Computer vision in aseptic pharmaceutical manufacturing: a deep learning-based approach to vial inspection and pack formation during a lyophilizer loading cycle

Vision systems are powerful tools playing an increasingly important role in modern industry, to detect errors and maintain product standards. With the enlarged availability of affordable industrial cameras, computer vision algorithms have been increasingly applied in industrial manufacturing processes monitoring. Until a few years ago, industrial computer vision applications relied only on ad-hoc algorithms designed for the specific object and acquisition setup being monitored, with a strong focus on co-designing the acquisition and processing pipeline. Deep learning has overcome these limits providing greater flexibility and faster re-configuration. In this work, the process to be inspected consists in vials’ pack formation entering a freeze-dryer, which is a common scenario in pharmaceutical active ingredient packaging lines. To ensure that the machine produces proper packs, a vision system is installed at the entrance of the freeze-dryer to detect eventual anomalies with execution times compatible with the production specifications. Other constraints come from sterility and safety standards required in pharmaceutical manufacturing. This work presents an overview about the production line, with particular focus on the vision system designed, and about all trials conducted to obtain the final performance. Transfer learning, alleviating the requirement for a large number of training data, combined with data augmentation methods, consisting in the generation of synthetic images, were used to effectively increase the performances while reducing the cost of data acquisition and annotation. The proposed vision algorithm is composed by two main subtasks, designed respectively to vials counting and discrepancy detection. The first one was trained on more than 23k vials (about 300 images) and tested on 5k more (about 75 images), whereas 60 training images and 52 testing images were used for the second one.

Study of the performance of the prototype EIC dRICH readout based on SiPM with thermoelectric cooling

L'Electron-Ion Collider è un futuro acceleratore di particelle che approfondirà la nostra conoscenza riguardo l'interazione forte tramite la collisione di elettroni con nuclei e protoni. Uno dei progetti attualmente considerati per la costruzione del rivelatore, il dual-radiator RICH, prevede l'impiego di due radiatori Cherenkov, sui quali verranno montati dei fotorivelatori per rilevare l'emissione della luce Cherenkov e risalire alla massa delle particelle. L'opzione di base per questi rivelatori sono i sensori al silicio SiPM. Questo lavoro di tesi si basa sullo studio delle prestazioni di un prototipo per l'acquisizione dei dati rilevati dai SiPM che sfrutta l'effetto termoelettrico per raffreddare la zona in cui sono situati i sensori. L'analisi dei dati acquisiti ha portato alla conclusione che le prestazioni del prototipo sono confrontabili con quelle misurate all'interno di una camera climatica quando si trovano alla stessa temperatura.