Autonomous printing process optimisation and in-situ anomaly detection in fused deposition modelling using an integrated data-driven approach
Haining Zhang, Yangwen Yu, Xingchen Liu, Nannan Liang, Yongrae Kim, Dongwoon Shin, Seung Ki Moon, Joon Phil Choi
Abstract
Fused Deposition Modelling (FDM) is the predominant material extrusion technique in polymer additive manufacturing (AM). While it offers compatibility with engineering-grade composites and enables the fabrication of polymer-composite components with intricate architectures unattainable through traditional techniques, the persistent dependence on empirical process tuning often leads to structural defects – critical limitations that hinder FDM's transition to advanced industrial applications. This paper proposes a data-driven approach that integrates advanced Artificial Intelligence (AI) with real-time computer vision to optimise FDM process parameters and enable in-process anomaly detection. In the developed approach, a stepwise machine learning strategy systematically models the printed line quality, ensuring pre-print process optimisation. A You Only Look Once (YOLO) object detection model is then deployed for in-situ monitoring, analysing the printed line morphology to assess melt flow stability and detect geometric deviations during printing. Validation experiments are conducted to assess the effectiveness of the developed YOLO model. Overall, the integrated framework demonstrates its superiority over empirical methods and analytical models in both pre-process optimisation and real-time quality assurance. Furthermore, the integrated machine vision and pattern recognition system exhibits adaptability to diverse material deposition systems, providing a unified approach to intelligent process optimisation across AM domains.