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

被引:12
作者
Alam, Mohammad Azad [1 ]
Ya, Hamdan H. [1 ]
Azeem, Mohammad [1 ]
Yusuf, Mohammad [2 ]
Soomro, Imtiaz Ali [1 ]
Masood, Faisal [3 ]
Shozib, Imtiaz Ahmed [4 ]
Sapuan, Salit M. [5 ,6 ]
Akhter, Javed [7 ]
机构
[1] Univ Teknol PETRONAS, Mech Engn Dept, Seri Iskandar 32610, Perak, Malaysia
[2] Univ Teknol PETRONAS, Chem Engn Dept, Seri Iskandar 32610, Perak, Malaysia
[3] Univ Teknol PETRONAS, Elect Engn Dept, Seri Iskandar 32610, Perak, Malaysia
[4] Rochester Inst Technol, Mech Engn Dept, Rochester, NY 14623 USA
[5] Univ Putra Malaysia, Inst Trop Forestry & Forest Prod, Lab Biocomposite Technol, Serdang 43400, Malaysia
[6] Univ Putra Malaysia, Dept Mech & Mfg Engn, Serdang 43400, Malaysia
[7] Univ Engn & Technol Taxila, Dept Mechatron Engn, Chakwal Campus, Chakwal 47050, Pakistan
关键词
mechanical alloying; Al7075; TiC composites; microhardness; artificial neural networks; crystallite size; lattice strain; ALUMINUM-MATRIX COMPOSITES; MECHANICAL-PROPERTIES; ALLOY; MICROSTRUCTURE; BEHAVIOR; CONSOLIDATION; REINFORCEMENT; OPTIMIZATION; MICROHARDNESS; HARDNESS;
D O I
10.3390/cryst12030372
中图分类号
O7 [晶体学];
学科分类号
0702 ; 070205 ; 0703 ; 080501 ;
摘要
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.
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页数:20
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