Optimization of fused deposition modeling process parameters using a fuzzy inference system coupled with Taguchi philosophy

Abstract

Fused deposition modeling (FDM) is an additive manufacturing technique used to fabricate intricate parts in 3D, within the shortest possible time without using tools, dies, fixtures, or human intervention. This article empirically reports the effects of the process parameters, i.e., the layer thickness, raster angle, raster width, air gap, part orientation, and their interactions on the accuracy of the length, width, and thickness, of acrylonitrile-butadiene-styrene (ABSP 400) parts fabricated using the FDM technique. It was found that contraction prevailed along the direction of the length and width, whereas the thickness increased from the desired value of the fabricated part. Optimum parameter settings to minimize the responses, such as the change in length, width, and thickness of the test specimen, have been determined using Taguchi’s parameter design. Because Taguchi’s philosophy fails to obtain uniform optimal factor settings for each response, in this study, a fuzzy inference system combined with the Taguchi philosophy has been adopted to generate a single response from three responses, to reach the specific target values with the overall optimum factor level settings. Further, Taguchi and artificial neural network predictive models are also presented in this study for an accuracy evaluation within the dimensions of the FDM fabricated parts, subjected to various operating conditions. The predicted values obtained from both models are in good agreement with the values from the experiment data, with mean absolute percentage errors of 3.16 and 0.15, respectively. Finally, the confirmatory test results showed an improvement in the multi-response performance index of 0.454 when using the optimal FDM parameters over the initial values.