Model-based real-time prediction of surface roughness in fused deposition modeling with graph convolutional network-based error correction

Publication Date

12-1-2023

Document Type

Article

Publication Title

Journal of Manufacturing Systems

Volume

71

DOI

10.1016/j.jmsy.2023.09.001

First Page

286

Last Page

297

Abstract

Additively manufactured parts usually have relatively rough surface finish due to the layer-by-layer process. Extensive research has been conducted on the effect of process parameters on surface roughness as well as on real-time monitoring of surface roughness using sensor data. However, very few studies consider both process parameters and condition monitoring data to perform real-time prediction of surface roughness. To address this research gap, we introduce an augmented deep learning framework that integrates a model-based predictor with a deep learning-based error corrector to predict the surface roughness of parts fabricated via fused deposition modeling. The model-based predictor predicts surface roughness by considering the effects of process parameters on surface roughness, while the deep learning-based error corrector estimates the prediction errors between the true surface roughness and predicted surface roughness using real-time sensor data collected during additive manufacturing. To consider both correlations among features extracted from sensor signals and temporal correlations of each sensor signal, we construct multiple graphs to reveal these correlations and introduce a multi-graph convolutional network to analyze these undirected graphs. Experimental results show that the proposed framework outperforms existing data-driven methods reported in the literature.

Keywords

Additive manufacturing, Error correction, Fused deposition modeling, Graph convolutional network, Surface roughness

Department

Industrial and Systems Engineering

Link to Full Text

Share

COinS