Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Abstract

Abstract Even though the prototypes built using Fused Deposition Modelling (FDM) process are found to exhibit good mechanical properties, there are ample scopes to improve them by means of selecting suitable process parameters. Since the FDM process involves more number of process parameters, the selection of optimized values becomes more complex and time consuming. Further, the complex correlation among the process parameters makes the selection process more tedious and involves more numerical steps. Hence it has been intended to perform a physical experiment with the known parameters to determine the performance measures of the built prototypes. With this moto, in this work the effect of the 3D printing parameters is studied and the optimal combination of these parameters are determined. The Taguchi L18 orthogonal array based values are assigned for process parameters and the physical prototypes are fabricated. These specimens are tested in the laboratory and the observations are analyzed. It has been found that the process parameters under consideration have a good effect on the strength of the built models. Out of the 18 experiments, better experiments are selected by using a Machine Learning (ML) approach namely decision tree (DT). Finally, the best combination of parameters has been determined by using a novel hybrid multi objective technique which is formulated by integrating Fuzzy Analytical Hierarchy Process (FAHP) and Complex Proportional Assessment of alternatives (COPRAS) techniques. Then a confirmation experiment has also been done to confirm the optimal combination of parameters. The influence of the parameters is also found by using ANOVA (Analysis of Variance) method. The final results show that the raster angle influences the outputs more while the raster to raster gap has the least influence.