Distribution of Carbon Nanotubes in an Aluminum Matrix by a Solution-Mixing Process.

In order to overcome the limitations of standard ball-mill mixing processes to fabricate a uniformly dispersed carbon nanotube (CNT) reinforcement composite without damaging CNTs in matrix powder, a unique and easy solution-mixing process was developed. The present study aims to synthesize Al-0.5 wt % CNT composites using ball-milling and solution-mixing processes and compares their CNT dispersion and structural and thermal properties. Compared with the ball-milling process, the solution-mixing process was simple and effective for the uniform distribution of CNTs without structural damage. Various methods were utilized to examine the structural characteristics of the composite powder. These techniques included high-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Raman spectroscopy, and particle size analysis. Raman spectroscopy observes an increase of defects in ball-milled composites, and the particle size analyzer confirms the structural deformation, resulting in the degradation of composite powder mechanical properties. In the solution-mixing process, aluminum particles and the structure of CNTs are well-preserved even after mixing. Thermogravimetric analysis (TGA) was used to research the thermal stability of the composite materials. The results validated the impact of CNTs on thermal characteristics enhancement (improved thermal resistance) when compared with pure aluminum, suggesting potential uses in the aerospace industry, transport, and construction sectors.

Prediction of Sinter Properties Using a Hyper-Parameter-Tuned Artificial Neural Network.

The present work aims at performing prediction validation for the physical properties of coke layered and nonlayered hybrid pelletized sinter (HPS) using artificial neural networks (ANNs). Physical property analyses were experimentally performed on the two HPS products. The ANN model was then trained to obtain the best prediction results with the grid-search hyper-parameter tuning method. The learning rate, momentum constant, and the number of neurons varied over specified ranges. The binary variable conversion was utilized to assess the two sintering processes. The nonlayered HPS product of 4 mm micropellets at basicity 1.75 and using 8% coke shows a good combination of physical properties, whereas HPS of 4 mm micropellets at 1.5 basicity using 4% coke as fuel and 2% coke as layering gives a radical improvement in physical properties. The yield of the HPS product is 96.07%, with the shatter index (SI), tumbler index (TI), and abrasion index (AI) values being 86.12, 79.60, and 5.74%, respectively. Hence, HPS can be preferred by implementing the layering of coke powder. The prediction analyses showed that the multilayer perceptron model (MLP) network with a 4-29-5 structure showed prediction accuracies of over 99.99% and a mean squared error (MSE) of 2.87 × 10-4. It verifies the accuracy and prediction effectiveness of the hyper-parameter-tuned ANN model.

Experimental Investigation of Different Fineness and Firing Temperatures on Pellets Properties of Different Iron Ore fines from Indian Mines.

In India, during mining and ore processing, ore fine generation is a common phenomenon, in which more than 60% of process ore becomes discarded material. To explore the alternative of high-grade ores, mutual replacement with the utility of dump ore fines is the best way. With this perspective, Kiruburu iron ore mine (Iron Ore No.1) and Meghataburu iron ore mine (Iron Ore No.2) dumped fines were chosen for a Blaine no. investigation, in the connection of firing temperatures, to get optimum desirable physical properties, Cold Compression Strength (C.C.S.),and Apparent Porosity (A.P.), with physico-chemical properties, Reducibility Degradation Index (R.D.I.), and Reducibility Index (R.I.). To characterize pellet properties with input variables, a microstructure phase study has been conducted using a scanning electron microscope (S.E.M.), energy dispersive spectroscopy (EDS), and X-ray diffraction analysis (XRD). The Iron Ore No.1 and 2 fine pellets survey showed good, desirable properties, at the Blaine no., of 1678 cm2/g and 2311 cm2/g (corresponding to 200 mesh size), and the best results are attained at a firing temperature of 1300 °C. Thermal kinetic analysis of the heating of pellets has been done to knowthe activation energy of different ore characteristics. The results showed that Iron Ore No.2 pellets have high activation energy.

Assessing the Mechanical Properties of a New High Strength Aluminum Hybrid MMC Based on the ANN Approach for Automotive Application.

Aluminum-based composites with characteristics such as low density and high strength to weight ratio have been identified to be one of the best-emerging alternatives. The lightweight composite is gaining popularity, particularly in the automotive industry. The composite's qualities make it a prospective material to replace significant materials that are now used in the automobile industry. For lightweight products, various weight reduction solutions were proposed. In the present work, one such lightweight composite was fabricated by using a stir casting process, which includes reinforcement powders viz. carbon nanotube and fly ash to pure aluminum. The use of fly ash helps in reducing the overall associated cost of the material as well as provides low density. The work aims to identify the amount of fly ash (by weight %) suitable to avail good mechanical properties. In concern with the mechanical properties, density, yield strength, ultimate tensile strength, and wear resistance of the composite specimen were examined. Moreover, the artificial neural network was adopted to identify minimum volumetric wear for a given set of conditions. From the results, it was perceived that with the increase in fly ash content, the volumetric wear of the fabricated composite decreases. However, with the increase in load and speed, the volumetric wear rate increases.