Solutions
Data Collection/Analysis
The Cycle for shop floor Improvement With Data Utilization
Improving productivity, quality, and energy efficiency by utilizing shop floor data to find the key to solving production issues and promoting improvements
Case15Replacing tools of machine tools only when necessary
Diagnosis
Predict cutting tool life based on usage conditions to determine a replacement time so tools can be used for their life expectancy while maintaining a high precision for machining.
iQ Monozukuri Tool Wear Diagnosis for Machine Tools
Tool life diagnosis
By collecting data from the CNC in real time and knowing the trends of the cutting/grinding parts, the system automatically detects changes caused by tool wear and tool defects or breakage. The number of tool replacements are reduced when compared to periodic replacements, and a high precision of machining is maintained.
Creating a machining prediction model by machine learning
By successfully detecting minute changes in the tool edge, the AI technology learns*1 the relations between the characteristics of the cutting/grinding parts and the machining dimensions and surface roughness to create a machining predictive model.
With this predictive model, any faults previously undetected in sampling inspections are detected, and defects occurring before a machining inspection takes place can be prevented.
*1.Machine learning is possible for engineers with no experience in data science when using Advanced Data Science Tool (sold separately). Advanced Data Science Tool is a software that interacts with iQ Monozukuri Tool Wear Diagnosis for Machine Tools, enabling big data to be utilized on a computer.
