On the Application of Artificial Intelligence Techniques to the Quality Improvement of Industrial Processes

In this paper, the combined use of decision trees and artificial neural networks is examined in the area of quality improvement of industrial processes. The main goal is to achieve a better understanding of different settings of process parameters and to be able to predict more accurately the effect of different parameters on the final product quality. This paper also presents results from the application of the combined decision tree neural network method to the transformer manufacturing industry. In the environment considered, quality improvement is achieved by increasing the classification success rate of transformer iron losses. The results from the application of the proposed method on a transformer industry demonstrate the feasibility and practicality of this approach for the quality improvement of industrial processes.