Classification techniques based on AI application to defect classification in cast aluminum

Carlos Platero; Carlos Fernandez; Pascual Campoy; Rafael Aracil

doi:10.1117/12.196088

23 November 1994 Classification techniques based on AI application to defect classification in cast aluminum

Carlos Platero, Carlos Fernandez, Pascual Campoy, Rafael Aracil

Proceedings Volume 2249, Automated 3D and 2D Vision; (1994) https://doi.org/10.1117/12.196088
Event: Optics for Productivity in Manufacturing, 1994, Frankfurt, Germany

Abstract

This paper describes the Artificial Intelligent techniques applied to the interpretation process of images from cast aluminum surface presenting different defects. The whole process includes on-line defect detection, feature extraction and defect classification. These topics are discussed in depth through the paper. Data preprocessing process, as well as segmentation and feature extraction are described. At this point, algorithms employed along with used descriptors are shown. Syntactic filter has been developed to modelate the information and to generate the input vector to the classification system. Classification of defects is achieved by means of rule-based systems, fuzzy models and neural nets. Different classification subsystems perform together for the resolution of a pattern recognition problem (hybrid systems). Firstly, syntactic methods are used to obtain the filter that reduces the dimension of the input vector to the classification process. Rule-based classification is achieved associating a grammar to each defect type; the knowledge-base will be formed by the information derived from the syntactic filter along with the inferred rules. The fuzzy classification sub-system uses production rules with fuzzy antecedent and their consequents are ownership rates to every defect type. Different architectures of neural nets have been implemented with different results, as shown along the paper. In the higher classification level, the information given by the heterogeneous systems as well as the history of the process is supplied to an Expert System in order to drive the casting process.

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Carlos Platero, Carlos Fernandez, Pascual Campoy, and Rafael Aracil "Classification techniques based on AI application to defect classification in cast aluminum", Proc. SPIE 2249, Automated 3D and 2D Vision, (23 November 1994); https://doi.org/10.1117/12.196088

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KEYWORDS

Fuzzy logic

3D vision

Neural networks

Classification systems

Rule based systems

Aluminum

Feature extraction

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