Federal Eco Foam

Datwyler is a leading provider of high quality, system-critical elastomer components active in industries such as healthcare, mobility, oil & gas and food & beverages. With its recognised core competencies and technological leadership, the company delivers added value to customers in the markets served. With more than 20 operating companies in 2021, sales in over 100 countries and more than 7’000 employees the Dätwyler Group generates annual sales of more than CHF 1’000 million.

The elastomer processing in these industries requires a detailed production process, each with its particular specifications. Automation of processes can lead to higher consistency in quality and output, but CapEx investments in a heavily loaded production area need to be first-time-right in order not to jeopardise the foreseen mid-term output.

“Connectivity is as a matter of fact the first mandatory step to start and gather value.”

In the past, the commissioning of new equipment was mainly done on-site after installation. With this way of working there was always a risk for delays in SOP due to material flow issues, debugging of programs when e.g. the new machines had to be connected with an existing system, training of maintenance and operators to reduce the timeline of a project and improve the final design of the system, the following digital systems have been introduced:

Simulation

Virtual commissioning based on simulation

Connectivity

Simulation and Virtual Commissioning

Based on FMEA (Failure Mode and Effects Analysis), process expertise and technical feedback of the suppliers, a number of operational scenarios are defined. These scenarios will all be simulated and evaluated to identify wrong or inefficient actions and resolve them before the machine is produced. This is even more important in a complex fully automated cell where the desired productivity can be achieved only with a perfect orchestration of all the involved elements: process machines, conveyors, handling devices, storage areas and human operators.

The dynamic simulation allows also to estimate with high accuracy the actual throughput of the whole system and then properly calculate the payback.
Once the simulation is validated across all scenarios, the outcome can be used as a base for virtual commissioning.

The simulation and the virtual commissioning create a virtual representation of the system: the so-called ‘digital twin’.

With virtual commissioning the time to standard operating can be further reduced as the automation programming activities can be parallelised when the machine is still under construction: every single element controlled by a PLC, an actuator or a sensor can be simulated in the digital twin, verified and, if the case, corrected, without the needs to have the machine physically present.

When the real machine will be ready and installed, the final real commissioning will happen faster with no surprises as most of the issues, typical in the ramp-up of complex systems, have been already solved in the virtual world.

Advantages of the virtual simulation:

Validation of complex systems with real data, at the early design and production stages

Shows bottlenecks which can result in changes (design, lay-out) and thus productivity improvements

Evaluation of alternatives on evidence-based on what-if approach (i.e. prediction of the impact of modifications on production performance)

Connectivity

The next step, after commissioning, is to connect the automated production system to the company network to collect data for analysis of cell performances and behaviour of the equipment.

Connectivity is as a matter of fact the first mandatory step to start and gather value.

The second step is to visualise such data in an effective and intuitive way, and begin to look at how the machine is operating, what is happening during production and, most importantly, why a downtime has happened. We want to turn data into information, gathering value through the interpretation of such important evidence.

Eventually, when enough proper data will be collected, new deep analytics techniques can be accessed to identify what also the most experienced operator cannot see: hidden correlation between parameters and variables. This innovative approach will then open the doors for the shift from reactive to predictive, that is understanding in advance when the machine is going to need intervention right before the breakdown (predictive maintenance) and when the production system is drifting towards not the adequate quality of the products, adjusting on time the relevant machine settings (predictive quality).

And all this in a secure way, with proper segregation of the shop floor network and the latest tool to avoid any kind of external interference that may impact production or quality.

The final goal is to extend this approach to all the machines and have all the machines and production cells as a valuable source of information to move from an event-driven organisation towards a data-driven organisation.