Multi-objective optimisation of fused filament fabrication of acrylonitrile butadiene styrene for enhancing mechanical performance and build time

Nguyen, PQK; Zhang, YS; Zhang, Z; Yang, RC

Multi-objective optimisation of fused filament fabrication of acrylonitrile butadiene styrene for enhancing mechanical performance and build time

Nguyen, PQK Zhang, YS Zhang, Z Yang, RC

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Publisher:: Springer Nature
Publication Type:: Journal Article
Citation:: International Journal of Advanced Manufacturing Technology, 2025, 137, (11), pp. 5431-5449
Issue Date:: 2025-04-01

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Field	Value	Language
dc.contributor.author	Nguyen, PQK
dc.contributor.author	Zhang, YS
dc.contributor.author	Zhang, Z
dc.contributor.author	Yang, RC
dc.date.accessioned	2025-08-11T03:51:12Z
dc.date.available	2025-08-11T03:51:12Z
dc.date.issued	2025-04-01
dc.identifier.citation	International Journal of Advanced Manufacturing Technology, 2025, 137, (11), pp. 5431-5449
dc.identifier.issn	0268-3768
dc.identifier.issn	1433-3015
dc.identifier.uri	http://hdl.handle.net/10453/189334
dc.description.abstract	Fused filament fabrication (FFF), a 3D printing technique, has gained prominence due to its diverse application in rapid prototyping, custom tooling, architectural modelling, and medical device fabrication. The current literature shows extensive works on single-objective optimisation but limitation in studies on multi-objective optimisation that determines a set of process parameters to attain a balance between conflicting properties such as mechanical properties and build time. Therefore, this study experimentally investigates mechanical properties and build time of FFF printed acrylonitrile butadiene styrene (ABS), considering the influences of five key FFF printing process parameters on mechanical properties, including extrusion temperature, layer thickness, printing speed, number of contours, and infill density. Response surface methodology (RSM) and artificial neural network (ANN) are both adopted for pattern recognition before the genetic algorithm (GA) and multi-criteria decision-making (MCDM) algorithm are applied for optimisation. Results reveal that the infill density is the main contributor to tensile strength while the layer thickness has the highest impact on build time. Both RSM-GA and ANN-GA approaches succeed at achieving a balance between tensile strength and build time. In comparison to RSM, ANN proves to be a superior tool with remarkable accuracy in predicting responses across diverse parameter settings. The findings of this study hold significant implications for designers and manufacturers of domestic and small industrial components.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	International Journal of Advanced Manufacturing Technology
dc.relation.isbasedon	10.1007/s00170-025-15457-3
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Multi-objective optimisation of fused filament fabrication of acrylonitrile butadiene styrene for enhancing mechanical performance and build time
dc.type	Journal Article
utslib.citation.volume	137
utslib.for	01 Mathematical Sciences
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2025-08-11T03:51:10Z
pubs.issue	11
pubs.publication-status	Published
pubs.volume	137
utslib.citation.issue	11

Abstract:

Fused filament fabrication (FFF), a 3D printing technique, has gained prominence due to its diverse application in rapid prototyping, custom tooling, architectural modelling, and medical device fabrication. The current literature shows extensive works on single-objective optimisation but limitation in studies on multi-objective optimisation that determines a set of process parameters to attain a balance between conflicting properties such as mechanical properties and build time. Therefore, this study experimentally investigates mechanical properties and build time of FFF printed acrylonitrile butadiene styrene (ABS), considering the influences of five key FFF printing process parameters on mechanical properties, including extrusion temperature, layer thickness, printing speed, number of contours, and infill density. Response surface methodology (RSM) and artificial neural network (ANN) are both adopted for pattern recognition before the genetic algorithm (GA) and multi-criteria decision-making (MCDM) algorithm are applied for optimisation. Results reveal that the infill density is the main contributor to tensile strength while the layer thickness has the highest impact on build time. Both RSM-GA and ANN-GA approaches succeed at achieving a balance between tensile strength and build time. In comparison to RSM, ANN proves to be a superior tool with remarkable accuracy in predicting responses across diverse parameter settings. The findings of this study hold significant implications for designers and manufacturers of domestic and small industrial components.

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http://hdl.handle.net/10453/189334