Our client, Boliden Harjavalta’s main products are copper, nickel, gold and silver. Boliden’s nickel smelter is the only one in Western Europe. Boliden produces high quality nickel matte using the latest technology in the industry.
The Robotics Academy students were commissioned to explore the possibilities of using machine vision in the Boliden process. Boliden was interested in the concentration of various components of the slag after the smelting process and hoped to use machine vision to simplify and speed up the process.
A machine vision system is one where computer imaging is used for industrial purposes. The system consists of a light source, an object to be photographed, a camera, a computer, and an image processing program that interprets the image automatically. Machine vision systems perform precisely pre-programmed tasks and are primarily used when optical inspection should be fast, accurate, round the clock and consistently repeatable. Machine vision can be used to perform tasks that are impossible for human vision by using wavelengths that cannot be detected by the human eye. In this assignment, a conveyor belt cared slag under the system to identify the customer-defined concentrations.
When using a smart camera, all image processing and counting is done in the camera itself. We chose to use Cognex’s In-Sight application, but the slowness of this camera was a disadvantage because the items had to be pictured on a fast conveyor belt. Due to scheduling concerns, Robotics Academy students were not able to continue their research further, however, it is possible to continue the project with another software and a different camera solution in the future. We recommend considering Halco software, which could handle all intellectual observation and computing, as well as speed up image processing.
In our project, we learned how to take advantage of different camera and light options. The overall benefits were mutual. The students’ gained knowledge and were able to give the customer the information they sought. With the help of additional instruction, we can better utilize machine vision in future projects.