A 3D model allows users to easily check for forming load changes during each press stroke.
Aside from load data, it can also display vibration and pressure waveforms from various locations on the press. It can verify and manage forming quality.

It displays the main motor energization time and operating time, the SPM setting, the production quantities, and the die change times, etc., and it sends out an email notification when a fault occurs. The system makes it easy to understand which equipment is experiencing faults and what type of faults have occurred most frequently, thereby enabling users to make improvements to increase utilization rates.

It can also display the number of fault occurrences for each product.
If a time period is specified, it is possible to see a ranked list of dies that have experienced frequent faults.

Displays daily operation information.
Because information such as "Production Quantities,' 'Operation Start/Stop Times,' 'Total Utilization Time,' and the 'Strokes Per Hour (SPH*)' can be outputted, it is easy to also utilize this as a simple daily report, which also helps reduce the daily workload of operators.

• The total production volume achieved in 1 hour of operation when no faults, etc., have occurred.

It displays a list of registered parts and the remaining service life of each part.
This enables planned maintenance, which translated into less downtime. The system also includes a feature that sends out email notifications about part replacements and inspection intervals.

The load application point is calculated from the press load waveform and is visually displayed for each cycle.
Because anomalies that are outside of the normal data range can be seen at a glance, it enables users to perform die maintenance in a timely manner.

The die monitor app analyzes die-related data and continuously monitors current die conditions. The app can also predict die life, enabling users to perform die maintenance in a timely manner.

Based on a model gathered from normal operation data, AI monitors for anomalies between predicted values and measured values.
It is possible for the app to predict signs of failure at an early stage because it can detect very small anomalies that would be difficult for humans to notice.

This generative AI feature answers user's questions about operation methods, resetting methods, and forming methods based on AIDA's wealth of accumulated knowledge.
This allow users to quickly acquire the information they are seeking.

Fault occurrence locations are highlighted on a 3D model, making it possible to even pinpoint fault locations that are not externally visible.
When the highlighted location is clicked, the fault details and a manual containing fault resetting information will be displayed. This enables quick and precise inspections.

When an operator approaches the operation panel HMI, the system immediately cross-checks their face with the registered face image data, and press operation will be enabled if the images match.
This prevents erroneous operations by unauthorized personnel who are not registered in the facial recognition system.

Performs offline simulations to automatically calculate transfer press slide motions that will not interfere with the motions of the transfer feeder.
This reduces adjustment tasks on the floor and enables rapid production startup. It also improves productivity by enabling fully optimized motion settings.

This system performs offline tandem line simulations to check the phasing between the motions of the various line equipment and to check for any interference.
This reduces adjustment tasks on the floor and enables rapid production startup. It also improves productivity by enabling fully optimized motion settings.