Optimization of the Young’s Modulus of Low Flow Polypropylene Talc/Colemanite Hybrid Composite Materials with Artificial Neural Networks

Abstract

Abstract In this experimental study, Taguchi experiment design and Artificial Neural Networks (ANN) methods were used to optimize the Young’s modulus of polypropylene / talc / colemanite hybrid composite materials depending on their production parameters and hybrid filler ratios in different percentages by weight. Low flow polypropylene homopolymer (LPPH) is a thermoplastic widely used in the automotive industry as a buffer and fender material (raw material) because of its high impact resistance. It is known, that in the automotive industry, weight reduction efforts are under way. However, there is currently no published scientific and / or experimental studies on the Tensile properties of polypropylene / talc / colemanite hybrid composite materials. On the other hand, it is also known that the Tensile properties of thermoplastic composites are influenced by injection molding parameters and that these characteristic features influenced by injection molding parameters can not be predicted and/or determined, for example from analysis and/or computer-aided design programs. In this context, in the first phase of the study, the predicted boundary conditions of the design parameters such as hybrid filler ratio and injection parameters such as nozzle temperature, injection speed and mold temperature were determined, which can influence the tensile properties and also increase and / or decrease the Young’s modulus. In the second phase of the study, tensile tests were performed on talc (TC) and colemanite (GC) filled polypropylene (LPPH) hybrid composite materials according to ISO 527-1, and the ANN method was used to model the Young’s modulus of LPPH / TC / GC hybrid composite materials. The results obtained were compared with the ANOVA results obtained with the Taguchi experiment design method. It was found that the ANN method provided satisfactory results.