Feasibility of acoustic print head monitoring for binder jetting processes with artificial neural networks

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

Influence of metal inserts with microformed edges on subsequent injection assembly moulding for media tight electronic systems

Recently, there is rising demand of electronic systems with functional density for industrial and automotive applications. Those components, which are typically manufactured by overmoulding blanked metal inserts within an assembly injection process, are often exposed to rough operating conditions. Especially penetrating water can lead to a severe damage of electronic systems which are often crucial to safety. Leakage can be caused among other things by crack initiation within the polymer at sharp edges of the metal insert as a consequence of stress concentration. In order to reduce stress concentration the effect of metal inserts with rounded edges and the forming process to manufacture such inserts is investigated. Since typical sheet thicknesses for electronic components are 1 mm and less the dimensions of the rounded edges are on the scale of micro features. The microforming operation of rounded edges is provided by open coining. The influence of varying part dimensions is investigated using FE-simulation. Furthermore, ideal rectangular insert shapes are compared to parts with sheared edge geometry. In addition the effect of rounded edges on stress distribution of overmoulded parts is analysed by combining resulting geometries of the forming simulation with the numerical analysis of stress distribution within the polymer

Influence of metal inserts with microformed edges on subsequent injection assembly moulding for media tight electronic systems

Recently, there is rising demand of electronic systems with functional density for industrial and automotive applications. Those components, which are typically manufactured by overmoulding blanked metal inserts within an assembly injection process, are often exposed to rough operating conditions. Especially penetrating water can lead to a severe damage of electronic systems which are often crucial to safety. Leakage can be caused among other things by crack initiation within the polymer at sharp edges of the metal insert as a consequence of stress concentration. In order to reduce stress concentration the effect of metal inserts with rounded edges and the forming process to manufacture such inserts is investigated. Since typical sheet thicknesses for electronic components are 1 mm and less the dimensions of the rounded edges are on the scale of micro features. The microforming operation of rounded edges is provided by open coining. The influence of varying part dimensions is investigated using FE-simulation. Furthermore, ideal rectangular insert shapes are compared to parts with sheared edge geometry. In addition the effect of rounded edges on stress distribution of overmoulded parts is analysed by combining resulting geometries of the forming simulation with the numerical analysis of stress distribution within the polymer

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

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