Hakan Dilipak

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 (21 × 33) 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 1 mm, feed rate of 0.05 mm/rev, cutting speed of 308 m/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.

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.