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With the proposed project we mean to investigate the influence of the combination of different binders on the mould filling and debinding stage of the low-pressure injection moulding process (LPIM) in the production of ceramic parts at the Hidria AET plant.
We intend to combine two distinct but yet inter-connected advanced techniques: artificial neural networks (ANN) and computer simulation of the mould filling process. Both of them will be supported by an extensive set of measurements and experimentally verified using data from a real production. As a consequence, three concrete results are expected: an improved mould filling tool for moulding ceramic shafts, an improved ceramic suspension (feedstock) optimizing mould filling and debinding stages of LPIM, and an ANN based software tool for prediction of green and brown part properties from feedstock composition and binder system.
The main industrial goals of the project are the improvement of the quality of ceramic parts, the introduction of new complex ceramic products and the decrease of production costs.
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