Defect prediction with neural networks

Abstract

The industrial and scientific world abound with problems that are poorly un&rstood or for which apparent anomalous conditions exist. Artificial Neural Networks are utilized with conventional techniques to extract salient features and relationships which are non-linear in nature. Defect causality in a large continuous flow chemical process is investigated. Significant gains in the prediction of defects over traditional statistical methods are achieved.