Prediction of machinability of AA7075 using artificial neural networks

In this study, the effect of modification Al-12Si (Etial 140) casting alloy with Al10Sr, CuSn5, and Al10Sr + CuSn5 master alloys has been investigated with respect to the cutting forces. In order to characterize the morphology of materials used in this study, metallography, hardness, density tests, and chemical analysis were performed. An optical microscope was used to examine the microstructure of the samples. Machinability tests were performed with the turning method by using a titanium diboride-coated cementite carbide cutting tool. All experiments were carried out under dry machining conditions with three different cutting speeds and feed rates with a constant cut depth. After the turning test, scanning electron microscope (SEM) images were used to examine the build-up edge (BUE) formation on the cutting tool. It was determined that the modification process has a positive effect on increasing the density and hardness. According to the results, it was observed that cutting (Fc), feed (Ff), and tangential (Fr) forces decreased for all material groups with increasing cutting speed V. Furthermore, Fc, Ff, and Fr forces increased for all material groups with increasing feed rate f. Finally, the highest cutting forces were observed on the Etial 140 c + Al10Sr master alloy.

Effect of casting modification materials on cutting forces of an Al12Si alloy used in aircraft technology

Fıçıcı

Keser

2023

Solid particle erosion wear behavior of severe plastically deformed AA7075 alloys

Kaya¹

2018

In this study, severe plastic deformation (SPD) and wear behavior of AA 7075 allosy widely used in aerospace, automotive and defence industries, has been investigated. The equal channel angular pressing (ECAP) process was applied to achieve an ultra-fine grain structure of homogenized AA 7075 materials. Solid particle abrasion behavior was impressed into homogenized and ECAP' ed materials via a variable particle impingement angle (PIA) and acceleration pressure. Microstructure and wear behavior of the tested materials were investigated using scanning electron microscopy (SEM) and a surface profilometer. In the studies conducted, it was observed that an increase in the number of ECAP passes with the effect of SPD, reduced the solid particle wear behavior.

Uncertainty analysis of milling parameters using Monte Carlo simulation, the Taguchi optimization method and data-driven modeling

Kahraman¹,

Bilge²,

Ozturk³

2019

Surface roughness plays an important role in the performance of finished structures. The surface quality obtained is enormously affected by cutting parameters. Therefore, the purpose of the present study is to examine the surface roughness value of aluminum 7075 workpiece material during milling operation by considering three steps: (1) the multi-nonlinear regression (MNLR) modeling basis of Taguchi design, (2) optimization based on signal to noise ratio (S/N), and (3) probabilistic uncertainty analysis depending on Monte Carlo technique as a result of depth of cut, cutting speed and feed rate. The depth of cut of 0.2 mm, cutting speed of 900 m × min−1, and feed rate of 0.1 mm × tooth−1 were determined as Taguchi-optimized conditions with a surface roughness of 0.964 μm. In order to justify the surface roughness predicted under optimized conditions in relation to the predicted Taguchi method, three repetitive verification experiments were performed and surface roughness of 0.964 μm ± 0.3 % was achieved. The best-fit MNLR method with an R2pred (predicted regression coefficient) of 98.02 % is useful for calculating the success of estimating the outcome variable. Monte Carlo simulations were found to be quite effective for identifying the uncertainties in surface roughness that could not be captured by means of deterministic methods.