With the help of neural networks, in which complex algorithms are used to monitor critical process steps, engineers are paving the way for zero-defect production in the area of metal powder injection molding.

The gain for manufacturers is less waste combined with time savings.

Things can occasionally go awry during production, and then it is often impossible to detect defects until after sintering, the final step in the process chain, by which time it is too late to correct the defect.

Now, researchers at the Fraunhofer Institute for Manufacturing and Advanced Materials IFAM are working towards achieving zero-defect production. Their idea is that, at any time during the molding process, the system should be able to monitor all parameters - such as weight, pressure and temperature - and to deliver a verdict on the quality of the component.

"In this way, errors, dimensional inaccuracies and defects such as cracks, warps or cavities can be detected on line," explains IFAM project manager Dr. Thomas Hartwig. "This will allow the manufacturer to respond immediately by changing the relevant settings."

In the long run the system can, if required, even be programmed to alter the parameters fully automatically. The necessary technical support is provided by a neural network developed for metal injection molding (MIM) in a joint effort by the IFAM engineers and algorithmica technologies, a private company. "The neural network is based on highly complex algorithms," says Hartwig. "Its advantage over existing solutions is that it is self-learning."

"IFAM researchers inspecting components produced using metal injection molding. (Credit: Copyright Fraunhofer IFAM)"

Source: Fraunhofer-Gesellschaft