Hamid Doostmohammadi

In this work, the softening and melting (S&M) behaviour and whole blast furnace (BF) performance of Newman Blend Lump (NBLL), plant sinter, and sinter-NBLL mixture were studied using S&M under load test and numerical BF modelling. Both physical and chemical interactions between sinter and lump were confirmed in the S&M process. Significant improvements were found in the S&M behaviour of the sinter-NBLL mixture because of the physical and chemical interaction. The physical interaction was examined using X-ray/Neutron Computed Tomography (CT) scanning on the samples from interrupted S&M under load tests. The void fraction in the ferrous layer of the sinter-NBLL mixture was found to be similar to the sinter and was higher than that for NBLL. The chemical interaction was investigated by analysing the Ca transfer from sinter to NBLL, which indicated that Ca transfer started around 1200°C in the S&M process. FactSage was used to assist in the interpretation of the S&M test results. It was found that the NBLL sample starts to melt at a lower temperature compared to other burdens used in the present study, which also agreed well with the CT scan results. The whole BF performance of different ferrous burdens was studied using the experimental results as inputs. The sinter-NBLL mixture behaved more like the sinter than the NBLL; compared with the sinter only burden with the same total basicity, the sinter-NBLL combination formed a more permeable CZ, had a lower total BF pressure drop, and a higher gas utilization rate.

As an innovative approach in this study, artificial neural network and cuckoo algorithm have been applied to estimate and optimize the main cutting forces of various Si brass alloys during turning operation due to economic reasons. Accordingly, the chemical composition (Cu, Zn and Si contents) and process parameters (cutting speed, feed rate and depth of cut) are simultaneously implemented as input variables and the main cutting force is adjusted as an output variable. Moreover, the genetic algorithm is used to determine the optimum condition of the input parameters to obtain the lowest amounts of the main cutting force. Coupling of the hybrid cuckoo algorithm with artificial neural network has resulted in decreasing the mean absolute percentage error of the optimum structure (6-10-7-1) from 9.025 to 1.59E–6%. The validation of the proposed model has been done by performing the new set of experimental tests. The measured and predicted main cutting forces are in good agreement. The Si brass alloys including Zn equivalent about 44.97 wt% has the lowest main cutting force due to the formation of the Widmanstäetten morphologies in the microstructure. The outcome of this study may be useful for machining industry of the free-cutting Si brasses.

Lead-free alloys have attracted great attentions recently due to the toxic nature of lead for the human body. In this study, low amounts of Mg and Sb were added to the Cu65–Zn35 brass and microstructure, mechanical properties and machinability of samples were compared to Cu65–Zn35 brass. Both Mg and Sb led to the promotion of β′ phase as well as the formation of new ternary copper rich intermetallic particles. It was found that these particles had a significant role in the reduction of the ultimate tensile strength, toughness, work hardening and elongation while increasing the hardness of samples. Results of machinability evaluation of samples showed that the cutting forces were decreased significantly and morphology of chips were improved compared to Cu65–Zn35 brass sample.

This study was aimed at developing lead-free brass alloys with the goal of substituting lead element with tin. For this purpose, lead-free alloys with tin were developed and the microstructure, hardness and machining behaviour of the Cu–30%Zn alloy was compared with Cu–30%Zn– x%Sn ( x = 1.2, 3.2, 5.4, 8, 11.4, 13.9, 17.4). The results showed that the addition of Sn to single-α phase brass led to the formation of duplex (α + β′) brass and then the formation of (β ′ + Ɣ ) brass both with increased hardness. In addition, the addition of Sn to Cu–30%Zn alloy led to the decrement of equivalent machining forces ( F_m), surface roughness and also the promotion of chip fragmentation due to the formation of the β ′ phase, which is an improvement in machinability.