Convolutional neural networks for the automatic control of consumables for analytical laboratories

In recent years, the need for advanced systems and technologies for industrial process optimization using com- puter vision and artificial intelligence (AI) techniques has become increasingly pervasive. The specific focus of this study is to introduce an AI-based monitoring system within a production chain involved in manufacturing plastic consumables for analytical laboratories, specifically targeting the control of vials containing an antico- agulant substance. Currently, the inspection process relies on manual visual inspection conducted on a sample basis, resulting in the potential discarding of entire production batches if the absence of the anticoagulant sub- stance is detected in a single vial. To overcome the inefficiency of the manual system, a comprehensive method is proposed to verify the presence of the anticoagulant substance in all produced vials, leveraging advanced computer vision and AI techniques. This innovative monitoring system offers promising solutions for enhanc- ing industrial processes by enabling accurate and real-time monitoring. Specifically, we present our model and some preliminary results showing the potentiality of the proposed approach