In recent years, problems caused by machine deposit have been increasing in Japan due to the deterioration of recycled paper utilization rates and raw material conditions. Furthermore, quality requirements from corrugated board mills have become more stringent and reducing the defect rate has become a major issue.
In addition, with a shrinking workforce and the retirement of skilled workers, it is becoming increasingly difficult to respond to machine soiling in a timely. and accurate handling. In response, we are developing “SmartPapyrus
®”, a system that visualizes machine deposits with IoT, performs predictive analysis of defects and paper breaks using AI, and prevents defects and paper breaks with machine deposit prevention technology. The goal of SmartPapyrus
® is to eliminate defects and breaks in the papermaking process. The elimination of defects and breaks will reduce the amount of work required, and by eliminating wasteful work, not only will the machines be more productive, but also the workers will be able to focus on more productive work. Furthermore, by reducing the number of defective products due to defects and paper breaks, energy consumption can be reduced. This will contribute to decarbonization.
Therefore, we have started to develop a defect occurrence prediction analysis as SmartPapyrus
® 2,0. SmartPapyrus
® 2.0 receives machine condition data from DCS and QCS, in addition to SmartPapyrus
® Ver. 1 fabric deposit data and SmartPapyrus
® 1.0 defect information and analyzes it using AI to detect signs of defects before they occur and to propose countermeasures to reduce defects. The system then analyzes the data to detect signs of defects before they occur and proposes countermeasures to reduce defects. In this report, we introduce an overview of the analysis of signs of defect occurrence and present a preliminary report on the Proof Of Concept (PoC) experiment.
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