Experimental Study and Predictive Modelling of Fused Deposition Modelling (FDM) Using TOPSIS and Fuzzy Logic Expert System

Abstract

Fused deposition modeling (FDM) is a well-liked additive fabrication method used to manufacture prototypes and components in industries. The quality of the 3D printed component depends on the temperature profile between the layers of the printed components and the process parameters. The deviations in the quality of manufactured components can be established using tools of metrology, including Coordinate-Measuring Machine and Machine Vision. This research is to determine the effect of temperature on the aforementioned phenomenon by using collected data to build a predictive model. The leading factor effect intrigue is stressed for the correlative closeness coefficient (Cn*) and Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS). The most favorable combinations of the experiment were obtained from the response diagram at a layer thickness of 0.3 mm, print speed of 80 mm/sec, and infill percentage of 20%. It is noted that the parameters have a contribution of 55.60%, 33.16%, and 0.15%, respectively. The majority of agreeable combinations of the investigations were acquired from the main factor effect response diagram, a layer thickness of 0.3 mm, printing FDM speed of 80 mm/sec, and an infill percentage of material is 20% for maximizing the temperature gradient and minimizing shrinkage and warpage. A fuzzy logic expert system was used to predict the shrinkage allowances precisely with less than 5% error.