Context and Initial Needs

The company faced several challenges in its production lines, mainly related to:

• Lack of real-time visibility into the state of production equipment.
• High frequency of uncontrolled shutdowns.
• Inability to anticipate failures in parts or machinery.
• Quality control processes that are partially manual and disconnected from the production system.
• The need to facilitate the work of operators through more precise and automated tools.

Faced with these challenges, we designed and implemented the FORSEN Project, with a comprehensive approach that encompasses sensorization, artificial intelligence, product traceability, digitized quality control and analytical dashboards.

Implemented Technical Solution

1. Plant Data Sensing and Integration

Various sensors and devices were incorporated, including the installation of a thermographic camera, whose main functionality is to monitor in real time the temperature of the matrices at different stages of the process (stress, first, preform and second).

This system allows the configuration of specific thermal control zones, with management of presets according to the processed reference. Emphasis is placed on maintaining the cleanliness of the lens, due to the sensitivity of color measurement.

2. Part Traceability System

Each piece is uniquely identified, which enables full traceability from its passage through the cooling tunnel to its integration into boxes, including:

• Scan the cashier code when entering it into the tunnel.
• Record the number of passages through the tunnel and their position (right or left).
• Association between production and quality data both at the part and box levels.

3. Digital Quality Control Platform

The quality control procedure was systematized so that each scanned box goes through a defined process, including:

• Manual selection of parts for control from the tunnel.
• Visual evaluation and through systems such as Magnaflux.
• Standardized data entry in a web quality control platform, where it is possible to register specific attributes based on the reference, detection of multiple defects per part and export of data.

The system provides control histories, visualization of attributes out of range, and advanced filtering by date, matrix, casting and manufacturing.

4. Automated Management of Production Stops

A system was implemented to automatically record and analyze downtimes. Two types are defined:

• Microstop: when in 60 seconds no part reaches the control points.
• Stop: if after a microstop, no parts are received for the next 3 minutes.

These events are reported on a web platform for the operator to enter the reason, the causing equipment, the fault and observations. The tool generates downloadable graphics with the production sheet, showing the relationship between operability and volume of parts produced.

5. Analytical Dashboards

Two main panels were developed:

• Production Chart:
  • Information about the piece in progress.
  • Parts produced per shift and day.
  • Real-time OEE indicators.
  • Current and estimated cycle times by matrix.
  • Error predictions per part and associated parameters.
• Maintenance Chart:
  • Monitoring of critical variables (saws, ovens, belts, PSA).
  • Automatic detection of anomalies with date indication and predominant causes.
  • Possibility to define customized alerts with logical conditions (AND/OR), evaluation frequency and control methods (average, maximum, minimum).

Results and Benefits Achieved

The implementation of the FORSEN Project has generated significant improvements in different areas:

• Reduction in the number of unscheduled stops, thanks to the real-time analysis of team behavior.
• Improved traceability of parts and boxes, allowing for higher quality controls and audits.
• Early prediction of part defects, which reduces reprocesses and associated costs.
• Increased operational efficiency through dashboards that allow for agile, data-based decision-making.
• Facilitating the operator's work, by having standardized tools and automated processes for quality control and shutdown management.

Conclusion

The FORSEN case shows how digitalization and the integration of advanced technologies — such as sensing, traceability, artificial intelligence and data analysis — can profoundly transform industrial operations. The solution has allowed a substantial improvement in efficiency, quality control and the ability to anticipate possible failures, consolidating a technological base for continuous improvement and operational excellence.

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