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A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation

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Abstract

In additive manufacturing (AM), due to large number of process parameters and multiple responses of interest, it is hard for AM designers to attain optimal part performance without a systematic approach. In this research, a data-driven framework is proposed to achieve the desired AM part performance and quality by predicting part properties and optimising AM process parameters effectively and efficiently. The proposed framework encompasses efficient sampling of design space and establishing the initial experiment points. Based on established empirical data, surrogate models are used to characterise the influence of critical process parameters on responses on interest. Furthermore, process maps can be generated for enhancing understanding on the influence of process parameters on responses of interests and AM process characteristics. Subsequently, multi-objective optimisation coupled with a multi criteria decision-making technique is applied to determine an optimal design point, which maximises the identified responses of interest to meet the part functional requirements. A case study is used to validate the proposed framework for optimising an ULTEM™ 9085-fused filament fabrication part to meet its functional requirements of surface roughness and mechanical strength. From the case study, results indicate that the proposed approach is able to achieve good predictive results for responses of interest with a relatively small dataset. Furthermore, process maps generated from the surrogate model provide a visual representation of the influence between responses of interest and critical process parameters for FFF process, which traditionally requires multiple investigations to arrive at similar conclusions.

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Abbreviations

AM:

Additive manufacturing

FFF:

Fused filament fabrication

PBO:

Part build orientation

LHS:

Latin hypercube sampling

GPR:

Gaussian process regression

ANN:

Artificial neural network

SVR:

Support vector regression

RW:

Raster width

RRAG:

Raster-raster air gap

RA:

Raster angle

RBF:

Radial basis function

PSP:

Process structure property

MOO:

Multi objective optimisation

MCDM:

Multi-criteria decision-making

TOPSIS:

Technique for order of preference by similarity to ideal solution

UTS:

Ultimate tensile strength

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Funding

This research is supported by a grant from ST Engineering Aerospace, EDB-IPP, the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme, and Singapore Centre for 3D Printing.

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Authors and Affiliations

  1. Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore

    Yongjie Zhang & Seung Ki Moon

  2. Department of 3D Printing, Korea Institute of Machinery & Materials, Daejeon, 34103, Republic of Korea

    Joon Phil Choi

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  1. Yongjie Zhang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yongjie Zhang. The first draft of the manuscript was written by Yongjie Zhang, and all authors reviewed previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Seung Ki Moon.

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Appendix

Appendix

Table 6 Normalised values for X,Y,Z, RRAG, RW, and RA
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Zhang, Y., Phil Choi, J. & Moon, S.K. A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation. Int J Adv Manuf Technol 120, 8275–8291 (2022). https://doi.org/10.1007/s00170-022-09291-0

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