Wet blue leather grading using fuzzy expert system.

Abstract

Wet blue hides defect extraction and classification has remained as the centre of research for the leather tanning industry although the investigation in this domain was first reported around one century ago. Several mathematical, statistical and empirical models have been developed in the past only to yield limited success in terms of accuracy and general applicability. In recent years, soft computing tools like artificial neural networks and neural-fuzzy models have been developed, which have shown remarkable prediction accuracy. However, artificial neural network and neural-fuzzy: models are trained using enormous amount of noise free input-output data, which are difficult to collect from the leather tanning industries. In contrast, fuzzy logic based models could be developed by using the experience of the grading personnel only and it gives good understanding about the roles played by various inputs on the outputs. This project therefore deals with the. modeling of wet blue leather grading using a simple fuzzy expert system. The grading accuracy of the model was found to be very encouraging.