Visual analytics and intelligent reasoning for smart manufacturing defect detection and judgement: A meta-learning approach with knowledge graph embedding case-based reasoning

Abstract

Visual inspection is an important procedure in manufacturing industries to guarantee product quality and reduce costs of poor quality. Several challenges exist for manufacturing inspection automation, including the false positive and false negative trade-off, autonomous decision-making, and inspection algorithm selection. This paper addresses these challenges by proposing a two-level visual analytics and intelligent reasoning system, where machine vision identifies and locates potential defects and convolutional neural network models further classify them into acceptable and defective features. Symbolic reasoning techniques are applied for autonomous decision making on part-level judgement to enhance the system reasoning capability, including part judgement (accept, reject, rework) and rework operation assignment. Furthermore, a case-based reasoning approach for meta-learning of inspection algorithm selection is proposed, which uses knowledge graph embedding (TransR embedding) to embed cases into a two-dimensional space for case similarity estimation and case retrieval. A case of smart assembly inspection is presented to validate the feasibility of the proposed system.