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Research Papers

Multi-Objective Accelerated Process Optimization of Part Geometric Accuracy in Additive Manufacturing

[+] Author and Article Information
Amir M. Aboutaleb

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759

Mark A. Tschopp

Fellow ASME
U.S. Army Research Laboratory,
Aberdeen Proving Ground, MD 21005

Prahalad K. Rao

Department of Mechanical and Materials
Engineering,
University of Nebraska-Lincoln,
Lincoln, NE 68588

Linkan Bian

Industrial and Systems Engineering Department,
Mississippi State University,
Starkville, MS 39759
e-mail: bian@ise.msstate.edu

1Corresponding author.

Manuscript received December 17, 2016; final manuscript received July 10, 2017; published online August 24, 2017. Assoc. Editor: Moneer Helu.This work is in part a work of the U.S. Government. ASME disclaims all interest in the U.S. Government's contributions.

J. Manuf. Sci. Eng 139(10), 101001 (Aug 24, 2017) (13 pages) Paper No: MANU-16-1656; doi: 10.1115/1.4037319 History: Received December 17, 2016; Revised July 10, 2017

The goal of this work is to minimize geometric inaccuracies in parts printed using a fused filament fabrication (FFF) additive manufacturing (AM) process by optimizing the process parameters settings. This is a challenging proposition, because it is often difficult to satisfy the various specified geometric accuracy requirements by using the process parameters as the controlling factor. To overcome this challenge, the objective of this work is to develop and apply a multi-objective optimization approach to find the process parameters minimizing the overall geometric inaccuracies by balancing multiple requirements. The central hypothesis is that formulating such a multi-objective optimization problem as a series of simpler single-objective problems leads to optimal process conditions minimizing the overall geometric inaccuracy of AM parts with fewer trials compared to the traditional design of experiments (DOE) approaches. The proposed multi-objective accelerated process optimization (m-APO) method accelerates the optimization process by jointly solving the subproblems in a systematic manner. The m-APO maps and scales experimental data from previous subproblems to guide remaining subproblems that improve the solutions while reducing the number of experiments required. The presented hypothesis is tested with experimental data from the FFF AM process; the m-APO reduces the number of FFF trials by 20% for obtaining parts with the least geometric inaccuracies compared to full factorial DOE method. Furthermore, a series of studies conducted on synthetic responses affirmed the effectiveness of the proposed m-APO approach in more challenging scenarios evocative of large and nonconvex objective spaces. This outcome directly leads to minimization of expensive experimental trials in AM.

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Figures

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Fig. 1

Flooded contour plots (deviation maps) of the benchmark part used in FFF experiments detailed in Sec. 3.1. The material is acrylonitrile butadiene styrene polymer. The first row (1) shows the top views, and the second row (2) contains the bottom views of the parts. (a)–(d) Different parts, printed under 70%, 80%, 90%, and 100% infill percentages at 230 °C, respectively. The reference scale is in millimeter [25].

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Fig. 2

(a) Contour plot of part concentricity versus infill percentage (If) and extruder temperature (te). (b) Contour plot of flatness versus infill percentage (If) and extruder temperature (te).

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Fig. 3

Design of the circle–square–diamond part—a simplified embodiment of the NAS 979 standard test artifact for testing accuracy of machining centers [25]. The dimensions are in millimeters. (a) and (b) Front and top views of the part, respectively, and (c) an isometric projection of the part [25].

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Fig. 4

The areas used to measure GD&T form the design part. (a) The faces used to measure flatness (), circularity (), and cylindricity (); (b) the planes used to measure the thickness—three thickness measurements are taken on each plane [25].

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Fig. 5

The eight points used for alignment of the scan points with the CAD model [25]

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Fig. 6

The representation of the data scatter plot matrix to illustrate both positive and negative correlations among pairs of part geometric characteristics (i.e., flatness, circularity, cylindricity, concentricity, and thickness). The slope of lines illustrates the Pearson correlation coefficient (ρ) for pairs of GD&T characteristics.

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Fig. 7

Schematic illustration of design space, objective space, nondominated design points, Pareto points, and Pareto front

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Fig. 8

Leveraging the information from prior data to accelerate solving subsequent subproblems

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Fig. 10

Schematic illustration of HV as the measure of the contribution of Pareto points. The rectangle represents ΔHV, i.e., the contribution of a new Pareto point in terms of the improvement in HV.

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Fig. 11

A schematic diagram of the FFF process [2,3]

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Fig. 12

Demonstrating the Pareto points and conducted experiments for the case study

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Fig. 13

Case A: Discretization of objective space for test problem with nonconvex Pareto front and well-distributed objective space

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Fig. 14

Case B: Discretization of objective space for test problem with nonconvex Pareto front and congested objective space

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Fig. 15

Case C: Discretization of objective space for test problem with increased number of process parameters

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Fig. 16

Case A: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with nonconvex Pareto front and well-distributed objective space)

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Fig. 17

Case B: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with nonconvex Pareto front and congested objective space)

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Fig. 18

Case C: Comparing estimated Pareto front resulted by m-APO and full factorial DOE with the true Pareto front (test problem with increased number of process parameters)

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