Towards Product Robust Quality Control with Sequential D-optimum Inputs Design

Abstract

Recently, there is a high demand on companies for high quality, reliable products for a reasonable price in a timely manner. In order to provide such goods novel methods of product quality control are required to be developed and used in industrial practice. For example, it is possible to use methods based on analytical redundancy. In such approaches the effectiveness of the methods depends on the quality of a product model. This paper proposes a new methodology for improving the neural model of the controlled product. For this aim experimental design technique is applied. Furthermore, a method of product quality control robust to neural model uncertainty is developed. All these conceptual approaches find the practical application for the three-screw spindle oil pump.