Artificial intelligence is not just data

Well, let's talk about which doors digitalisation opens – which specific opportunities it makes available (compared to the past).

Smaller quantities, faster product changeovers, increased flexibility and quality requirements combined with seamless documentation and a lack of skilled workers – these are current challenges that need to be solved. This can only be achieved with the help of automated and digital sequences. Modern machines will continue to be high-quality, durable, capable of integration and efficient. But they also have flexible assistance and control modules that form the basis for a 0-ppm strategy.

With the Gestica control system, the use of two physically separate host computers - one for operation and visualisation and the other for controlling and regulating the processes in real time - opens up possibilities that were previously only available to a limited extent with one controller. Good examples of this are 3D views and assistance functions. I am sure that with our Gestica we are taking injection moulding to a new level. It will be a whole new experience for digitalisation and for our customers' control concepts. As the 'brain' and seal of quality of the machine, the Gestica offers an enormous amount of flexibility, especially with frequent mould and product changeovers, or when users want to optimise their processes and keep them stable. They can use it to make excellent process analyses and optimise sequences and cycle time.

One example of an 'intelligent' assistance function, thanks to which the machine 'knows' the part it is producing, is the axw Control FillAssist - a valuable tool for setting up a mould to speed up the process and make it more reliable at the same time. The function works by animating the filling level of the part in relation to the current position of the screw as a 3D graphic in real time. So you can run filling simulations directly on the machine, even while it is working. Three other control systems – the ScrewPilot, PressurePilot, and ReferencePilot – enable adaptively regulated injection. These are control strategies that build on each other for central quality requirements such as constant shot weights and uniform mould filling, irrespective of viscosity fluctuations.

Thanks to aXw Control MeltAssist, the Gestica identifies the built-in cylinder module via a chip and uses this data to automatically calculate and monitor parameters such as plasticising utilisation and dwell times. In addition, the running performance and plasticising load are stored. This enables performance-based or predictive maintenance, efficient service, and saves valuable working time in everyday production.

All assistance functions have one thing in common: they contain the expert knowledge and experience of experienced operators in a stored and retrievable form, making operation very simple; no special software knowledge is required. This is particularly advantageous when operating and service staff change frequently, and to compensate for the different levels of know-how worldwide.

As injection molding processes become more digitalised, the amount of data increases. Data contains a lot of information but this is often not recognisable at first glance. How does using this information support processes optimisation?

Data is the driver of digitalisation and, so to speak, the gold of the future - even of the present. But for most tasks, it is not 'big' data that is needed, but 'smart' data. For example, in order to achieve the goal: 'machine knows part' by bringing simulation and reality together, a large amount of process data must be collected and evaluated and conclusions drawn with a great deal of expertise. This is something that we are working hard on.

The machine technology's real-time network system, a 'smart' control system, and data analyses form the basis for reliable process monitoring. With appropriate sensors, the conditions of parts susceptible to wear can be recorded (condition monitoring) and predictive maintenance enabled. This minimises the amount of maintenance required and at the same time ensures long, stable, and fault-free run times. In this way, batch to batch variations in the material or wear in hydraulic valves or vacuum generators, for example, can be detected and predicted. The control system informs the operator in good time as soon as the parts susceptible to wear need to be replaced. Another example is the load-dependent, automated lubrication of toggles on electrical machines depending on the application and parameter settings during ongoing production.

Generally, there are two options for data analysis. In an 'on-cloud' solution, the collected data is transmitted to a higher-level central system in the cloud, where it is analysed and later communicated back to the machine. This requires an elaborate IT infrastructure. Arburg, on the other hand, relies on an 'on-device' solution, i.e. the data is analysed and evaluated directly in the Gestica operating unit, where data models trained by us are located, so Arburg's process knowledge is also directly incorporated. The control of the machine itself remains unaffected. In our view, higher-level systems are more suitable for topics such as production planning and long-term analyses.

At Arburg, more than 90 per cent of all sequences are already digital today - from setting up the machines to the mobile planning of service technician assignments. Interlinking along the entire value chain makes production-relevant information actively available at any time and any place. Thanks to meticulous data maintenance, there has long been a digital twin for every machine. The data stored for a machine is also made accessible to customers via the customer portal. Every customer is assigned their own virtual room in arburgXworld, to which only they have the 'key'.

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