FA Application Package Tool Wear Diagnosis for Machine Tools
We Provide Various Functions to Support the Realization of Digital Transformation
Set the parameters for connection and data collection according to the target device and then perform data collection.
Settings during installation
System settings
Configure the common settings for the diagnosis system (diagnosis system ID and password settings).
Common settings screen
Machine settings
The machine tool communication method and data cleansing conditions can be set for each machine to enable the collection of machining status data.
Machine settings screen
Model settings
Register the diagnosis model for each machining condition.
The automatic model registration function allows you to automatically register models according to the received machining conditions.
Model settings screen
Collection of machining data
Collect machining data of each machine tools in real-time for storage or comparison purposes. （compare differences between deteriorated tools and new tools）
Machining data collection screen
Machine status monitoring
On the Machine state detail display, the data received from the CNC and the collected data are displayed in real time.
Machine details status screen
Waveform and trend display function
The Advanced Data Science Tool allows you to display stored waveform and trend data over a specified selection data. This allows you to check the wear state of the tool.
Machining data collection screen
Data transfer
Advanced Data Science Tool
The set threshold is automatically calculated from the collected data, and the optimized diagnosis threshold is then set in the system.
Diagnosis threshold setting
Advanced Data Science Tool
Section extraction setting
While checking the cutting torque waveform, it is possible to set the selection extraction conditions from the collected waveform data.
Diagnosis threshold setting
The statistical analysis function automatically calculates recommended thresholds from trends in the feature values.
Model detail setting
It is possible to set the data cleansing conditions, data processing conditions, and diagnostic feature values for the model according to your diagnostic requirements.
Model detail setting screen
Diagnosis threshold setting
It is possible to use the values calculated by the Advanced Data Science Tool or custom values from the user.
Diagnosis threshold setting screen
Tool wear diagnosis
Analyze Management of Trend Data for Various Feature Values
Various feature values are automatically calculated, and their trend data is displayed. The threshold deviation is then determined according to the calculation results.
Analyze management of trend data for various feature values
Tool wear diagnosis
The system can predict the tool service life according to the state of wear and notify the user of the estimated remaining count of machines of the tool.
Visualization of tool wear
Machining abnormality diagnosis
In the case of a tool or machining abnormality, a threshold deviation judgment is made and an alert is issued.
Visualization of tool abnormalities
Machine learning algorithms are applied to inspection and machining data to enable the prediction of machining quality.
Quality prediction
Transmission of internal measurement values from machine
Dimension data measured by a CNC device can be transmitted to the Advanced Data Science Tool
Internal measurement value transmission screen
Data transfer
Advanced Data Science Tool
Analysis of the relationship between inspection and machining data
n the Advanced Data Science Tool, the machining and measurement data (machining quality) are linked according to the production serial number information.
Model creation and evaluation
From the results of correlation analysis between the machining quality and the collected machining data, the feature values of the optimum target for learning are automatically selected, and a predictive model is automatically created by machine learning.
Predictive model and diagnosis threshold settings
The Advanced Data Science Tool allows you to set the calculated predictive model and the diagnosis threshold calculated from feature value trends.
Advanced Data Science Tool
Predictive model
Wear diagnosis model settings screen
Measured value prediction
Immediately after machining, the predicted measured value (quality indicator) is calculated from the predictive model. The system then calculates the threshold deviation and the estimated remaining count of machines.
Measured value prediction screen
Provides support for tool replacement that utilizes tools up to the end of their service life
Tool replacement information
The system is able to display the tool usage status.(To help with the tool change process, the system can trigger an alarm when it is time to change the tool.)
Tool replacement information screen
Alarm history
The system is able to display tool error messages and tool change alarms.
Alarm history screen
Improvement of cycle time and tool life through optimization of machining conditions
Maximum Average Load Comparison, Maximum Integral Value Comparison
By comparing machining condition for the same tool between machining programs, optimizes machining conditions such as cutting speed, feed amount, cutting depth, etc. to support improvement of cycle time.
Maximum average load comparison screen
Maximum integral value comparison screen
Waveform Comparison
Optimal machining conditions can be confirmed by comparison of changes in time load is placed on tool.
Waveform comparison screen