Reduction of engineering time for recovery

Our unique planning technology virtually simulates the optimal path of multiple robots. For example, when a spot welding robot for an automobile body suddenly stops during operation in a process where 8 to 10 robots are placed close to each other, it may then be necessary to return the robot to a zero position. Regarding which robot arm can be moved first to return to the origin without bumping into other arms, it takes many hours even for a skilled worker to operate it with a programming pendant. However, by automatically generating the optimal path with the planning function, the simulation can be completed in a few minutes, greatly reducing engineering time for recovery.

This technology can also be applied to automatic path generation when field conditions are uncertain, such as picking of randomly placed objects.

Other solutions

  • Production Production
    Production
  • Quality Quality
    Quality
  • Maintenance Maintenance
    Maintenance

Shorter installation time

AI picking

AI Picking

By utilizing the AI technology “Alliom” developed by Yaskawa Group, the installation time to actual operation is drastically shortened, and the accuracy to actual machine can also be improved.

Production Production
 

Flexible production

High Variety and Variable Quantity Production

High Variety and Variable Quantity Production

By using digital data to manage automated production lines, setup can be prepared automatically without manual intervention, enabling high variety and variable quantity production from a minimum of one unit.

Production Production
 
Autonomous distributed manufacturing

Autonomous Distributed Manufacturing

Digital data such as the torque value, vibration value, and temperature of the servomotor is absorbed into the controller, and the robot can think for itself how to move.

Production Production
 

Accuracy improvement

Accuracy improvement of defect cause analysis Yaskawa case

Accuracy Improvement of Defect Cause Analysis < Yaskawa Case >

By “visualizing” the operation status of equipment/devices with Yaskawa Cockpit, it is possible to identify the root cause by comparing the normal value and abnormal value of the data in the factor analysis for defects in production.

Quality Quality
 

Quality inspection

Automated product quality assessment with AI

Automated Product Quality Assessment with AI

When the quality inspection process is labor-saving, the use of an image judgement service that utilizes AI technology such as deep learning makes it possible to automatically determine complex No Good patterns with the same level of accuracy as humans.

Quality Quality
 

Failure prediction

Predictive failure diagnosis of equipment

Predictive Failure Diagnosis of Equipment

To reduce downtime to zero by performing planned maintenance in anticipation of equipment failure due to wear, etc., in response to concerns that production may become impossible due to the sudden shutdown or something else.

Maintenance Maintenance
 

Recovery support

Investigating the cause of equipment failure

Investigating the Cause of Equipment Failure

By acquiring quality data on when, with which equipment, and how it was processed, it is possible to accurately identify the cause of the problem between which equipment and equipment at the time of failure.

Maintenance Maintenance