Surface defect recognition using multi-layer perceptron and C-means algorithm

Quality assurance in the powder injection molding is a critical problem due to its complicated processing methods. As surface conditions are major issues for the product quality of the powder injection molding, automated visual inspection on the surface is highly demanded. This paper proposes representation and recognition schemes for the surface defects on the powder injection molding. From the edge image, line segments were extracted, then they were represented using parameters. Multi-layer perceptron and C-means algorithm were tested to recognize defective features in the powder injection molding. The neural network method showed better recognition for the defective features based on the selected measures. Significance: The surface defect in powder injection molding is a critical problem for the product quality assurance. From the complicated surface features, the recognition of defective features were compared between an artificial neural network and traditional pattern recognition method.