Concrete strength control charts pattern recognition based on Linear Vector Quantization neural networks

Abstract

The objective in this study is to detect the errors that occur or may occur in the future during the process in which the company?s quality objectives are fulfilled and to show the applicability of the Artificial Neural Networks (ANN) which is one of the Artificial Intelligence (AI) techniques. Thus, it will be able to contribute to the main purposes which make quality control necessary such as to raise the level of quality, reduce operating costs, time savings, raising employees? motivation and reducing customer complaints. For this purpose, average compressive strength, one of the most important quality indicators, of a company that produces ready-mixed concrete has been used. Linear Vector Quantization (LVQ) type ANN model has been established by using the quality characteristics observation values related to control charts and the parameters related to control charts, and when these two models are compared, it has been found out that the model whose quality characteristics have been constructed using the observation values result in more successful results than that constructed with the model's control charts.