Optimisation of Surface Roughness in 3D Printing Using the Bees Algorithm - Kamaruddin, Shafie; Ridzuan, Arman Hilmi; Sukindar, Nor Aiman (Springer, 2025)
Additive 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 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 Algorithm (BA). This study uses the Bees Algorithm to predict the best combination parameters to optimise the surface roughness of parts printed by a fused deposition modelling (FDM) machine. The predicted results are compared with the experimental 3D model sample and previous findings of other optimisation methods. Comparative analysis between predicted and actual surface roughness measurements showed good agreement with differences of less than 2%, indicating a significant prediction method. The result also shows that the Bees Algorithm found a better combination of parameters compared to other algorithms. This research provides another alternative optimisation approach for industries that utilise 3D printing.