How to use prior knowledge for injection molding in industry 4.0

Abstract

Searching for the optimal injection molding settings for a new product usually requires much time and money. This article proposes a new method that uses reinforcement learning with prior knowledge for the optimization of settings. This method uses an actor-critic algorithm for the optimization of the filling phase and the holding phase. For five different injection molded products, the filling phase and holding phase were adjusted with the above-mentioned method. The learning algorithm optimized the settings for one product (pre-learning) and used this acquired knowledge (prior knowledge) to optimize the injection molding settings for a new product (post-learning). This research shows that the method is able to optimize the injection molding parameters in a reasonable time when prior knowledge is derived from a product with a different material, gate design or even geometry. On average, less than 16 injection molding cycles were needed for the algorithm to optimize the filling phase and less than 10 cycles to optimize the holding phase. The presented method can greatly facilitate the development of self-adjusting injection molding machines.

Keywords

• Injection molding
• Reinforcement learning
• Actor-critic algorithm
• Industry 4.0
• Self-adjustment