Applying artificial neural networks and virtual experimental design to quality improvement of two industrial processes

Abstract

Artificial neural networks (ANNs) are powerful tools to model the non-linear cause-and-effect relationships inherent in complex production processes, usually for process and quality control. This paper substantiates the concurrent application of ANNs and virtual design of experiments to quality improvement. For a chemical manufacturing process and a printed circuit board machining process, respectively, empirical ANN models were constructed and validated using historical data, which were further used to predict the outputs of well-designed process settings. The predicted results were then used to perform statistical tests and identify the significant factors and interactions that affect the quality-related output variables. For the production of a resin intermediate, it was revealed that the combination of low water concentration and an appropriate ratio of raw materials increases both the yield and product quality in a synergistic manner. For the machining of printed circuit board slot by a milling cutter, it was concluded that a high forwarding speed was preferred for the better quality of the milled surface. For both cases, the preliminary conclusions lead to the directions of further real-world experiments for quality improvement. The data mining approach integrating ANNs and virtual design of experiments showed great potential to achieve a better understanding of process behaviour and to improve the process quality efficiently.