AI-based root cause analysis for visual defects to improve board quality

In cases of visual defects on paperboard, technology reduces time needed to locate the origin of process disturbances. Camera technology on the board machine is constantly monitoring the paperboard quality. If a visual defect is observed, AI-based root cause analysis commences. The AI analyses 10,000 machine parameters, taking into account process delays to locate the origin of the visual defect.

Vision-based condition monitoring for tail threading ropes to decrease unplanned shutdowns

Tail threading ropes are used in a board machine when the web is fed through the machine after a standstill or a web brake. AI-technology has been adopted to avoid unplanned shutdowns caused by breaking of the tail threading ropes. 12 cameras are connected to the AI-based machine vision system, which constantly monitors tail threading ropes. The AI-based machine vision analyses video material and when the risk of tail threading rope breaking is high operators are alerted. This enables operators to do a controlled replacement of the tail threading rope and avoid unplanned machine downtime.

Have a look at the video illustrating AI use on Metsä Board Äänekoski’s board machine:

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