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© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.The surface of the commercial CoCr28Mo alloy was textured using a 20W pulsed fiber laser according to different beam scan strategies. Surface roughness and drop contact angle measurements made on the textured surface were used to evaluate the effects of process parameters. The surface topography was highly affected by both scan direction and beam overlaps named pulse-to-pulse overlap and scan overlap. As the beam overlap ratio increased, the amount of heat transferred to the unit area also increased, which caused more metal to evaporate in the interaction zone and form a more considerable amount of molten and re-solidified metal layer, resulting in a chaotic texture formation on the surface. The mean surface roughness values of textured surfaces for eighteen test samples ranged from 1 to 7 μm. These surface roughness values were in the micro-scale roughness value range. According to the results of the drop contact angles, the surface topography significantly changed the wettability. The test results were also analyzed by the Taguchi method considering signal-to-noise ratios and evaluated by analysis of variance. Considering the highest S/N values for each parameter, the optimum parameters combination for the laser engraving process was 45°/-45° for scan direction, 70 % for power, 1000 mm/s for scan speed, 50 kHz for frequency, 0.01 mm for hatch distance, and 75 ns for pulse duration.

In this study, real-time detection of defects that may occur in rail components will be investigated with machine learning algorithms. This research aims to detect the faults that will occur in the railway components early and to prevent accidents and losses in advance. The study was carried out by detecting the faults of basic parts such as rail fractures, lack of fastener, lack of rail fixing clamp, and lack of screws. Multiple images will be used in our application for training machine learning algorithms. Defective or non-defective images will be classified by a deep learning network. In other words, our deep learning network will detect incoming test images contactless and in real-time. In our study, appropriate methods and success metrics are given and the results are explained comparatively.

Yüksek Lisans Tezi, Bu çalışmada LNG'nin soğuk enerjisini kullanan yeni bir birleşik güç sistemi Marmara Ereğlisi'nde bulunan BOTAŞ LNG alım terminali için önerilmiştir. Önerilen sistemde üst çevrimi Brayton çevrimi alt çevrimleri ise süper kritik CO2 çevrimi (S-CO2)ve organik Rankine çevrimi (ORÇ) oluşturmaktadır. Birleşik güç sisteminin enerji, ekserji ve eksergo ekonomik analizi gerçekleştirilmiş ve sistemin optimum noktaları bulunmuştur. Kabul edilen tasarım girdileri ile elde edilen çalışma koşulları, optimizasyon sonucunda bulunan çalışma koşulları ile kıyaslandığında %67 değerinde bir maliyet azalması olduğu görülmüştür., In this study, a new combined power system using LNG's cold energy was proposed for BOTAŞ LNG reception terminal in Marmara Ereğlisi. In the proposed system, the upper cycle is Brayton cycle and the sub cycle consists of supercritical CO2 cycle (S- CO2) and organic Rankine cycle (ORC). The energy, exergy and exergo economic analysis of the combined power system was performed and the optimum points of the system were found.When the working conditions obtained with the accepted design inputs and the working conditions as a result of the optimization were compared,a costreduction of %67 was observed.