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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月4日

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  • Phan Quoc Khang Nguyen,
  • Yixia Sarah Zhang,
  • Zhongpu Zhang,
  • Richard Chunhui Yang

摘要

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.