Affinerie CCR, based in the Montreal region, is a refinery specializing in copper and precious metals. CCR carries out the final refining stage using an electrolytic plating process, during which copper is deposited on stainless steel plates (known as master plates). After 10 years of service, the numbered master plates were starting to show signs of surface corrosion, which was affecting the physical quality of the plates and reducing the production speed. Consequently, CCR undertook a plate restoration process and called on INO to design and implement a follow-up and monitoring method.
During the initial phase, which ended in 2012, INO developed an optical system and a digital vision algorithm to segment and classify the various characters in the codes used to identify the master plates. The system and algorithm are used to monitor the path of each plate from one end of the production facility to the other. Thanks to this system, continuous monitoring is provided and any plates requiring restoration are taken out of production without interrupting the operations.
Recently, CCR called on INO again to modernize its system and boost the read rate. Thanks to their deep learning and artificial intelligence expertise, the members of the INO team developed a new character recognition algorithm using convolutional neural networks.
This type of algorithm is known to exceed human performance in various tasks, such as image recognition. The previous algorithm used hand-crafted features while the new algorithm learns the characteristics directly from the data with a view to minimizing the number of read errors. This makes the system much more adaptable since by adding images to the database, new learning is facilitated and performance is improved.
In addition, read accuracy performance was boosted from 94% to 99%, enabling CCR to better monitor the condition of master plates in the facility. Based on a total of 75,000 master plates read per week, the number of incorrect readings dropped from 4,500 to 750 per week.
This second phase of the collaboration with CCR showed that by combining optics and artificial intelligence, two of INO’s key areas of expertise, innovative solutions can be delivered to the manufacturing sector.
INO provided us with state-of-the-art expertise that CCR lacked. In addition, INO’s desire to teamwork with CCR and deliver a quality product made this project a success. The operators and CCR’s management team are very pleased with the master plate monitoring system.