Feasibility of Acoustic Print Head Monitoring for Binder Jetting Processes with Artificial Neural Networks

Abstract

The clogging of piezoelectric nozzles is a typical problem in various additive binder jetting processes, such as the manufacturing of casting molds. This work aims at print head monitoring in these binder jetting processes. The structure-born noise of piezoelectric print modules is analyzed with an Artificial Neural Network to classify whether the nozzles are functional or clogged. The acoustic data are studied in the frequency domain and utilized as input for an Artificial Neural Network. We found that it is possible to successfully classify individual nozzles well enough to implement a print head monitoring, which automatically determines whether the print head needs maintenance.

Keywords

• acoustic monitoring
• structure-born noise
• binder jetting
• core materials
• water-glass
• neural networks