Cosmetic defect classification found in ophthalmic lenses using artificial neural networks

This paper presents the solution of a specific classification problem classification of cosmetic defect found in ophthalmic lenses - using artificial neural networks (ANN). In an ordinary industrial inspection process cosmetic defect classification involves a lot of subjectivity because the test depends on the appreciation of the cosmetic defect by a human inspector. Therefore, a machine classifier is of great help in order to reduce the effect of the subjectivity. The neural network classifier described in this paper is of relevance because it demonstrates the applicability of ANN to solve real world problems. The classifier also helped to discover a bias criteria during the inspection performed by human inspectors. Besides, the performance of the neural network impacts positively in the cost of incorrect decisions during cosmetic lens inspection. The paper shows the performance of several ANN classifiers designed. The best classifier turned out to be a multilayer perceptron trained with the backpropagation algorithm. This classifier has 94% of correct classification and solves the controversies between the production and quality departments due to subjectivity of the inspection.