Yazar "Akinci, I.B." seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim Yok
    Öğe
    Comparison of Iron and Steel Production Defects Using Classification Algorithms
    (Institute of Electrical and Electronics Engineers Inc., 2021) Akinci, I.B.; Alobaidi, D.; Ersoz, F.
    The iron and steel industry are a strong foundation of economic development in the world. The iron and steel industry are directly related to the economic developments in the world market and the economic powers of the countries. It also provides input to all branches of industry. Data mining and techniques in this sector play an important role in the activities of corporate and large enterprises as a scientific method. It involves the processes of finding and modeling meaningful relationships between meaningless large chunks of data in an enterprise. Studies on the iron and steel industry in the world and in our country are very limited. Analyzing products in the iron and steel industry using data mining techniques will both save time and contribute to reducing the financial burden of operators. It is also thought that it can increase the preferability level by increasing the quality of the products offered to the customers. In this study, the data mining process of the iron and steel industry is defined and the data mining studies applied to some quality improvement problems in the production sector are examined, and the optimization of the process and quality parameters from the quality improvement problems are emphasized. In the application section, data mining techniques are used to determine the variables and levels that cause manufacturing defects in an industrial enterprise. To achieve this goal, the decision tree, which is one of the data mining classification methods, has been applied with the C5.0, CRT, CHAID and QUEST algorithms, the decision tree has been created with the highest accuracy C5.0 algorithm and the results have been examined. As a result of the analysis, the products produced by the industrial enterprise are classified according to production defects. © 2021 IEEE.
  • Küçük Resim Yok
    Öğe
    Determination of Production Defects in Iron and Steel Sector by Data Mining
    (Institute of Electrical and Electronics Engineers Inc., 2019) Akinci, I.B.; Ersoz, F.
    The studies related to the production industry are limited in the world and in our country. Especially in iron and steel sector, quality levels of different types of products need to be monitored. The studies show that with the emphasis on the quality levels of iron and steel products, the product life span is prolonged, and price and sales superiority is provided in the products. Accordingly, the market value of the products increases and there is a minimum loss of product. The primary purpose of the enterprises that realize the importance of quality work and improvements is to support quality production by preventing or reducing defects in production. Therefore, scientific studies in this sector should be focused on. Data mining and techniques, which is one of the scientific methods in this sector, have been used effectively in institutional and large enterprises. Data mining makes a significant contribution to business managers and includes the processes of finding and modeling meaningful relationships among the meaningless large data stacks in the enterprise. At this point, it is possible to define data mining as a set of techniques and concepts that generate new information for decision-making processes. In this study, firstly the data mining process is defined, data mining studies applied to certain quality improvement problems in manufacturing sector are examined and the optimization of process and quality parameters from quality improvement problems is emphasized. In the application part, data mining techniques are used to determine the variables and levels that cause production defects in an industrial enterprise. To achieve this aim, K-Means algorithm, which is one of the multivariate statistical methods, was examined by clustering analysis and the results obtained were supported by discriminant analysis. As a result of the analyzes, the products produced by the industrial enterprise were classified according to the production defects. © 2019 IEEE.