An IKB defect classification system for automated industrial radiographic inspection

Abstract

Abstract: This paper presents work undertaken as part of a project concerned with the development of a fully automated industrial radiographic inspection system, based on both conventional image‐processing techniques for the detection and analysis of defects in the radiographic image, and intelligent knowledge‐based (1KB) techniques for the classification and evaluation of defect data against the quality assurance requirements of the inspection process. In this paper the 1KB defect classification system is presented. This system is based on a hierarchical frame‐based knowledge representation and a backward‐chaining production rule system. Examples of the frame structures, frame taxonomies and the data‐driven procedures, which maintain the knowledge base are given, along with an outline of the defect classification rules and the inference mechanism for dealing with uncertainty by means of confidence factors.