An adaptive Bayesian classification for real-time image analysis in real-time particle monitoring for polymer film manufacturing

Contaminant particles in molten plastic flowing through processing equipment result in very undesirable defects in manufactured film. Images of these particles can now be obtained in-line during processing. A Bayesian classification model was previously shown to be capable of distinguishing images containing a contaminant particle from those that did not [1]. However, new processing conditions cause changes in the background "noise" in the acquired images which necessitate retraining of the model. This paper demonstrates how a recently presented method of adaptive machine learning, termed the "Intelligent Learning Machine" (ILM) [2], [3], was successfully used to enable the classification model to adapt to such changes in image quality. The use of the ILM permitted this to be accomplished with great efficiency and flexibility without interrupting the process monitoring.