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Additive manufacturingAdditive manufacturing (AM) is renowned for its capability to produce parts that are low-cost and have less manufacturing time. One of the main challenges in this additive manufacturingAdditive manufacturing technology is selecting proper input process parameters to achieve good quality of the 3D printed model. The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees AlgorithmBees algorithm (BA). This study uses the Bees AlgorithmBees algorithm to predict the best combination parameters to optimise the surface roughnessSurface roughness of parts printed by a fused deposition modelling (FDM)Fused Deposition Modelling (FDM) machine. The predicted results are compared with the experimental 3D model sample and previous findings of other optimisationOptimisation methods. Comparative analysis between predicted and actual surface roughnessSurface roughness measurements showed good agreement with differences of less than 2%, indicating a significant predictionPrediction method. The result also shows that the Bees AlgorithmBees algorithm found a better combination of parameters compared to other algorithmsAlgorithms. This research provides another alternative optimisationOptimisation approach for industries that utilise 3D printing3D Printing.

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Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm

  • 章节

  • 发表日期：

    2025年

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  • Shafie Kamaruddin,
  • Arman Hilmi Ridzuan,
  • Nor Aiman Sukindar

摘要

Additive manufacturingAdditive manufacturing (AM) is renowned for its capability to produce parts that are low-cost and have less manufacturing time. One of the main challenges in this additive manufacturingAdditive manufacturing technology is selecting proper input process parameters to achieve good quality of the 3D printed model. The focus of this study is to determine the optimum input parameter of the 3D printer using the Bees AlgorithmBees algorithm (BA). This study uses the Bees AlgorithmBees algorithm to predict the best combination parameters to optimise the surface roughnessSurface roughness of parts printed by a fused deposition modelling (FDM)Fused Deposition Modelling (FDM) machine. The predicted results are compared with the experimental 3D model sample and previous findings of other optimisationOptimisation methods. Comparative analysis between predicted and actual surface roughnessSurface roughness measurements showed good agreement with differences of less than 2%, indicating a significant predictionPrediction method. The result also shows that the Bees AlgorithmBees algorithm found a better combination of parameters compared to other algorithmsAlgorithms. This research provides another alternative optimisationOptimisation approach for industries that utilise 3D printing3D Printing.