Design of an In-Process Quality Monitoring Strategy for FDM-Type 3D Printer Using Deep Learning

Additive manufacturing is one of the rising manufacturing technologies in the future; however, due to its operational mechanism, printing failures are still prominent, leading to waste of both time and resources. The development of a real-time process monitoring system with the ability to properly forecast anomalous behaviors within fused deposition modeling (FDM) additive manufacturing is proposed as a solution to the particular problem of nozzle clogging. A set of collaborative sensors is used to accumulate time-series data and its processing into the proposed machine learning algorithm. The multi-head encoder-decoder temporal convolutional network (MH-ED-TCN) extracts features from data, interprets its effect on the different processes which occur during an operational printing cycle, and classifies the normal manufacturing operation from the malfunctioning operation. The tests performed yielded a 97.2% accuracy in anticipating the future behavior of a 3D printer.

Automated summary

This paper proposes a real-time process monitoring system that accurately predicts abnormal behaviors in additive manufacturing, such as nozzle clogging. By using collaborative sensors to collect data and machine learning algorithms for processing and classification, the system achieves accurate prediction of future behavior of 3D printers, reducing printing failures and resource waste.