Adem Çiçek

Optimization of drilling parameters using Taguchi technique and response surface methodology (RSM) in drilling of AISI 304 steel with cryogenically treated HSS drills

In this study, the effects of cryogenic treatment and drilling parameters on surface and hole quality were investigated in the drilling of AISI 304 stainless steel under dry drilling conditions. The control factors to provide better surface roughness (Ra) and roundness error (Re) were determined using the Taguchi method. RSM was also used to determine interactions among the control factors. In addition, analysis of variance was employed to determine the most significant control factors on the surface roughness and roundness error. Three drill categories (conventional heat treatment—CHT, cryogenic treatment—CT, cryo-tempering—CTT), cutting speeds, and feed rates were considered as control factors, and an

Prediction of Damage Factor in end Milling of Glass Fibre Reinforced Plastic Composites Using Artificial Neural Network

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ANN and multiple regression method-based modelling of cutting forces in orthogonal machining of AISI 316L stainless steel

L27 full factorial design with a mixed orthogonal array was selected for experimental trials. As a result, it was found that the feed rate and cutting speed were the most significant factors on the surface roughness and roundness error with percentage contributions of 83.07 and 64.365xa0% respectively. The predictive quadratic models were derived by the RSM to obtain the optimal surface roughness and roundness error as a function of drilling parameters and heat treatments applied to the drills.

Effects of Deep Cryogenic Treatment on the Wear Resistance and Mechanical Properties of AISI H13 Hot-Work Tool Steel

Exp. (experimental) and num. (numerical) cutting forces were obtained by the exp. studies and FEM analysis.The best convergence between exp. and num. cutting forces was provided.Num. cutting temperatures were predicted by ANN within very low error interval.Exp. cutting temperatures were obtained using exp. cutting forces.Exp. temperature results were quite satisfactory. In this study, an approach based on artificial neural network (ANN) was proposed to predict the experimental cutting temperatures generated in orthogonal turning of AISI 316L stainless steel. Experimental and numerical analyses of the cutting forces were carried out to numerically obtain the cutting temperature. For this purpose, cutting tests were conducted using coated (TiCN+Al2O3+TiN and Al2O3) and uncoated cemented carbide inserts. The Deform-2D programme was used for numerical modelling and the Johnson-Cook (J-C) material model was used. The numerical cutting forces for the coated and uncoated tools were compared with the experimental results. On the other hand, the cutting temperature value for each cutting tool was numerically obtained. The artificial neural network model was used to predict numerical cutting temperatures by means of the numerical cutting forces. The best results in predicting the cutting temperature were obtained using the network architecture with a hidden layer which has seven neurons and LM learning algorithm. Finally, the experimental cutting temperatures were predicted by entering the experimental cutting forces into a formula obtained from the artificial neural networks. Statistical results (R2, RMSE, MEP) were quite satisfactory. This demonstrates that the established ANN model is a powerful one for predicting the experimental cutting temperatures.

3D numerical modelling of micro-milling process of Ti6Al4V alloy and experimental validation

Glass fibre reinforced plastic (GFRP) composites are an economic alternative to engineering materials because of their superior properties. Some damages on the surface occur due to their complex cutting mechanics in cutting process. Minimisation of the damages is fairly important in terms of product quality. In this study, a GFRP composite material was milled to experimentally minimise the damages on the machined surfaces, using two, three and four flute end mills at different combinations of cutting parameters. Experimental results showed that the damage factor increased with increasing cutting speed and feed rate, on the other hand, it was found that the damage factor decreased with increasing depth of cut and number of the flutes. In addition, analysis of variance (ANOVA) results clearly revealed that the feed rate was the most influential parameter affecting the damage factor in end milling of GFRP composites. Also, in present study, Artificial Neural Network (ANN) models with five learning algorithms were used in predicting the damage factor to reduce number of expensive and time-consuming experiments. The highest performance was obtained by 4-10-1 network structure with LM learning algorithm. ANN was notably successful in predicting the damage factor due to higher R2 and lower RMSE and MEP.

Application of Deep Cryogenic Treatment to Uncoated Tungsten Carbide Inserts in the Turning of AISI 304 Stainless Steel

In this study, the effects of deep cryogenic treatment (DCT) on the wear resistance of AISI 52100 bearing steel were investigated. For this purpose, a number of bearing steel samples were held for different times (12, 24, 36, 48, 60xa0h) at deep cryogenic temperatures (−145xa0°C). The wear experiments were carried out in a ball–disk arrangement, by applying loads of 10 and 20xa0N and a sliding velocity of 0.15xa0m/s. After conducting the experimental studies, 36xa0h was found to be the optimal holding time. At this holding time, the wear rate and friction coefficient were decreased, while the hardness reached to maximum values. It was observed that DCT led to significant microstructural changes, which resulted in improved tribological properties.

Inconel 718 süperalaşımının delinmesinde kriyojenik soğutmanın delme performansı üzerine etkilerinin araştırılması

AbstractnIn this study, predictive modelling was performed for the cutting forces generated during the orthogonal turning of AISI 316L stainless steel. An artificial neural network (ANN) and a multiple regression analysis were utilised. The input parameters of the ANN model were the cutting speed, feed rate and coating type. In the model, tungsten carbide cutting tools, uncoated and with two different coatings (TiCNxa0+xa0Al2O3xa0+xa0TiN and Al2O3), were used. The ANN predictions closest to the experimental cutting forces were obtained for the main cutting force (Fc) and the feed force (Ff) by 3-7-1 and 3-6-1 network architectures with a single hidden layer, respectively. While the SCG learning algorithm provided the optimal results for Fc, the optimal results for Ff were provided by the LM learning algorithm. A very good performance of the neural network, in terms of agreement with the experimental data, was achieved. With the developed model, the cutting forces could be precisely predicted depending on the cutting speed, feed rate and coating type. The prediction results showed that the ANN was superior to the multiple regression method in terms of prediction capability.

Prediction of engine performance and exhaust emissions for gasoline and methanol using artificial neural network

In this study, a number of wear and tensile tests were performed to elucidate the effects of deep cryogenic treatment on the wear behavior and mechanical properties (hardness and tensile strength) of AISI H13 tool steel. In accordance with this purpose, three different heat treatments (conventional heat treatment (CHT), deep cryogenic treatment (DCT), and deep cryogenic treatment and tempering (DCTT)) were applied to tool steel samples. DCT and DCTT samples were held in nitrogen gas at −145xa0°C for 24xa0h. Wear tests were conducted on a dry pin-on-disk device using two loads of 60 and 80xa0N, two sliding velocities of 0.8 and 1xa0m/s, and a wear distance of 1000xa0m. All test results showed that DCT improved the adhesive wear resistance and mechanical properties of AISI H13 steel. The formation of small-sized and uniformly distributed carbide particles and the transformation of retained austenite to martensite played an important role in the improvements in the wear resistance and mechanical properties. After cleavage fracture, the surfaces of all samples were characterized by the cracking of primary carbides, while the DCT and DCTT samples displayed microvoid formation by decohesion of the fine carbides precipitated during the cryo-tempering process.