Less is more: Intelligent, sustainable hydraulics saves resources

The current shortages of energy, raw materials and specialist staff are affecting companies around the world. At the same time, climate protection and decarbonization are becoming key priorities for global industry. Intelligent, sustainable and safe hydraulic solutions play an important role in helping machine manufacturers and end users of metal forming and sheet metal working to meet these challenges throughout the entire life cycle.

Software tools and preconfigured modules for hydraulic systems make engineering easier and speed up commissioning. By shifting once mechanical functions to the software, hardware variance can be reduced and concepts can be adapted to meet specific customer- specific requirements quickly.

Even in the design phase, engineers are able to parameterize the digital twins of intelligent hydraulic solutions. On this basis, they can then reduce the commissioning time by transferring the saved settings to the hydraulic modules. Software wizards guide technicians – even those without a specific knowledge of fluid technology – logically through the commissioning process. As a result, they obtain stable and efficient controller settings more quickly.

For end users, the availability and productivity of machines and systems are key factors. Hydraulics scores here with its great robustness and energy efficiency. With their variable-speed drives, modern hydraulic power units and ready-to-install self-contained actuators set new standards when it comes to power and energy efficiency. They reduce the power consumption of hydraulic systems by up to 80 percent. Intelligent die cushion controllers for the lower piston recover energy and feed it back into the machine to reduce energy consumption.

Data-based maintenance concepts are becoming increasingly important as a way of avoiding unplanned downtimes. Connected hydraulic systems monitor component wear, allow real-time access to current and historical data and analyze these data predictively.

With the help of machine learning, the data obtained can be used to come up with maintenance recommendations early on, thus allowing end users to replace the components affected as planned. By avoiding unplanned downtime and with improved maintenance planning, considerable costs can be saved and productivity can be increased. In many cases, avoiding an unplanned system standstill means that the money invested is immediately recouped.