İskender Özkul

Mathematical Modeling of Diameter and Circularity Deviation in Wire Electrical Discharge Machining of a Hot Work Tool Steel

Abstract The suitable selection of manufacturing process and conditions are very important in manufacturing processes to obtain good surface quality and dimensional precision. Thus, it is required to know properties relating to material, surface quality and dimensional precision by means of mathematical models which allow some predictions taking into account operation conditions such as feed rate, current and pulse on time, etc. Wire electrical discharge machining (WEDM) represents a modification of electro discharge machining (EDM) and is widely used for a long time for cutting punches and dies, shaped pockets as well as other machine parts on conductive work materials. Present work is focused on the regression modeling predicting diameter and circularity deviation of a hot work tool steel. Mathematical relationships between circularity and diameter deviation as well as WEDM cutting parameters (feed rate, current and pulse on time) have been investigated. Results show that, regression analysis can be successfully applied for this mathematical model.

Prediction of Surface Roughness in Longitudinal Turning Process by a Genetic Learning Algorithm

Abstract The surface roughness is one of the major parameters for determining the level of machining quality. The cutting parameters and conditions have great importance to achieve the desired values during the turning process. In the present work, a new approach was considered for modelling the effect of various turning process parameters and conditions on surface roughness. The experimental studies about the surface roughness after the turning process documented in the literature were collected and compiled into a model based on a genetic learning algorithm. As input parameters for modeling the work piece alloy type, tool type, tool tip radius, tool coating type, cooling conditions, cutting speed, feed rate, and cut depth were used in the study and were comprehensivly compiled.

CHARACTERIZATION OF HYDROTHERMALLY SYNTHESISED HYDROXYAPATITE BIOCERAMIC

In this study, hydroxyapatite (HAP) was synthesized by hydrothermal method. The structural analysis, thermal analysis and electrical characteristics of HAP sample have been investigated. The structural analysis was performed to determine the crystal structure and to observe the surface morphology of the sample. The thermal analysis was made from room temperature to 925 ˚C, to determine the mass loss according to temperature and phase transitions or decomposition in the sample and also TG-DTA analysis was done to determine the thermal stability. The compositional analysis was done by EDX. I-V analysis was made to calculate the electrical conductivity value of the sample and electrical conductivity of the sample was obtained to be1.2x10 -10 S/cm.

Investigation of Fe content in Cu–Al–Ni Shape Memory Alloys

Polycrystalline Cu–Al–Ni–Fe-based shape memory alloys with different chemical composition were produced in an arc-melting furnace under an argon atmosphere. Homogenized and aged specimens were prepared for multiple analyses. The temperatures of reversible martensitic transformations, namely As, Af, Ms, Mf, Amax and ΔH enthalpy values were determined by a DSC device. The phase transition analysis from the room temperature to 850°C was undertaken by DTA. To characterize the lattice structure, an XRD analysis was conducted, the results of which were confirmed by microstructure images obtained from optical microscope observations.

The effect of the aging period on the martensitic transformation and kinetic characteristic of at % Cu 68.09 Al 26.1 Ni 1.54 Мn 4.27 shape memory alloy

In this work, the effect of aging period on the characteristic transformation temperatures, thermodynamic parameters and structural variations of CuAlNiMn shape memory alloys were investigated. Aging was performed at above the austenite finish temperature of the un-aged specimen (120°C) for six different retention times, namely 1h, 2h, 3h, 4h, 5h and 6h. The changes in the transformation temperatures were examined by differential scanning calorimetry at different heating/cooling rates. The aging period was found to have an effect on the characteristic austenite and martensite transformation temperatures and thermodynamic parameters such as the enthalpy and entropy of alloys. High-temperature order-disorder phase transitions were determined using a differential thermal analysis, which showed that all the un-aged and aged specimens had an A2 → B2, B2 → L21 and an L21 → 9R, 18R transition. The structural analysis of the un-aged and aged specimens was performed through X-ray diffraction measurements at room temperature. The intensities of the diffraction peaks varied according to the aging time.

ANN surface roughness prediction of AZ91D magnesium alloys in the turning process

Abstract This contribution presents an approach for the modeling and prediction of surface roughness in the turning of AZ91D magnesium alloys using an artificial neural network. The experiments were conducted with CCGT, DCGT and VCGT cutting tools under minimum quantity lubrication and dry machining conditions. AZ91D alloys were machined at different cutting speeds and feed rates, and the depth of cut was kept constant. 15 out of 18 experimental data points were used for the training of the artificial neural network model and the remaining 3 were used for the testing process. The average percentage error was calculated as 0.000815 % and 0.663 % for training and testing, respectively. The model and target results were found to have extremely low error rates.

Effects of machining parameters and reinforcement content on thrust force during drilling of hybrid composites

Abstract In this study, aluminum metal matrix composites were fabricated using the powder metallurgy technique with different ratios of alumina (Al2O3) and graphite as reinforcing elements. The obtained workpieces were then drilled using different machining parameters and the thrust force was measured using a force dynamometer. The thrust force data were modeled using a gene programming soft-computing technique and a function was obtained using the input parameters.