Artificial neural network modeling to predict the effect of milling time and tic content on the crystallite size and lattice strain of Al7075-TiC composites fabricated by powder metallurgy - Alam, Mohammad Azad; Ya, Hamdan H.; Mohammad Azeem; Mohammad Yusuf; Soomro, Imtiaz Ali; Masood, Faisal; Shozib, Imtiaz Ahmed; Salit, M. Sapuan; Akhter, Javed (MDPI, 2022)
In the study, Al7075-TiC composites were synthesized by using a novel dual step blending process followed by cold pressing and sintering. The effect of ball milling time on the microstructure of the synthesized composite powder was characterized using X-ray diffraction measurements (XRD), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and transmission electron microscopy (TEM). Subsequently, the integrated effects of the two-stage mechanical alloying process were investigated on the crystallite size and lattice strain. The crystallite size and lattice strain of blended samples were calculated using the Scherrer method. The prediction of the crystallite size and lattice strain of synthesized composite powders was conducted by an artificial neural network technique. The results of the mixed powder revealed that the particle size and crystallite size improved with increasing milling time. The particle size of the 3 h-milled composites was 463 nm, and it reduces to 225 nm after 7 h of milling time. The microhardness of the produced composites was significantly improved with milling time. Furthermore, an artificial neuron network (ANN) model was developed to predict the crystallite size and lattice strain of the synthesized composites. The ANN model provides an accurate model for the prediction of lattice parameters of the composites.