| Abstract |
A hype topic, one that now has become an established idea, is the possibility to increase plant efficiency by gaining and applying a better awareness of how scrap is performing in the melting process. Scrap management becomes the key point in cost reduction since it could comprise up to the 50% of the overall production costs. Technological innovations promise to be the driver to improving raw material management, shortening its acquisition time and reducing the waste during the metallurgical process. Expensive raw materials require a huge involvement of plant resources, and are highly dependent on the human factor. All the quality and logistics decisions belong to the judgment of the operators, increasing the chance of non-conformities (e.g., erroneous classification, material discharged in the wrong location, error loading material in the buckets). To overcome these issues, online classification of the scrap is the keystone. Starting from the arrival of scrap at the plant, through the acceptance of the delivery note and the check-in of the carriers, Automatic Scrap Classification gives support to inbound-scrap control and classification, enabling real-time traceability of the scrap inside the bays. The Quality Control System will benefit from all the details of the material used in production. Danieli Automation, with its Digi&Met business unit, implemented the Q-ASC a system that, leveraging Artificial Intelligence (AI) and deep learning techniques, can assist scrap classification procedures through computer vision and automatic scrap recognition. The goal of scrap identification is to localize and assign a specific class label to a given visual sample of scrap or inert/hazardous material. The classification can be conducted using different methodologies based on material shapes or dimensions. Danieli Automation Q-ASC is the entry point for the Scrap Yard Management and can be considered as the central data hub for managing the scrap inbound to the plant, connecting all the systems requiring reliable scrap data such as ERP, MES, WMS, and the Process Control and Quality Control Systems. Q-ASC collects, consolidates and transforms data, in the form of pictures, into information for scrap classification. |