Defect prediction with neural networks

Abstract

The industrial and scientific world abound with problems that are poorly understood 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.