Relevant:
Associated Results
The machines (OT) are connected to intelligence in the Edge and Cloud (IT) for generation of Zero Defect Manufacturing functions
For many years, and in the context of INDUSTRY 4.0, FAGOR ARRASATE is working together with IKERLAN in smart platform for press machines and industrial processes. The platform goes from the sensitization of the machine’s critical elements to the remote monitoring of press conditions. The platform focuses on improvement of asset management and OEE (Overall Equipment Effectiveness) and allows FAGOR ARRASATE to increase quality of service for their clients.
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Results:
We are building a connected environment through the industrial furnace smartization, but also implementing an IT solution that enables data gathering and transferring on real-time to GHI server, where then the data analysis is performed.
We are developing Sinapro IIoT MES/MOM cloud solution (part of the Kolektor Digital Platform) as the cornerstone of the MOM system which enables real-time collecting, evaluating, validating, filtering, checking, and storing of production data. The captured production data can be processed in real-time for the purpose of obtaining various production information, which enables immediate action. MOM function for production analyses with depth learning technology of AI gives users additional and high-quality information’s for fast decisions to achieve zero-defect goals in production.
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Results:
Operators are more involved in the whole process, but mainly receives valuable information of the furnace operation through the real-time monitoring interface of the platform. Also daily reports provide them valuable information.
The Kolektor Digital Platform enables human involvement on various levels. Human operators can monitor, view and inspect created datasets. During the process of model training, the operator can monitor the current state and detailed information of the training process. The Kolektor Digital Platform opens a channel between a data scientist and a decision-making individual in the production line. It is desirable to have multiple people, each assigned to a specific task. The whole process could be split into subtasks - acquiring images on the production line, human expert labeling the images (classification, anomaly,..), data scientist training the model on the new dataset and at the end evaluation of the model and pushing the new (improved) model to the production line.
The RiaStone Qu4lity Pilot enables human involvement at various levels and production stages.
Human operators inspect, monitor, and view all created datasets. During the process of model training, the operator guides the ML algorithm through the image inspection training process.
This is performed after the image acquisition in the production line, through joint algorithm/human expert labeling of the images (new defect classification, conformant/non-conformant product), this allowing for the training of the algorithm in the existing defects dataset, and at evolving the algorithm and pushing the new (improved) model to the production line AQL platform
Operators are connected to the smart funcions the receive valuable information related to components condition that allow them to take decisions related to machine and process. Maintenance technitians and specialized engineers from the machine tool provider can also be included in the process.