Title:	Operator-friendly machine vision tool for scrap identification
DNr:	Berzelius-2023-303
Project Type:	LiU Berzelius
Principal Investigator:	Sudhanshu Kuthe <kuthe@kth.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2023-11-04 – 2024-06-01
Classification:	20506
Homepage:	https://www.kth.se/profile/kuthe
Keywords:

Abstract

With the need for energy-efficient steelmaking, the optimization of the EAF bucket charge emerges as a crucial aspect. Effective scrap management and strategic storage are pivotal for achieving sustainable steel production. In contemporary steel plants, scrap is manually retrieved using mechanisms like the octopus claw and magnets. For this system to function seamlessly, an advanced scrap identification system is imperative. Such a system supports operators in the control room, ensuring accurate scrap retrieval at designated intervals. This dual-objective system aids in process control, ensuring precise scrap retrieval by the crane and subsequently verifying the bucket layering needed for the EAF steelmaking. An image database sourced from actual industrial enviornment is avaibale for data processing. Leveraging image segmentation and object detection techniques, it becomes feasible to classify specific types of steel scrap. The project proposes the utilization of deep learning algorithms for these image processing tasks. The main goal is to develop an operator-friendly machine vision tool for scrap identification.