Sensor platform for the automation of ironing in a university industrial environment
Company
The University of Seville is a leading institution in research, development, and technological innovation.
With the aim of moving towards a more automated and intelligent workshop model, the university needed to validate a technological solution that would enable the automatic identification, processing, and management of plates, reducing manual intervention and improving the traceability of the process.
Challenge
Design and implement a pilot R&D project in sensorization that would enable the automation of plate processing using industrial cranes.
The solution had to integrate with the workshop's existing systems, be flexible enough for an experimental environment, and serve as a basis for future advanced automation developments.
Problem
- Manual plate processing
Plate identification and management was done manually, which increased operating times and the risk of human error. - Lack of automation
The cranes did not have vision or automatic reading systems that would allow them to be integrated into digitized workflows. - Absence of automatic traceability
The identification codes on the plates were not automatically used to feed the workshop's management and control systems. - Limitations for advanced R&D projects
The lack of sensors and data capture limited experimentation in automation, robotics, and intelligent processes.
Solution
How we do it?
A pilot project involving sensorization and artificial vision was developed with a view to automating the workshop, combining industrial cameras, QR code reading, and software integration with the University of Seville's systems.
- Sensorization and artificial vision
Cameras were strategically installed on the cranes responsible for processing the plates, allowing images to be captured in real time during the operation. - Automatic QR code reading
The system automatically identifies the QR codes on the plates, extracting the information necessary for processing without human intervention. - Integration with existing systems
The cameras and vision system were integrated with the university's systems, allowing the captured information to feed directly into the workshop's management and control processes. - Workflow automation
Automatic plate identification enables automatic treatment, classification, or tracking processes, laying the foundation for a smart workshop. - R&D pilot approach
The solution was designed as an experimental, scalable, and adaptable environment aimed at validating technologies and methodologies for future industrial automation projects.
Results
- Automation of plate processing using machine vision
- Significant reduction in manual tasks in the workshop
- Automatic and reliable identification using QR codes
- Improved traceability and process control
- Technological basis for future R&D developments in industrial automation
- Real integration of sensorization and digital systems in a university environment