Optimization of FDM process parameters to minimize surface roughness with integrated artificial neural network model and symbiotic organism search

• Published in: Neural computing & applications Vol. 34; no. 20; pp. 17423 - 17439
• Main Authors: Saad, Mohd Sazli, Mohd Nor, Azuwir, Abd Rahim, Irfan, Syahruddin, Muhammad Ariffin, Mat Darus, Intan Zaurah
• Format: Journal Article
• Language: English
• Published: London Springer London 01.10.2022; Springer Nature B.V
• Subjects: Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Defects; Fused deposition modeling; Image Processing and Computer Vision; Mathematical models; Model accuracy; Neural networks; Optimization; Organisms; Original Article; Probability and Statistics in Computer Science; Process parameters; Product development; Rapid prototyping; Response surface methodology; Searching; Surface properties; Surface roughness; Thickness; Fused deposition modeling (FDM); Symbiotic organism search (SOS); Surface roughness; Artificial neural network (ANN)
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07370-7

Fused deposition modeling (FDM) has shown to be a highly beneficial process for product development. However, one of the great challenges in using FDM is maintaining the surface quality of the produced part. Poor texture quality can be regarded as a defect. It is not part of the geometric prototype but results from the fabrication process. Poor input parameters typically cause these defects by the user. This paper presents the integration between an artificial neural network (ANN) and symbiotic organism search, known as ANN–SOS, to model and minimize the surface roughness ( R a ) of the FDM process. The FDM input parameters considered were layer height, print speed, print temperature, and outer shell speed. The experimental data were collected using the central composite design response surface method. Then, the surface roughness model was established using an ANN. After validating the model's accuracy, it was combined with symbiotic organism search (SOS) to determine the optimal parameter settings for the minimum surface roughness value. The results illustrate that ANN–SOS with a 4-8-8-1 network structure would be the best model for surface roughness prediction. It was observed that decreasing the layer thickness, printing speed, print temperature, and outer shell speed of the FDM input parameters for ANN–SOS resulted in minimum surface roughness of approximately 2.011 µm, which was 12.36% better than the RSM method.