Towards new methods for process adjustments based on parts quality measurements

TL;DR

This paper explores a neural network-based method for real-time adjustment of injection molding parameters to enhance part quality, addressing the challenge of rapid cycle times in industrial manufacturing.

Contribution

It proposes a novel self-adaptive adjustment approach using neural networks to optimize process parameters based on quality measurements in short cycle times.

Findings

Neural network models can predict optimal process adjustments.

The method enables real-time parameter tuning within 30 seconds.

Preliminary results show improved part quality consistency.

Abstract

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.