An Adaptive Bayesian Classification For
Real-time Image Analysis In Real-time Particle
Monitoring For Polymer Film Manufacturing
Author(s)
K. Torabi, S. Sayad & S. T. Balke
Abstract
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.
1 Introduction
Plastic film is commonly manufactured by using a single screw extruder to pump
molten plastic through a slit while simultaneously stretching the cooling product.
Defects in the film, such as holes, are very destructive to film performance and
originate from contaminant particles in the melt. Images of these particles in the
flowing melt can now be obtained using a special camera termed a \“Scanning
Particle Monitor” [4] at an acquisition rate of about 15 to 20 images per minute.
A Bayesian classification model was developed and shown to very effectively
distinguish between images containing contaminant particles (\“with particle”
(WP) images) and images not containing such particles (\“without particle” (WO)
Keywords
Related Book
Other papers in this volume
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