Towards new methods for process adjustments based on parts quality measurements
For the injection molding industry, this work aims to enable real-time process adjustments based on quality measurements, but it is currently only a literature review with no experimental validation.
The paper addresses the lack of a method to adjust injection molding process parameters for optimizing part quality, proposing a neural network-based self-adaptive adjustment method. No concrete results are presented as it is a literature review.
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.