Advanced Machine Engineering for Battery

To adapt to a rapidly evolving market, transparency and agility are key. Storing and sourcing data within the cloud is one way to standardize traceability; to take it a step further, you can use an open-system software solution to integrate your technology stack. This means that when a change occurs in one system, it automatically populates across your other systems, ensuring the latest requirements, modules and data are being used, in turn creating a standardized process for plug-and-produce designs.

You can get machines to market in less time by integrating your mechanical, electrical and simulation domains, then prioritizing design and the Digital Twin. By translating your design into the Digital Twin, a holistic 3D mockup of a machine is realized, helping you simulate, test and validate a battery machine’s performance virtually. This virtual environment saves you time and money while the cost of change is low, while also reducing the need for physical commissioning. This same logic applies to a machine’s PLC code, any associated applications and virtual trainings.

The most notable way to improve machine performance without a loss in quality is by using operational data. By fitting and retrofitting machines with sensors and edge-sharing devices, you can capture important analytics about machine performance. Then, by feeding this data into the Digital Twin and simulation software, you gain a closed feedback loop informing where realistic improvements can be made. This data further informs future machine design decisions, where energy efficiency, speed, waste and other factors can be considered, in addition to prioritizing remote maintenance services.