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

Mohammad Azad Alam,Mohammad Yusuf,Faisal Masood,Javed Akhter,Imtiaz Ahmed Shozib,Salit M Sapuan,Mohammad Azeem,Hamdan H Ya,Imtiaz Ali Soomro

doi:10.3390/cryst12030372

Mohammad Azad Alam, Mohammad Yusuf + Show 7 more

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https://doi.org/10.3390/cryst12030372

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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.

Highlights

The quest for fuel-saving and cost-effective materials with attractive structural and mechanical properties has led to the development of aluminum matrix composites for automotive and aircraft applications
The estimated artificial neuron network (ANN) values were similar to the experimental results, indicating a slight difference in error
Similar studies support the effectiveness of developed ANN models for optimization of the mechanical alloying process for producing composite powder and for prediction of mechanical properties [57,65]

Summary

Introduction

The quest for fuel-saving and cost-effective materials with attractive structural and mechanical properties has led to the development of aluminum matrix composites for automotive and aircraft applications. In various engineering fields, such as transportation, aviation, and the military, there is a growing need for new and advanced materials with superior physical and mechanical properties. This is because single monolithic materials do not display combined structural properties such as hardness and ductility. Composites of the particle-reinforced aluminum alloy-based metal matrix (AMMCs) are highly desirable materials for aircraft and automotive applications. Aluminum alloy based MMCs reinforced with titanium carbide (TiC) particles have been suitable for aircraft, automotive, defense, and other structural applications due to their excellent mechanical and physical characteristics.

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