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.
Case03Improving quality while reducing costs
Predictive
Maintenance
Equipment level
Sampling inspection cannot completely prevent defect outflow and clarify when the defects occurred.
Tools are replaced in a short period of time because the tool ware state is not visible.
Utilize AI to predict the machining quality through machining diagnosis, detect signs of machining errors, and diagnose tool wear.
With iQ Monozukuri Tool Wear Diagnosis for Machine Tools, create a predictive model from the relation between the machining IoT data and the machining dimension to predict the workpiece quality during machining.
Create a machining error diagnosis model from the machining IoT data to immediately detect signs of abnormality.
Replace tools at the optimal timing by capturing the deterioration trend of the cutting tools and predicting when the tool life is reached.
Prevent defect outflow with quality prediction that can detect quality abnormality immediately when it arises.
Prevent defects by detecting signs of abnormality with the machining error diagnosis.
Improve the operating rate, lower tool costs, and reduce the labor hours needed to replace tools thanks to less tool replacement frequency.
