Endless Doorsealing Application
Application of rubber sealing on car door through a robotic arm with gripper and application head.
Cycle production, 60s cycle time, 960 applications per day, 5% error rate.
The Initial Situation:
New door sealing machines were implemented
Initial ramp-up and handover phase was planned to take 3 months
Production is not yet at desired capacity now over a year later
There is a (door sealing) center of competency uniting machine experts to facilitate exchange of information across plants
The machine is not yet operating at the desired capacity and is experiencing significant production issues. The error rate in production process is high (e.g. bubbles, overlaps, gaps), while available data on production errors is very limited. Thus, constant engagement of high-paid machine experts / planners is required. Production quality differences between the plants are not fully explained.
Lack of standardized, digitalized tools and processes
Large amount of manual data. No standardized data format and no strategy to consolidate or digitalize it
Error types and location are not tracked and analyzed systematically
Significant amount of time spent on manual report generation. Information is not shared across the plant network
Lack of availability of
high quality data
Machine error messages are too generic
No way to see errors happening without standing next to the machine
Using regular cameras requires work council approval (GDPR compliance) and results in hundreds of hours of footage
Heavy reliance on machine experts
Heavy reliance on the expertise of the machine operators, maintenance and planners
Individual knowledge and coordination between the stakeholder is essential
Learnings are not systematically captured and leveraged
exchange of information
The exchange of information between the plants is not standardi-zed
Processes around error management are not standardized
Cultural challenges make remote support by machine experts challenging
Installation of VISENSE hardware kits consisting of cameras,
sensors and edge device on machine
Automated cycle detection through a
laser sensor at the
point of rubber
Recording of video footage of the application head and rubber throughput from five different angles.
Tracking of temperature, humidity, and vibration as anomaly indicators (impacting rubber quality)
Triggering of incidents through integration with central production system (machine-triggered incidents) and manual triggering
Visualization of error incidents and corresponding data on the dashboard.
Access to all machines across plants, facilitation of remote support by machine experts. Ability to communicate & share learnings through the platform
Automated report generation, digitalized data outputs such as lop lists (ramp up error tracking list)
Digitalization and support in error root cause identification, ramp-up process, information exchange and capture of learnings, detailed machine monitoring, repeat error prevention.
hours of capacity freed up among operators, maintenance and planners per year.
man-year saved during ramp-up phase of a new or re-calibrated machine.
reduction of time spent on error type and root cause identification.
international travels of machine experts saved per year.
reduction of time needed for reporting through automation.