Deep Fuzzy Cognitive Maps for Defect Inspection in Antenna Assembly

Abstract

Regardless of the tremendous technological development in manufacturing processes and equipment, assembly errors are an emerging concern. Hence, quality inspection software is of high importance for capturing defects at the source and preventing further error propagation. The advantages of Fuzzy Cognitive Maps (FCMs) as knowledge representation models with explainable capabilities can be compounded with the advantages of Deep Convolutional Neural Networks (CNNs) in order to acquire accurate and interpretable inspection results in Smart Manufacturing. In this work, an explainable binary classification approach is performed, namely DeepK-FCM, and appears to be able to assess sufficiently industrial images from TELEVES. The suggested DeepK-FCM methodology, inheriting the powerful characteristics of deep learning and FCMs, includes a number of steps as follows: fine-tuning of well-known CNNs for feature extraction with transfer learning, feature clustering by performing K-Means algorithm, definition of causal relationships through fuzzy measures on similarities produced and FCM model for the decision-making. Through the experimental analysis on a real industrial data set, it is being proved that the current approach with DeepK-FCM is more efficient for the task of defect inspection in the antenna assembly than the straightforward binary classification accomplished by CNNs. The attained accuracy of 80% is improved by 3% compared to the state-of-art CNN classifiers and indicates a high potential even when the data are scarce. In addition, the decision-making with FCMs levitates the interpretability of the system which contributes to the efforts towards a more explainable AI method for quality control inspection.