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EN
Squeeze casting has emerged as an attractive alternative for the casting of aluminum alloys to boost the mechanical and microstructural attributes. However, the alloys practiced in structural applications where ductility is considered a key characteristic, additional heat treatment processes are opted after casting. Considering the industrial applications of Al7050, the current study focused on manufacturing defect-free casting for structural applications. For this purpose, three key process variables including squeeze pressure (SP), melt temperature (MT) and die temperature (DT) have been preferred to improve the percentage elongation, ultimate tensile strength and hardness with minimal casting defects. Annealing treatment is preferred to further advance the ductile behavior of the squeeze-casted Al7050 alloy. Among different process variables, SP has a significant contribution in raising the mechanical properties followed by MT and DT. Taguchi-based Grey relational analysis (GRA) has been used to attain the optimal level of input parameters (SP = 135 MPa, MT, 740 °C and DT = 240 °C) for the superior microstructural and mechanical attributes simultaneously. Microstructural investigations revealed that application of high SP and DT with reasonable MT significantly improved the grain structure and minimized the typical casting defects including micro-voids, porosity and shrinkage cavities. Annealing treatment has been observed productive for improving ductility and reducing the casting defects specifically micro-porosity.
EN
Throughout the geological history of the earth, there have been many climate changes due to natural and external factors. In the past, the changes in climate were caused by natural causes, and today it is primarily caused by human activities. Besides being diferent climate types, Turkey is among countries that will be afected by climate change induced by global warming. Climate changes in the regions will be afected diferently and degrees due to the country’s surroundings by seas, fragmented topography and orographic features. Trend analysis methods are used in many areas such as on various engi neering, agriculture, environmental and water resources, especially in climate change impact studies resulting from global warming. When data are analyzed with classical trend analysis methods, forward-looking predictions are generally made as low, medium, high, decreasing and increasing. However, risk classes showing changes between available data sets are not known. Innovative Trend Pivot Analysis Method (ITPAM) determines risk classes by establishing a relationship between data. Furthermore, in this method, increasing and decreasing trend regions are separated into fve classes more clearly than classical/traditional trend methods. In this study, Susurluk Basin’s total monthly precipitation data (2006–2017) were analyzed by using ITPAM which the newest trend method. When arithmetic mean analysis results are examined, a signifcant change is observed between frst data set and second data set at two stations (Bandirma and Uludag). When examined at other stations, it is observed that at least one month of almost every station is in 1st degree risk group. When standard deviation analysis results of each station are examined, a signifcant change is observed between frst data set and second data set at many stations. Because while trend class of a point in developed IPTA graph is the medium degree, this point is in 1st risk class in the risk graph.
EN
Climate change is an event that has significant effects as direct or indirect on ecosystem and living things. In order to be prepared for the effect of climate change, it is necessary to anticipate these changes and take measures for this change. Therefore, many studies have been carried out on changes in climate parameters in recent years. The most common method used in these studies is trend methods. Innovative Polygon Trend Analysis (IPTA) and Trend Polygon Star Concept are trend analysis methods. IPTA Method divides data series into two as first and second data set and analyzes these two data sets by comparing them with each other. Trend Polygon Star Concept analyzes distance between two months in data set in graph, which is result of IPTA, and shows analysis result by dividing it into four regions. Therefore, in this study, monthly average temperature data are analyzed by using this two-polygon method. This data set is for 22 years (1996–2017). Polygon graphics were created as a result of study. Besides, trend slopes and lengths of temperature data with IPTA Method were calculated. The values of graphs created with Trend Polygon Star Concept Method on x- and y-axis were given in a table. When the results of both analysis methods were examined for a station, the following results were observed. For example, a regular polygon was not seen in arithmetic mean and standard deviation graphs of IPTA Method of Bandirma Station. Besides, when general evaluation of arithmetic mean analysis results was examined an increasing trend in most months. When arithmetic average graph created by Trend Polygon Star Concept Method of Bandirma Station was examined, transition between two months was seen first and third region. When standard deviation graph was examined, transitions between two months were seen in all four regions.
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