A Combination of Vision- and Sensor-Based Defect Classifications in Extrusion-Based Additive Manufacturing

Abstract

Additive manufacturing, also known as 3D printing, has been facing the problem of inconsistent processing defects and product quality as a transformative technology, thus hindering its wide application in industry and other fields. In this context, machine learning (ML) algorithms are increasingly used for automatic classification of process data to achieve computer-aided defect detection. Specifically, in this paper, two data-driven classification prediction models are built by monitoring the sensing signals (temperature and vibration data) and interlayer images during the printing process, using the fused deposition model (FDM) as the base case, and the prediction results of the two machine learning models are fused for prediction. The experimental results show that by fusing the prediction results of the two models, the classification accuracy is significantly higher than the prediction results of a single model. These findings can benefit researchers studying FDM with the goal of producing systems that self-adjust online for quality assurance.