Sedat Bingöl

Experimental and Numerical Study on the Strength of Aluminum Extrusion Welding

The quality of extrusion welding in the extruded hollow shapes is influenced significantly by the pressure and effective stress under which the material is being joined inside the welding chamber. However, extrusion welding was not accounted for in the past by the developers of finite element software packages. In this study, the strength of hollow extrusion profile with seam weld produced at different ram speeds was investigated experimentally and numerically. The experiments were performed on an extruded hollow aluminum profile which was suitable to obtain the tensile tests specimens from its seam weld’s region at both parallel to extrusion direction and perpendicular to extrusion direction. A new numerical modeling approach, which was recently proposed in literature, was used for numerical analyses of the study. The simulation results performed at different ram speeds were compared with the experimental results, and a good agreement was obtained.

Application of gene expression programming in hot metal forming for intelligent manufacturing

Design of the die in hot metal forming operations depends on the required forming load. There are several approaches in the literature for load prediction. Artificial neural networks (ANNs) have been successfully used by a few researches to estimate the forming loads. This paper aims at using the effectiveness of a new evolutionary approach called gene expression programming (GEP) for the estimation of forging load in hot upsetting and hot extrusion processes. Several parameters such as angle (α), L/D ratio (R), friction coefficient (µ), velocity (v) and temperature (T) were used as input parameters. The accuracy of the developed GEP models was also compared with ANN models. This comparison was evidenced by some statistical measurements (R2, RMSE, MAE). The outcomes of the study showed that GEP can be used as an effective tool for representing the complex relationship between the input and output parameters of hot metal forming processes.

Optimizing Cutting Conditions and Prediction of Surface Roughness in Face Milling of AZ61 Using Regression Analysis and Artificial Neural Network

In this paper artificial neural network (ANN) and regression analysis were used for the prediction of surface roughness. Five models of neural network were developed and the model that showed best fit with experimental results was with 6 neurons in the hidden layer. Regression analysis was also used to build a mathematical model representing the surface roughness as a function of the process parameters. The coefficient of determination was found to be 94.93% and 93.63%, for the best neural network model and regression analysis, respectively, from the comparison of the models with thirteen validation experimental tests. Optical microscopy was conducted on two machined surfaces with two different values of feed rates while maintaining the spindle speed and depth of cut at the same values. Examining the surface topology and surface roughness profile for the two surfaces revealed that higher feed rate results in relatively thick roughness markings that are distantly spaced, whereas low values of feed rate result in thin surface roughness markings that are closely spaced giving better surface finish.

FE Analyzing of Layout Types for Multi-hole Extrusion Dies

Extrusion method is an important bulk-forming process to transform materials into semi-finished products in the form of bar, strip, and solid section as well as tubes and hollow sections. One of the basic considerations of die design is to determine the number of die openings based on the shape and size of the profile. In this study, variation of the extrusion load and temperature is investigated for different number and layout of die openings by finite element analysis. Three types of layouts, radial layout of a multi-hole die, flat layout of a multi-hole die and orientation of a shape around its center of gravity, were simulated with four openings in the dies. Moreover, the most commonly used type of them was also simulated for different number of openings in the die to compare the effect of the openings number on the extrusion.

Prediction of Cutting Conditions in Turning AZ61 and Parameters Optimization Using Regression Analysis and Artificial Neural Network

All manufacturing engineers are faced with a lot of difficulties and high expenses associated with grinding processes of AZ61. For that reason, manufacturing engineers waste a lot of time and effort trying to reach the required surface roughness values according to the design drawing during the turning process. In this paper, an artificial neural network (ANN) modeling is used to estimate and optimize the surface roughness (Ra) value in cutting conditions of AZ61 magnesium alloy. A number of ANN models were developed and evaluated to obtain the most successful one. In addition to ANN models, traditional regression analysis was also used to build a mathematical model representing the equation required to obtain the surface roughness. Predictions from the model were examined against experimental data and then compared to the ANN model predictions using different performance criteria such as the mean absolute error, mean square error, and coefficient of determination.

Modeling and comparison of failure load with FEM and ANNs

In this study, failure analysis was researched in woven glass fiber-reinforced epoxy resin composite plates jointed with two parallel pins under the effect of seawater. The effects of immersion time in seawater and changes in joint geometry on first failure loads were experimentally examined. In addition to this, the experimental results were compared with the results of the finite element analysis and artificial neural network modeling. The specimens were immersed in seawater for periods of 0, 3, and 6 months to observe the effects of seawater. For joint geometry, the parameters of the edge distance-to-upper hole diameter (E/D), the two hole-to-hole center diameter (K/D), the distance from the upper or the lower edge of the specimen to the center of the hole-to-hole diameter (M/D), and the width of the specimen-to-hole diameter (W/D) ratios were selected. In the numerical analysis, the finite elements models of the specimens were realized with the ANSYS 11.0 finite elements modeling and the Tsai-Wu failure criteria were used to predict the first failure loads. The comparison made in this study, results of the experimental, the finite element analysis, and artificial neural network modeling were compared and were found to be in good agreement for the first failure load.

The Decreasing of the Extrusion Time with Varying Ram Speed

In the extrusion process, friction and workpiece deformation usually cause an increase in the temperature of the extrudate. During the stroke, this temperature increase may reach a critical level and restrict the ram speed. In this study, extrusion processes, which were conducted under real production conditions, were simulated at different ram speeds using finite element modeling. In this way, the maximum workable constant ram speed was determined according to a defined critical extrusion temperature as a reference value. In the next stage, new ram-speed models were developed as alternatives to the maximum-workable, constant ram speed. The results showed that the time taken to extrude could be significantly shortened by using these models.In the extrusion process, friction and workpiece deformation usually cause an increase in the temperature of the extrudate. During the stroke, this temperature increase may reach a critical level and restrict the ram speed. In this study, extrusion processes, which were conducted under real production conditions, were simulated at different ram speeds using finite element modeling. In this way, the maximum workable constant ram speed was determined according to a defined critical extrusion temperature as a reference value. In the next stage, new ram-speed models were developed as alternatives to the maximum-workable, constant ram speed. The results showed that the time taken to extrude could be significantly shortened by using these models.

Finite Element Modeling of a Torque Rod Forging Process

Aluminum forging products has been increasingly used in automotive and aerospace industry due to their lightness and strength. Torque Rod is a connection part used for tying of the axle to the chassis frame. In this study, torque rod part from two different aluminum alloys (AA7075 and AA6061) were analyzed by finite element method. Three different initial temperatures of the workpiece; 300, 400 and 500 °C, have been used in simulations. Finally, the load-stroke and effective stress-stroke diagrams were examined according to forging of the mentioned aluminum alloys at different temperatures.