Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161922
Title: A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation
Authors: Zhang, Yongjie
Choi, Joon Phil
Moon, Seung Ki
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Zhang, Y., Choi, J. P. & Moon, S. K. (2022). A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation. International Journal of Advanced Manufacturing Technology, 120(11-12), 8275-8291. https://dx.doi.org/10.1007/s00170-022-09291-0
Journal: International Journal of Advanced Manufacturing Technology
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.
URI: https://hdl.handle.net/10356/161922
ISSN: 0268-3768
DOI: 10.1007/s00170-022-09291-0
Rights: © 2022The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles
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