Optimizing cardboard-blank picking in a packaging machine by using Reinforcement Learning algorithms

Artificial Intelligence (AI) has been one of the most promising research topics for years and the world of industrial process control is beginning to approach the possibilities offered by the so-called Machine Learning. Problems without a model, with multiple degrees of freedom and difficult to interpret, where traditional control technologies lose efficiency, seem like the ideal benchmark for AI. This thesis focuses on a packaging machine of coffee capsules being currently optimized at the Research and Innovation department of IMA S.p.A. company. In particular, the analysis concerns the apparatus responsible for picking the cardboard blanks that will be subsequently and progressively formed to envelop the capsules. The success of this first operation depends on various controllable parameters and as many disturbances. The relationship between the former and the latter is not easily identifiable, and its understanding has so far been entrusted to the experiential knowledge of operators called to intervene in the event of incorrect picking cycles. This thesis activity aims to achieve an adaptive control using Machine Learning algorithms, specifically the ones in the branch of Reinforcement Learning, to identify and autonomously perform the correction of the parameters that best avoid missing or incorrect picking cycles, which affect the productivity of the production system and the quality of the final product. After a dissertation on the theoretical foundations and the state of the art of RL, the case study is introduced and adapted to the RL framework. The subsequent choice of the best training modality is then followed by the description of the implementation steps, and the results obtained online during the tests of the controller are finally presented.