Development of a graph convolutional network-based surface quality monitoring approach - Peh, Gerald Zong Xian (Nanyang Technological University, 2022)
Many traditional quality monitoring approaches faced issues such as a huge number of uncontrollable parameters which leads to prediction inaccuracy. Other forms of modern monitoring system utilize Deep Learning (DL) models such Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNN). However, such models are unable to mine complex relations between each signal. To counter this issue, this study would introduce Graph Convolutional Networks (GCNs) to prediction of surface quality where it takes graph-structured data as an input instead of the usual Euclidean structured data. Such models can learn complex relationships which allows better feature representation of each sampled data. However, most GCNs have certain limitations such as a fixed kernel size which prevents learning of relationship from multi-hop neighborhood domains.