Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) have been widely used in various fields of today’s manufacturing industries such as transportation, aerospace, and medical because of its ability to produce parts of complex designs with less manufacturing time and cost. However, a proper selection of input process parameters is a vital aspect in order to obtain the best quality of the printed part. This paper presents several approaches, namely response surface methodology, particle swarm optimization, and symbiotic organism search, to find the optimal parameter settings for better surface quality, i.e., surface roughness of the FDM printed part. Layer height, print speed, print temperature, and outer shell speed were considered as the input parameters and surface roughness as the output response. The experimental design was carried out using response surface methodology (RSM) method. Then, the relationship between the input parameters and the surface roughness was established using regression model. Once the accuracy of the model had been validated, the model was then coupled with particle swarm optimization (PSO) and symbiotic organism search (SOS) to optimize the input parameters that would lead to minimum surface roughness. Experimental results showed that the surface roughness obtained using PSO and SOS have improved about 8.5% and 8.8%, respectively, compared with the conventional method, i.e., RSM. A good agreement between the predicted surface roughness and the experimental values was also observed.
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This research study was supported by the researchers from University Malaysia Perlis. The authors would like to express their gratitude to University Malaysia Perlis for their guidance in order to complete this research study.
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