Murat Sarıkaya

Modeling and multi-response optimization of milling characteristics based on Taguchi and gray relational analysis

This article focuses on experimental investigation and effective approach to optimize the milling characteristics with mono and multiple response outputs such as vibration signals, cutting force, and surface roughness. To achieve this goal, experiments were designed based on Taguchi’s L18 (21u2009×u200933) orthogonal array. During the milling of AISI 1050 steel, process performance indicators such as vibration signals (RMS), cutting force (Fx), and surface roughness (Ra) were measured. The effect of process parameters such as depth of cut, feed rate, cutting speed, and number of insert on RMS, Fx, and Ra were investigated and parameters were simultaneously optimized by taking into consideration the multi-response outputs using Taguchi-based gray relational analysis. Taguchi’s signal-to-noise ratio was employed to obtain the best combination with smaller-the-better and larger-the-better approaches for mono- and multi-optimization, respectively. Analysis of variance was conducted to determine the importance of process parameters on responses. Mathematical models were created, namely, RMSpre, Rapre, and Fxpre, using regression analysis. According to the multi-response optimization results, which were obtained from the largest signal-to-noise ratio of the gray relational grade, it was found out that the optimum combination was depth of cut of 1u2009mm, feed rate of 0.05u2009mm/rev, cutting speed of 308u2009m/min, and number of insert of 1 to minimize simultaneously RMS, Fx, and Ra. It was obtained that the percentage improvement in gray relational grade with the multiple responses is 42.9%. It is clearly shown that the performance indicators are significantly improved using this approach in milling of AISI 1050 steel. Moreover, analysis of variance for gray relational grade proved that the feed rate is the most influential factor as the minimization of all responses is concurrently considered.

Optimization and predictive modeling using S/N, RSM, RA and ANNs for micro-electrical discharge drilling of AISI 304 stainless steel

In present work, micro-deep holes on AISI 304 stainless steel were drilled via electrical discharge machining (EDM) method. In the first phase of this work, the effect of test parameters on the drilling performance and the profile of drilled holes were investigated experimentally. Test parameters including discharge current, dielectric spray pressure and electrode tool rotational speed were taken and then the machining rate (MR), electrode wear rate (EWR), average over-cut (AOC) and taper angle (TA) were measured in order to assess the drillability of EDM. After experimental study, an analysis of variance was performed to identify the effect of the importance of test parameters on experiment outputs. In the second phase of this study, optimum process parameters were determined using signal-to-noise analysis and response surface methodology (RSM) for mono-optimization and multi-response optimization, respectively. In the last phase, regression analysis and artificial neural network (ANN) models for predicting the MRR, EWR, AOC and TA. As a result of experimental analysis, discharge current was the most important parameter for micro-drilling with EDM. It was found out that this parameter influenced positively MR, while it has negatively an effect on EWR, AOC and TA. Mathematical model based on ANNs exhibited a successful performance for predication of outputs. Optimum process parameters which were discharge current of 10.18xa0Å, dielectric liquid pressure of 58.78xa0bar and electrode tool rotational speed of 100xa0rpm for multi-objective optimization were determined through RSM with desirability function analysis in micro-deep hole EDM drilling of AISI 304 stainless steel.

The Analysis of Process Parameters for Turning Cobalt-Based Super Alloy Haynes 25 / L 605 Using Design of Experiment

Haynes-25 alloy (also known as L-605 alloy) is extensively used in the applications of aerospace industry, turbine and furnace parts, power generators and heat exchangers and petroleum refining components due to its excellent properties. However, machining this alloy is more difficult compared to normal steel or even stainless one because of its characteristics of hardness and strength. This paper presents experimental investigation into machining parameters in the turning process of Haynes 25 alloy using uncoated carbide tools. Design of experiment (DOE) has been used for studying the effect of the main turning parameters such as cooling condition, cutting speed and feed rate on the arithmetic average surface roughness (Ra) of Haynes-25 alloy. Tests are designed according to Taguchi’s orthogonal array. Experiments have been performed under dry cutting and conventional wet cooling. Minimum surface roughness was obtained in turning using uncoated tools under wet cooling condition at the cutting speed of 45 m/min and feed rate of 0.12 mm/rev.

Investigation of deep-drilled micro-hole profiles in Hadfield steel

Abstract Hadfield steel, due to its high manganese content, is difficult to drill and work-hardens very quickly. In this study, Hadfield steel material was drilled with micro-size deep holes using the electrical discharge machining (EDM) technique, and hole diameter values were examined for specific machining parameters. Experiments were carried out with three different discharge currents (6, 12 and 24 A), three different electrode rotational speeds (200, 400 and 600 rev × min−1), three different pulse durations (12, 50 and 100 µs), a fixed dielectric spray pressure (40 bars) and a fixed pulse interval (3 µs). It was determined that the hole profiles obtained following the tests are directly related to the machining parameters, and that the resulting average overcut (AOC) and taper (Tp) values increased with discharge current, electrode rotational speed and pulse duration. Analysis of variance (ANOVA) conducted demonstrates that pulse duration is the dominant parameter affecting AOC, whereas pulse duration has the highest effect on Tp. When determination coefficients and normal probability plots were compared for the mathematical models obtained from analyses conducted for the prediction of test values, it was observed that the models obtained by quadratic regression analysis exhibited a better performance than the models produced by linear regression analysis.

Optimization of the wear behavior of uncoated, TiN and AlTiN coated cold work tool steel 1.2379 using response surface methodology

Abstract In this study, the wear behavior of uncoated, TiN and AlTiN coated cold work tool steel 1.2379 which is widely used in the mold industry was investigated experimentally. Heat treatment was applied to the specimens, then TiN and AlTiN PVD coating process was performed. The wear tests were carried out at 0.5 m × s−1 sliding speed, 5, 10 and 15 N loads and 120 m sliding distance by using reciprocating abrasion device. The microhardness was measured and metallographic tests of the samples were investigated by SEM and EDS analysis. In order to examine the effect of process parameters on wear results, a statistical method such as analysis of variance (ANOVA) was employed. A mathematical model was created by using regression analysis based on both linear model and quadratic model for predicted wear value. Process parameters were optimized using response surface methodology with desirability function analysis. It is observed that the uncoated specimens worn approximately two times more than AlTiN coated and one time more than TiN coated. In the SEM images and EDS analysis, it is seen that the coating is spread uniformly on the materials.