Electronic Equipment Industry Pilot (CONTINENTAL)

Electronic equipment industry pilot use case aims at improving the maintenance process of the equipment in the production line of CONTINENTAL’s factory. It is desired to support maintenance operators in their work, providing them with dashboards, improved manuals, work instructions, technical drawings of the equipment using digitalization and Augmented Reality.
Another objective in this use case is to reduce maintenance cost in terms of head count time used for preventive, corrective and predictive maintenance in the final assembly line.
Also, CONTINENTAL intends to apply the KYKLOS 4.0 capabilities to implement 3D printing technology and CAD data visualization for maintenance interventions in order to reduce reaction time in maintenance.
And finally, CONTINENTAL will use KYKLOS 4.0 capabilities in order to enhance their existing production lines making use of a digital twin representation that will also provide an optimization version of the production lines analysed.

The preventive maintenance process that will be improved using the KYKLOS 4.0 platform will provide the following new functionalities:

• Automatic triggering of maintenance equipment schedule (for line of production/ assembling and also, for equipment’ components). Triggering of preventive maintenance is based on production planning.
• Better support and guidance of the maintenance task using Augmented Reality technology. Pictures, workflow instructions, etc. in digital format will be used to create a better assistance to the operator. At this moment, the instructions are technical, but the descriptive visual part is missing.
• Information related to real time equipment status will be shown; the equipment to be examined together with the problem or the condition will be shown.
• Repository of issues for each equipment when maintenance is performed.
• Automatic spare parts identification and delivery based on planning.

The reactive or corrective maintenance process that will be improved using the KYKLOS 4.0 platform will have to meet at least the following requirements:

• Connection with the equipment sensors to get real time data from the production line equipment.
• Automatic issue triggering from equipment (such as failure, damage, break down).
• Automatic dashboards and escalation process in case of a break down.
• Automatic tracking and monitoring of the corrective maintenance process, based on a ticketing system, which allows to know who is responsible of the task, how much time has been taken to solve it, and specifics of the action done.
• Better support and guidance of the maintenance task using Augmented Reality technology.

The predictive maintenance process that will be developed using the KYKLOS 4.0 platform will have to meet at least the following requirements:

• Monitoring of the line and possible defects
• Prediction based on historical data which includes information about what component has a high risk of damage (e.g. how many times has been replaced, critical for equipment, etc.).
• Prediction of what machine, device or component should be preventively repaired or exchanged, before any break down happens.
• Prediction of the Remaining Useful Life (RUL) of a device or component.
• Notifications and alarms will be raised in case of subcomponents lifetime is coming to end (Cylinders, pins, etc).
• Overview in terms of cost optimization for spare parts, stock adjustment for spare parts, by finding the right balance for in terms of cost versus number of failures in the Final Assembly Line.

The 3D printing process that is desired to be improved using the KYKLOS 4.0 platform will have to meet the following requirements:

• Implementation of 3D printing technology and CAD data visualization for Maintenance interventions; if a spare part is needed, it must be automatically checked if it can be produced with the 3D printing technology existing in the company, before being ordered to an external supplier. This will reduce reaction time on maintenance.
• Development of a knowledge database including type of materials, equipment, and parameters to build 3D printed ESD components for CONTINENTAL’s prototype and components of production equipment.

Regarding the production line optimization:

• Digital twin technology will be used which monitors production workflows.
• Modelling of the process, involved systems, sensor/actuation record and involved actors will be performed from all the heterogeneous data collected from the production lines, using semantic approach.
• Machine Learning algorithms will support the analysis of the equipment in the production lines for the digital twin and the optimization process.
• Potential optimizations for the process and/or the involved equipment, systems, actors, etc. will be identified and reported.