce the shape deviations caused by part geometrical deviation, laser beam positioning error, and other location effects simultaneously via an integrated modeling and compensation framework. Experimentation and data collection are conducted to investigate error sources and to validate the developed modeling and accuracy control methods.

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Figures

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Fig. 1

Flow chart of proposed strategy for accuracy improvement: (a) modeling procedure and (b) implementation procedure

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Fig. 2

Design of shape deviation experimentation on a single plate (unit: mm)

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Fig. 3

Deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of three cylinders by cylindrical basis model g1: (a) 20 mm cylinder, (b) 10 mm cylinder, and (c) 5 mm cylinder

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Fig. 4

Deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of the polyhedron shapes by freeform model: (a) 15 mm pentagon and (b) 132 mm2

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Fig. 5

Deviation profile (dots) and prediction (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of 12 mm freeform shape

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Fig. 6

Deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of three cylinders by freeform model: (a) 20 mm cylinder, (b) 10 mm cylinder, and (c) 5 mm cylinder

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Fig. 7

Measured surface roughness profile for cylinder with r0 = 5 mm

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Fig. 8

Illustration of roughness influence in PCS

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Fig. 9

Actual deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of three cylinders by cylindrical basis model g1 while filtering surface roughness: (a) 20 mm cylinder, (b) 10 mm cylinder, and (c) 5 mm cylinder

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Fig. 10

Actual deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of the polyhedron shapes by freeform model while filtering surface roughness: (a) 15 mm pentagon and (b) 132 mm2 square

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Fig. 11

Actual deviation profile (dots) and prediction (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of 12 mm freeform shape while filtering surface roughness

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Fig. 12

Actual deviation (dots) and prediction profiles (solid lines denote posterior means, and dashed lines denote the 2.5% and 97.5% posterior quantiles) of three cylinders by freeform model while filtering surface roughness: (a) 20 mm cylinder, (b) 10 mm cylinder, and (c) 5 mm cylinder

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Fig. 13

Experimental grid with 9 × 9 small cylinders in location effect experimentation

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Fig. 14

Pattern of the laser beam positioning error. Each point denotes the desired position of cylinder center. Measured and predicted positioning errors are denoted by solid and dashed arrows, respectively. In the online version, they are shown in blue and red color, respectively.

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Fig. 15

Measurement (points) and prediction profiles (surface) of the laser beam positioning error (unit: mm): (a) x-direction and (b) y-direction

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Fig. 16

Model fitting residual plots of the laser beam positioning error (unit: mm): (a) x-direction and (b) y-direction

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Fig. 17

Center fitting standard deviation of each cylinder (unit: mm): (a) x-direction and (b) y-direction

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Fig. 18

Equivalent deviation profiles of three cylinders: (a) 20 mm cylinder, (b) 10 mm cylinder, and (c) 5 mm cylinder

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Fig. 19

Deviation profiles of the nine cylinders in the top row of the grid in location effect experimentation

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Fig. 20

Model fitting and residual plots of the location effect term x0(s) (unit: mm): (a) estimated x0(s) (points) and prediction profile (surface) and (b) model fitting residual plot

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