Yazar "Orak, Ilhami M." seçeneğine göre listele

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    Artificial neural network application for modeling the rail rolling process
    (Pergamon-Elsevier Science Ltd, 2014) Altinkaya, Huseyin; Orak, Ilhami M.; Esen, Ismail
    Rail rolling process is one of the most complicated hot rolling processes. Evaluating the effects of parametric values on this complex process is only possible through modeling. In this study, the production parameters of different types of rails in the rail rolling processes were modeled with an artificial neural network (ANN), and it was aimed to obtain optimum parameter values for a different type of rail. For this purpose, the data from the Rail and Profile Rolling Mill in Kardemir Iron & Steel Works Co. (Karabuk, Turkey) were used. BD1, BD2, and Tandem are three main parts of the rolling mill, and in order to obtain the force values of the 49 kg/m rail in each pass for the BD1 and BD2 sections, the force and torque values for the Tandem section, parameter values of 60, 54, 46, and 33 kg/m type rails were used. Comparing the results obtained from the ANN model and the actual field data demonstrated that force and torque values were obtained with acceptable error rates. The results of the present study demonstrated that ANN is an effective and reliable method to acquire data required for producing a new rail, and concerning the rail production process, it provides a productive way for accurate and fast decision making. (C) 2014 Elsevier Ltd. All rights reserved.
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    Designing workshop management system supporting decision making with OODB
    (Elsevier Science Bv, 2012) Orak, Ilhami M.; Yaman, Beyza; Guerrini, Giovanna
    In this study, a workshop production management system supporting decision making is designed for use in small and medium-sized machine workshops. Small and medium sized companies' quality, price, and delivery time issues are in a tough competitive environment. Especially such as the orders which cannot be delivered to customers in time, disruptions in production schedules and low production performance seem to be the major problems facing in this type of sector. Through this research it is aimed to design software to keep all the needed information in a suitable database and give the customers fast and accurate information. In order to support future implementation of decision support system, the software is designed such that it will enable the user to easily analyze its production parameters such as efficiency, cost deviation of production from planned cost, cost estimation of new production, etc. Due to the natural structure of this project Object Oriented database is chosen for implementation. These databases can be constructed through object-oriented language and in the scope of this work. NET based db4o is used. For software development, Unified Modelling Language (UML) is used since by using UML the database system is well defined, visualized, generated and specified considering complete project. The user interfaces are designed by using ASP. NET for easy access of the user location independently. It is seen that without using so much coding load, an effective object-oriented database can be designed. It is possible to use the same language for both application and object-oriented databases in contrast to relational databases. The designed database can be used in different machine workshops with small modifications depending on the specifications of workshops.
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    E-Commerce According To Hobby
    (Elsevier Science Bv, 2014) Sehirli, Eftal; Orak, Ilhami M.
    This paper describes a web application called E-Commerce According to Hobby. In this application, E-Commerce According to Hobby application contains some different technologies such as RSS so as to be able to get the news of products from an e-commerce web site, LINQ to SQL and Ado. Net in order to make a connection with SQL Server Database System and compare these two technologies. Thanks to this study, we hope that people who use this application can reach the desired news more easily. Therefore, they can get a chance to get rid of the unrelated news for them. This study aims to increase the user-friendliness to do shopping from a specific e-commerce web site such as EBAY without spending a lot of time in a fast way. (C) 2014 Elsevier Ltd.
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    Performance analysis of hot metal temperature prediction in a blast furnace and expert suggestion system proposal using neural, statistical and fuzzy models
    (Edp Sciences S A, 2021) Bozkurt, Erdogan; Orak, Ilhami M.; Tunckaya, Yasin
    Blast Furnace (BF) production methodology is one of the most complex process of iron & steel plants as it is dependent on multi-variable process inputs and disturbances to be modelled properly. Due to expensive investment costs, it is critical to operate a BF by reducing operational expenses, increasing the performance of raw material and fuel consumptions to optimize overall furnace efficiency and stability, also to maximize the lifetime. The chemical compositions and temperature of hot metal are important indicators while evaluating the operation, therefore, if the future values of hot metal temperature can be predicted in advance instead of subsequent measuring, then the BF staff can take earlier counteractions on several operational parameters such as coke to ore ratio, distribution matrix, oxygen enrichment rate, blast moisture rate, permeability, flame temperature, cold blast temperature, cold blast flow and pulverized coal injection rate, etc. to control the furnace optimally. In this study, Artificial Neural Networks (ANN) model is proposed combined with NARX (Nonlinear autoregressive exogenous model) time series approach to track and predict furnace hot metal temperature by selecting the most suitable process inputs and past values of hot metal temperatures using the real data which is collected from the BF operated in Turkey during 2 months of operation. Various data mining techniques are applied due to requirements of charge cycling and operating speed of the furnace which secures novelty and effectiveness of this study comparing previous articles. Furthermore, a statistical tool, Autoregressive Integrated Moving Average (ARIMA) model, is also executed for comparison. ANN prediction results of 0.92, 8.59 and 0.41 are found very satisfactory comparing ARIMA (1,1,1) model outputs of 0.73, 97.4 and 9.32 for R-2 (Coefficient of determination), RMSE (Root mean squared error) and MAPE (Mean absolute percentage error) respectively. Consequently, an expert suggestion system is proposed using fuzzy if-then rules with 5x5 probability matrix design using the last predicted HMT value and the average of the last 5 HMT values to decide furnace's warming or cooling movements state in mid-term and maintain the operational actions interactively in advance.
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    A Review on Semantic Web and Recent Trends in its Applications
    (Ieee, 2014) Menemencioglu, Oguzhan; Orak, Ilhami M.
    Semantic web works on producing machine readable data. So semantic web aims to overcome the amount of data that is consisted. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that is consisted as a result of the existing pages on the web. Semantic search engines are extensions to traditional search engins and improved version. This paper summarizes semantic web, traditional and semantic search engine concepts and infrastructure. Also semantic search approaches and differences from traditional approach are detailed. A summary of the literature is provided by touching on the trends on this area. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging.