Determination of Production Defects in Iron and Steel Sector by Data Mining
İsmail Burak AKINCI Filiz ERSÖZ
AbstractThe 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. Iron and steel products obtained from the studies have prolonged their use and price and sales superiority has been achieved. At the same time, the market value of the products increases and there is a minimum loss of product. Therefore, studies in this field should be focused on. On the basis of quality, instead of debugging errors is the approach of not making mistakes. Instead of using your earnings as a philosophy, we should adopt an understanding of gaining from our losses. Understanding the importance of quality work and improvements, the primary purpose of enterprises is to support quality production by preventing or reducing errors in production. Data mining has started to be used effectively in enterprises. Data mining involves the process of selecting, organizing and modeling the most necessary data for business executives. At this point, it is possible to define data mining as a set of techniques and concepts that produce new information for decision-making processes. In this study, firstly the VM process is defined and then the VM studies which are selected from the literature covering 2010-2018 and applied to certain quality improvement problems in the manufacturing sector are evaluated. The definition of process and product quality, estimation of quality, classification of quality and optimization of quality parameters are discussed. In addition, the application of decision trees, one of the most widely used and effective VM techniques, in order to determine the variables and levels that cause production errors in an industrial organization is also included.