Attempt of neural modelling of castings crystallisation control process

Ryszard Tadeusiewicz , Joanna Grabska-Chrząstowska, Henryk Połcik

AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland.

DOI:

https://doi.org/10.7494/cmms.2008.2.0193

Abstract:

The paper presents a successful attempt to create a neural model for a casting process. Neural network, following the training process, is able to predict selected effects of casting cooling (as a number of metal crystals in certain point of the casting), based on the data referring to the cooling method (the casting is cooled with water, the flow of which is controlled according to a set time algorithm). Various structures of neural networks used for solving the problem in question have been illustrated and the results obtained have been discussed. After creating several successful versions of a straight model, an attempt was made to set up a reverse model, it is such model in which expected control result is supplied to the input of the neural network (here, the number of crystals in a casting), and the network is expected to generate output signal stating how the cooling of the casting should be controlled to accomplish that objective. This has proven impossible, and the relevant reasons and circumstances have been presented.

Cite as:

Tadeusiewicz, R., Grabska-Chrząstowska, J., Połcik, H., (2008). Attempt of neural modelling of castings crystallisation control process. Computer Methods in Materials Science, 8(2), 58 – 69. https://doi.org/10.7494/cmms.2008.2.0193

Article (PDF):

Keywords:

Casting, Casting cooling, Neural network, Neural model, Cooling control, Reverse model

References: