Parametric analysis and optimization of fused deposition modeling technique for dynamic mechanical properties of acrylic butadiene styrene parts

Abstract

Fused deposition modeling (FDM) technology is catching the fast global market in the real-time production of polymeric parts. Process variables highly influence the performance characteristics of FDM-generated parts, so mechanical performance is not perfect for all applications. In actual conditions, parts produced by FDM are constantly subjected to loading at different temperatures. The former studies mainly concentrated on the properties of FDM products to static loading environments. There is a scope of effective investigation on the influence of FDM processing conditions on dynamic mechanical properties using artificial intelligence (AI) based techniques. The present study focused on investigation and optimization the manufacturing process parameters to evaluate the dynamic mechanical performance of FDM-produced part. The experimental runs were obtained through central composite design in Minitab software. A DMA8000 instrument was used to test the specimens for dynamic mechanical performance. The mathematical models were developed and optimized through different approaches like response surface methodology-genetic algorithm (RSM-GA) and artificial neural network-genetic algorithm (ANN-GA). The techniques for order preference by similarity to an ideal solution (TOPSIS) is employed to obtain the best parameter settings from sets of optimized solutions. The sequential use of ANN-GA and TOPSIS methods predicted the highest values of storage modulus 1619.61 MPa and loss modulus 257.38 MPa corresponding to 68.94° raster angle, 81.48% infill density, 0.10 mm layer thickness, 237.73°C nozzle temperature and 38.97 mm/s print head speed. The confirmation tests were conducted to validate the predicted result that upscale the desired properties. The RSM-GA-TOPSIS occurred with a prediction error of 2.40% and −3.31%, corresponding to storage and loss modulus. Similarly, ANN-GA-TOPSIS shows 2.17% and 2.89% prediction error corresponding to storage and loss modulus. The experimental and analytical outcome of present study will be helpful for the designers of intricate functional parts which come under thermo-mechanical loading conditions.