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In this study, the surface of AISI H13 steel was borided with powder blends of B4C and NaBF4 using the powder-pack method at 800, 900 and 1000 °C for 2, 4 and 6 h. The structural and mechanical characteristics of the boride layers formed on the surface were characterized using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffractometry, 2D surface profilometry, microhardness and electrochemical corrosion (3.5 wt% NaCl) tests. The boride layer exhibited a single phase structure (Fe2B) in samples coated at 800 °C and a dual-phase structure (FeB + Fe2B) at higher boriding temperatures (900 and 1000 °C). The boride layers were compact and crack-free in all boriding conditions. Depending on boriding parameters, the thickness, hardness and average surface roughness (Ra) of the coatings were found to range between 5.81 and 102.46 µm, 1635–1915 HV and 0.315–0.650 µm, respectively. The borided AISI H13 steel displayed up to 33.5 times and 2.4 times higher corrosion resistance than untreated AISI H13 steel and martensitic AISI 431 steel, respectively. This suggests potential use of borided AISI H13 steel in the steam turbines and marine applications as an alternative to the more costly martensitic and duplex stainless steel grades. The corrosion resistance depended on the phase structure (single- or dual-layer), density, thickness and surface roughness of the boride coatings.Graphic Abstract

Modern condition monitoring-based methods are used to reduce maintenance costs, increase aircraft safety, and reduce fuel consumption. In the literature, parameters such as engine fan speeds, vibration, oil pressure, oil temperature, exhaust gas temperature (EGT), and fuel flow are used to determine performance deterioration in gas turbine engines. In this study, a new model was developed to get information about the gas turbine engine’s condition. For this model, multiple regression analysis was carried out to determine the effect of the flight parameters on the EGT parameter and the artificial neural network (ANN) method was used in the identification of EGT parameter. At the end of the study, a network that predicts the EGT parameter with the smallest margin of error has been developed. An interface for instant monitoring of the status of the aircraft engine has been designed in MATLAB Simulink. Any performance degradation that may occur in the aircraft’s gas turbine engine can be easily detected graphically or by the engine performance deterioration value. Also, it has been indicated that it could be a new indicator that informs the pilots in the event of a fault in the sensor of the EGT parameter that they monitor while flying.

In this study, AISI 304 austenitic stainless steel surface was boronized with nanoboron and ekabor-III powders at 950 and 1000°C for 2 and 4 hours period by solid-state box boronizing method. Then, behaviors of the boronized specimen in the microstructure, three-point bending, and tensile strength characteristics were investigated. As a result of the boriding process, the boride layer thickness in the range of 23–67 µm and microhardness value in the range of 1020–2200 HV have been obtained according to the increase in processing time and temperature and to the particle size of the boron source (0, 1). The coating layer on boronized specimens did not exhibit any sign of reaction caused by the tensile strength applied until the yield point was in both tests. Although the particle size of the boron agents was more effective on the boronized specimen’s bending and tensile strength behaviors, it was observed that processing temperature and its duration are effective as well.

Purpose – The paper aims to clarify the effect of boriding on the corrosion behavior and mechanical properties of AISI 304 austenitic stainless steel. Design/methodology/approach – The commercially available steel was subjected to a boriding treatment with Ekabor III powders at temperatures of 1,223-1,273 K with boriding durations of 2-4 h. Microstructural characterization of the steel was carried out with optical microscopy, scanning electron microscopy and X-ray diffraction analyses. Static immersion corrosion tests were made using a 10 percent H2SO4 acid solution and salt spray tests were carried out in accord with the ASTM B-117 standard. Findings – Grain boundary precipitation of carbides was observed in the transition zone beneath the boride layers. The corrosion resistance of the steel against the acid solution increased to about seven times its untreated value with the boriding treatment. Research limitations/implications – The boride coating improved the corrosion resistance of the AISI 304 stainless steel against acidic media, but suffered from spalling in the salt spray test. Future work will focus on improving the adhesion between the coating and the substrate by changing the parameters for the boriding process. Practical implications – Pack boriding is a simple, environmentally friendly coating process and can be recommended for use in small and medium enterprises. The boride coatings deposited have potential in further improving the wear and corrosion resistance of stainless steels. Originality/value – The outcome of the research is of great importance for the industry using wear- and corrosion-resistant coatings.

Purpose With the condition monitoring system on airplanes, failures can be predicted before they occur. Performance deterioration of aircraft engines is monitored by parameters such as fuel flow, exhaust gas temperature, engine fan speeds, vibration, oil pressure and oil temperature. The vibration parameter allows us to easily detect any existing or possible faults. The purpose of this paper is to develop a new model to estimate the low pressure turbine (LPT) vibration parameter of an aircraft engine by using the data of an aircraft’s actual flight from flight data recorder (FDR). Design/methodology/approach First, statistical regression analysis is used to determine the parameters related to LPT. Then, the selected parameters were applied as an input to the developed Levenberg–Marquardt feedforward neural network and the output LPT vibration parameter was estimated with a small error. Analyses were performed on MATLAB and SPSS Statistics 22 package program. Finally, the confidence interval method is used to check the accuracy of the estimated results of artificial neural networks (ANNs). Findings This study shows that the health conditions of an aircraft engine can be evaluated in terms of this paper by using confidence interval prediction of ANN-estimated LPT vibration parameters without dismantling and expert knowledge. Practical implications With this study, it has been shown that faults that may occur during flight can be easily detected using the data of a flight without expert evaluation. Originality/value The health condition of the turbofan engine was evaluated using the confidence interval prediction of ANN-estimated LPT vibration parameters.

During the takeoff phase, aircraft engines reach maximum speed and temperature to achieve the required thrust. Due to these harsh operating conditions, the performance of aircraft engines may decrease. This decrease in performance increases both fuel consumption and environmental damage. Reducing or eliminating the damages caused by aircraft is among the objectives of ICAO. In order to achieve this goal, aircraft engines are compulsorily tested, evaluated by experts and certified. The data obtained during the test process is recorded and stored in the engine emission databank (EEDB). During the takeoff phase, there is no system that can evaluate aircraft engines without dismantling and without expert knowledge. In this study, EEDB 2019 and 2021 takeoff phase data sets were used. Fuel flow T / O parameter is an important parameter used both in the calculation of aircraft emissions and in the evaluation of engine performance. Gaussian process regression (GPR), support vector machine (SVM) and multilayer perceptron (MLP) models were used to estimate the fuel flow T / O parameter. The results obtained were compared according to error performance criteria and the best model was selected. In MATLAB ® environment, confidence intervals were plotted with the estimated fuel flow T / O value at 99% confidence level. This study demonstrates that the performance evaluation of aircraft engines during the takeoff phase can be performed without the need for expert knowledge.

Planned maintenance is required by licensed maintenance organizations to detect and prevent performance degradation in aircraft engines. In the literature, engine performance is evaluated with parameters that show engine performance. Fuel flow parameter is one of the important parameters that shows engine performance. In the models developed earlier, no engine performance evaluation was made with the fuel flow parameter at all stages from the take-off to the landing of the aircraft. In this study, fuel flow parameter is estimated with over 99.9% accuracy by using artificial neural network in MATLAB ® software. In order to detect the engine performance deterioration of the aircraft, the fuel flow values obtained from the artificial neural network and confidence intervals with 99% confidence level were established. Each value taken from the fuel flow sensor is evaluated by the model in all flight phases. In the model, engine performance is considered normal if the fuel flow value is within the confidence interval, and abnormal (anomaly) if it is outside the confidence interval. An accuracy of over 99.9% was achieved and results of this study showed that fuel flow rate of the engine of interest was within the confidence interval (no performance deterioration).

In the present study, the metallographic, mechanical and tribologic behaviors of AISI D2 steel specimens coated with TiC through the titanizing process were investigated. The titanizing treatment was performed at the temperatures of 900°C, 1000°C or 1100°C for 1 h, 2 h or 3 h using a solid-state box thermoreactive diffusion technique. In all cases, the predominant phase in the coating was TiC, but the mechanical properties of the coating varied with treatment condition. The wear resistance of the coated samples against a linear reciprocating Al 2 O 3 ball improved as the hardness and thickness of the coating increased. The effective wear mechanism of samples that had been treated at 900°C, 1000°C and 1100°C were severe plastic deformation, delamination and polishing type wear, respectively. The wear performance was affected by coating layer’s thickness and uniformity as well as its surface hardness, elastic modulus and toughness.

Adequate lymph node evaluation is required for proper staging of colorectal cancer, and the number of lymph nodes examined is associated with survival. In this study, we aimed to evaluate the factors affecting the number of lymph nodes retrieved from specimens of patients operated for colorectal cancer. Medical records of 320 consecutive patients with colorectal cancer were evaluated retrospectively whom had curative resection between 2002 and 2007. Variables such as age, gender, tumor localization, depth of tumor invasion, number of lymph nodes retrieved, specimen length, stage and grade of disease, type of surgery, primary/recurrence disease’ presence of preoperative chemo radiotherapy (CRT), surgeon, staff surgeon, pathologist, and staff pathologist were recorded and the results were evaluated statistically. Mean number of lymph nodes retrieved was 14.98 (0 to 129) and mean metastatic lymph node number was 2.37(0-25). Tumor localization, staging, primary/recurrence disease, length of specimen, type of operation, pathologist (resident pathologist), staff pathologist, staff surgeon, presence of CRT, affected statistically significiant in terms of lymph nodes harvested (p≤0.05). The hypothesis that disease recurrence occurred due to inaccurate staging. Maximal attention should be paid while doing oncologic surgery and should be paid to the total number of lymph nodes retrieved.

In this study, corrosion behavior and mechanical properties of AISI 304 austenitic stainless steel, which was borided with Nanoboron powder, was investigated. The commercially available steel was subjected to a boriding treatment with a size of 10–50 nm Nanoboron powders, at the temperatures of 1223 K to 1273 K with boriding durations of 2 to 4 h. Microstructure characterization of the steel was carried out with optical microscopy, scanning electron microscopy, microhardness and X-ray diffraction analyses. Corrosion tests were made by static immersion into a 10% H 2 SO 4 acid solution and weight loss calculations as well as salt spray tests were carried out in accord with the ASTM B-117 standard. Boriding thermal treatment, increased the corrosion resistance of the steel against the acid solution, up to about 4.3 times while in the salt spray tests, weight loss corrosion resistance increased up to tier 2. However, anti-corrosion resistance decreased by 40%, its untreated value.