In this study, Artificial Neural Network approach to prediction of diffusion bonding behavior of Ni-Ti alloys, manufactured by powder metallurgy process, were obtained using a back-propagation neural network that uses gradient descent learning algorithm. Ni-Ti composite manufactured with a chemical composition of 51 % Ni-49 % Ti in weight percent as mixture with a average dimension of 45µm. Diffusion welding process have been made under argon atmosphere, with a constant load of 5 MPa, under the temperature of 850, 875, 900 and 925ºC and, in 20, 40 and 60 minutes experiment time. Microstructure examination at bond interface were investigated by optical microscopy, SEM and EDS analysis. Specimens were tested for shear strength and metallographic evaluations. After the completion of experimental process and relevant test, to prepare the training and test (checking) set of the network, results were recorded in a file on a computer. In neural networks training module, different temperatures and welding periods were used as input, shear strength of bonded specimens at interface were used as outputs. Then, the neural network was trained using the prepared training set (also known as learning set). At the end of the training process, the test data were used to check the system accuracy. As a result the neural network was found successful in the prediction of diffusion bonding shear strength and behavior.

In this study, X-ray radiographic tests of Ti6Al4V alloys welded by plasma tungsten arc welding (PTA) were investigated. PTA welding experiments were carried out under argon shielding gas atmosphere, at 1400–1600 W and 1800 W welding powers as well as 1 m/min, 0.75 m/min, and 0.50 m/min welding speeds. After this process, radiography of the welded joints was performed by X-ray diffraction. The result of the radiographic tests indicated that by increasing welding power the widths of deep penetration increased in all specimens. On the contrary, increasing welding speeds decreases the widths deep penetration. The best properties of Ti6Al4V joints were observed for specimens welded at 1800 W welding power and at 0.50 m/min welding speed.

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The competitive environment observed in the international construction sector has reflected in the Turkish construction sector through joint projects. In order to achieve competitive advantage in joint construction projects and to achieve success against national competitors, it is necessary to review resource selection strategies. In this context, the main purpose of this study is to look at the resource selection criteria of the construction companies in terms of International Resource-Based Theory. The 77 resources of the construction firms reviewed during this study were evaluated by taking into consideration their ability to be strategic resource and their competitive advantages. In this study, the Building Information Modelling (BIM) tools and technique, which has been spoken as a resource that will benefit competition in Turkey in recent years, was discussed as an objective and the research problem was whether BIM is a strategic resource or not. In the resource evaluation process, resources should be listed and evaluated by firm employees and managers. In the survey conducted for this purpose, construction firms were asked to select the resources they already had from the resource pool of the research and to score only 9 important resources. To establish vertical hierarchy and horizontal relationships, the obtained results of the evaluation were analyzed by using the Analytical Network Process (ANP) method. In the established hierarch, the objective was BIM and the selection criteria were VRIO criteria including Valuable, Rare, Inimitable and Organization, which are the resource selection criteria of the Resource-Based Theory. The scores obtained as a result of the survey study applied to the Turkish construction firms were reflected to the ANP technique. While the data processed with the Super Decisions software provides numerical and quantitative comparisons of resources in the construction sector, it also points to a selected set of resources that can work with BIM.