Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/1202
  Modeling and analysis of WEDM with a new deep neural network

  Mahmat, Aybars; Dogan, Gurkan; Ergen, Burhan; Tosun, Nihat

  In some cases, the models targeted in machining processes are difficult to apply to industrial production. Artificial intelligence techniques applied to industrial production methods in recent times eliminate these problems in machining. The use of deep learning methods in solving these problems has recently increased. In this study, a new deep neural network (DNN) based on artificial neural networks is proposed to model the machining of AISI4140 steel by wire electrical discharge machining (WEDM). The proposed DNN model predicts surface roughness (SR) and wire wear ratio (WWR), which are the most important inferences in cutting with WEDM. Obtaining inferences such as SR and WWR in a real experimental environment means a lot of time and cost. With the proposed model, there is a significant reduction in time and cost factors. The dataset used to train and test the proposed model was obtained from the real experimental environment. For training and testing the model, the data set is divided into two parts: 20% test data and 80% training data. In the experimental tests carried out with the proposed model, error rates such as Mean Absolute Error (MAE), R2, and Mean Squared Error (MSE) accuracy rate between SR and WWR values estimated according to the SR and WWR values obtained from the real environment were examined. MAE, MSE, and R2 for SR reached 0.0495, 0.0005, and 98.48%, respectively, while MAE, MSE, and R2 for WWR reached 0.0156, 0.0036, and 98.39%, respectively. These results of the proposed model provided a lower error rate and higher accuracy than the state-of-the-art models in the literature.

  AtıfScopus5·WoS5·OpenAlex7

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• MakaleYalnız künye2025Scopus SJRQ2/r/munzur/1196
  Experimental comparison of conventional and ultrasonic-assisted high-speed drilling of glass fiber reinforced epoxy composites

  Ağar, Salih; Mahmat, Aybars; Doğan, Akar

  Glass fiber reinforced epoxy composites are favored in several industries, including automotive and aerospace, owing to their superior strength-to-weight ratio. Drilling is a prevalent machining procedure employed to add fasteners for laminate assembly. Nonetheless, drilling these composites presents significant challenges due to the abrasive characteristics of the reinforcement, necessitating an examination of machining performance. During the drilling of composites, cutting temperature, thrust force, and delamination damage are primary concerns. In recent years, high-speed ultrasonic drilling, an innovative machining technique designed to address these challenges, has garnered significant interest. This investigation involved high-speed ultrasonic drilling and conventional drilling of glass fiber reinforced epoxy material. In the studies, optimal conditions were studied by evaluating thrust force, cutting temperature, and delamination damage by varying speeds, feed rates, and frequencies. The thrust force was 11 N in the traditional approach at 40,000 rpm, but it was recorded at 9 N with the ultrasonic method. Moreover, with ultrasonic drilling, cutting temperatures decreased by over 25% relative to traditional drilling, attributable to the harmonic motion that dissipates heat in the drilling zone. Analysis of the drilling region revealed a notable enhancement in damage, particularly in delamination, with the application of ultrasonic drilling. The experimental results indicate that ultrasonic machining enhances machining performance.

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  İncele
• MakaleAçık Erişim2024Scopus SJRQ2/r/munzur/2311
  Effects of Ga, Nb, V, Zr, and Cr on the crystal structures and thermal behavior of CuAlMn shape memory alloys

  Özkul, İskender; Karaduman, Oktay; Canbay, Canan Aksu

  CuAlMn alloys are cheaper and more promising alloy types compared to NiTi alloys. In this study, based on CuAlMn shape memory alloy, thermodynamic and structural characteristics of some new alloys containing four elements were investigated in detail. In this study, five new quaternary shape memory alloys, each containing a CuAlMn base alloy and a different fourth additive element (Ga, Nb, V, Zr, and Cr), were produced. The properties of the obtained alloys were characterized using a series of analysis methods such as X-ray diffraction (XRD), optical microscopy, differential scanning calorimetry (DSC), and differential thermal analysis (DTA). The results showed the effects of different elements on the crystal structures of the alloys and how different elements could change the thermal behavior of the alloys.

  AtıfScopus4·WoS3·OpenAlex6

  İncele
• MakaleYalnız künye2021Scopus SJRQ2/r/munzur/3062
  High temperature effect on quasi-static and low velocity impact behaviors of advanced composite materials

  Karakuzu, Ramazan; Djele, Akim; Dogan, Akar

  In this study, the effect of ambient temperatures on the punch shear and low-velocity impact behavior of S2 glass fabric and carbon-kevlar hybrid fabric reinforced epoxy composites was investigated. The quasi-static tests (QST) were performed by using a Universal Shimadzu Tensile Testing Machine with various crosshead speeds. Low-velocity impact (LVI) tests were also carried out by using the CEAST Fractovis Plus impact test machine at different impact energies. Flat-nose impactors with a cylindrical punch diameter of 12.70 mm were mounted to the test machines. Both tests have been carried out at various temperatures. After the experiments, the load-displacement, the absorbed energy-impact energy/crosshead speed and punch shear strength graphs are plotted. From the obtained results it can be said that, in penetration cases of low-velocity impact and quasi-static tests, absorbed energy and punch shear strength values show similar behavior for hybrid carbon-kevlar/epoxy at each temperature except for 80 °C while these values are different from each other for S2 glass/epoxy at all temperatures.

  AtıfScopus16·WoS14·OpenAlex14

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