The Russian aluminium giant, RUSAL has deployed a neural network to detect the quality of aluminium ingots produced at the Irkutsk Aluminium Smelter.
The latest system automatically scans the surface quality of small aluminium ingots during production. Surface defects on each aluminium ingot are examined on the casting conveyor using a video monitoring system and special-purpose software.
The neural network uncovers various types of defects, such as cracks, bumps, and foreign inclusions. It saves all the information concerning the defect and is displayed as an analytical diagram, with data available regarding the number of each type of defect per day and the number of ingots that did not pass quality assurance per shift. Employees can tweak the parameters based on which ingots are found to be defective by choosing the type of defect and its size as per the specification for the product.
Victor Mann, Chief Technical Officer, Rusal said: “This new solution enables production line personnel to track the quality of aluminium ingots in real-time, while they're being cast. It will guarantee that the products which get shipped to our customers meet all the applicable requirements.”
The video detecting system will be integrated with the existing automated production process control system and used on the casting line. Whenever a defect is found, the ingot is labelled as defective by a laser and is then removed from the conveyor.
RUSAL now plans the deployment of a new aluminium ingot quality assurance system at its other aluminium smelters. The aluminium company has consistently been automating its production processes. Thus, the deployment of robotics systems at several of the Company's aluminium smelters has completely automated the task of stacking finished aluminium ingots onto pallets. The production sites are now also adopting an automated video monitoring system to detect the accidental loss of hermetic sealing in the reduction cells.
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