  Thermoplastics injection molding allows the production of complex parts in
large series. Industrial quality requirements are increasing. The injection
molding process needs to be regulate in order to maintain a working point.
There is actually no method to adjust all the parameters of the process in
order to optimize the final quality of the product. We can rely on the success
of neural networks models to propose a robust self-adaptive adjustment method.
Objective is to adjust machine parameters for each cycle, based on measured
quality characteristics on the produced part. A classical industrial cycle time
is often less than 30 seconds ; therefore challenges are: measuring, computing
and setting up machine parameters within this short timeframe. In this
presentation we establish a specific literature review, on which will base our
experimental works.
