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

Abstract

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.

Prediction of Particle Size Distribution of Mill Products Using Artificial Neural Networks

“…Details of ANN and other soft computing technologies applications on mining engineering can be found in Jang and Topal (2014) [11]. In relation to PSD prediction, ANN has been applied to the crystallization process with mechano-chemical alloying [12], the prediction of rock fragmentation after blasting [13], and the prediction of PSD in a granulation process [14]. On the other hand, based on our literature review, the ANN-based predictive modeling of size reduction in milling processes has not been found.…”

The Development of New Perovskite-Type Oxygen Transport Membranes Using Machine Learning

“…Accordingly, the use of ANN is not envisaged in this work and will not be discussed further in detail, as such a discussion would go far beyond the focus of this work. For more extensive introductions to ANNs and their use, especially in materials research, we refer to some recent papers [77,78], among others.…”

Experimental investigation and numerical modeling of mechanically alloyed Al-TiC composites

“…Besides, the UCD and hardness increased with increasing the TiC content with enhancement over 133% and 105%, respectively, higher than the AA6061matrix. Alam et al 20 investigated the effect of 1, 3, 5, and 7 h MT on the mechanically alloyed Al-4 wt. % TiC composites.…”

“…[15][16][17][18] Due to the excessive plastic deformation and rupture processes that occur under the impact of small and hard balls with the powders, MA functions as a simple mechanical milling process that produces homogenous nanocrystalline materials and composites. 4 The recent previous investigations 7,8,13,17,19,20 associated with AMCs and TiC as reinforcements are listed in Table 1.…”