Production Equipment Data Grouping for Real-Time Tool Life Prediction
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Solution Overview
Problem
Existing data processing systems for production equipment face challenges in real-time data utilization due to the need for pre-stored database information and inability to accurately predict tool life during varied machining processes, especially when producing small quantities of diverse products.
Innovation Solution
A data processing device that acquires and combines reference data and target data in real-time, generating grouped data to facilitate real-time monitoring and tool life prediction by associating operation times and detection periods, allowing for accurate state evaluation and abnormality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is stored in a database in advance for abnormality detection, then abnormality discrimination can be performed using separated signals, but real-time data utilization becomes difficult and preparation time is required
Solution Approach 1:
The system performs preliminary actions by pre-storing types, operation timings, and characteristics of drive components in a database. This allows the abnormality diagnosis system to quickly retrieve and process relevant sensor signals without requiring real-time preparation, thus resolving the contradiction between having prepared data for accurate detection and enabling real-time utilization.
2Loss of information
If multiple types of data are collected for comprehensive analysis, then more information is available for processing, but data processing complexity increases
Solution Approach 1:
The system segments the collected big data into multiple data groups based on drive component types, operation timings, and characteristics. This segmentation allows comprehensive data collection while simplifying processing by handling each segment independently with appropriate analysis methods, thus resolving the contradiction between data completeness and processing complexity.
3Reliability
If traditional tool life prediction methods are used based on cumulative wear evaluation indices, then tool life can be estimated, but accurate prediction during varied machining processes for small quantity production becomes difficult
Solution Approach 1:
The system dynamically adapts the tool life prediction method based on the machining process type and production volume characteristics. For small quantity varied production, it uses real-time sensor data and machine learning models that adapt to changing conditions, whereas for mass production it may use traditional cumulative methods. This dynamic approach resolves the contradiction between prediction accuracy and adaptability to different production scenarios.
Data Source
AI summary
To provide a data processing device of production equipment that can generate data usable by an operator or an administrator by performing processing for a collected plurality of types of data. A data processing device includes a reference-data acquiring unit configured to acquire, in production equipment, reference data including information concerning time in which a reference for grouping of data operates, a target-data acquiring unit configured to acquire target data concerning a state of the production equipment detected by detectors provided in the production equipment, and a combined-data generating unit configured to generate, for each group of the reference data, combined data for each group obtained by combining, with the reference data, data detected in the same period of time as an operation period of time of the reference data in the target data.