SolutionsTotal Maintenance
In today’s dramatically changing business environment, the impact of sudden equipment downtime on corporate profits is enormous, and an increasing number of businesses are implementing planned equipment maintenance with the aim of achieving non-stop factories.
Meanwhile, the manufacturing industry faces another major issue of passing down the expertise of highly experienced employees.
Case04Monitoring changes in machine tool condition
Predictive
Maintenance
Equipment level
Although the same machining task is executed with the same model, quality abnormality occasionally occurs only on unit 2.
Confirm the machine tool condition changes through the machining load changes, which helps users perform predictive maintenance.
The Advanced Data Science Tool* monitors the variations (standard deviation) in the machining load (feature value) for the same-type machine tools executing the same machining task. The monitoring is executed at regular intervals so that users can confirm any changes in the machine condition that could become a factor of defective workpiece. This allows users to ascertain when to conduct overhaul and maintenance, as well as check the effect of maintenance conducted.
* The Advanced Data Science Tool is a software that links to iQ Monozukuri Tool Wear Diagnosis for Machine Tools to utilize IoT data for supporting tool diagnosis, equipment maintenance, statistical analysis, etc.
By monitoring the difference of the machining load variations between the machine tools and conducting maintenance for the machine tool with large variations as required, it is possible to reduce stoppage caused by failure.
