Working Tool Abnormality Detection With Selective Measurement Data
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Solution Overview
Problem
Existing abnormality detection systems for working tools face challenges in efficiently processing large amounts of measurement data, leading to increased memory capacity requirements, prolonged analysis times, and reduced accuracy, especially when detecting abnormalities based on position or distance.
Innovation Solution
An abnormality detection apparatus that stores correlations between machining process features and tool conditions for each working tool type, allowing for the collection and analysis of only the most effective measurement data, thereby reducing data volume, processing time, and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of measurement data items are collected to improve abnormality detection accuracy, then the detection accuracy is improved, but the memory capacity required increases and processing time is prolonged
Solution Approach 1:
The patent extracts and collects only the necessary measurement data items relevant to each working tool type, rather than collecting all possible measurement data. This selective extraction approach maintains detection accuracy while significantly reducing the volume of data that needs to be stored and processed
Solution Approach 2:
The patent segments the measurement data collection process by working tool type, where different sets of measurement data are collected for different tool types (e.g., drills, end mills, inserts). This segmentation allows the system to collect only the specific measurement data needed for each tool type, reducing overall data volume while maintaining comprehensive monitoring coverage
2Measurement precision
If more measurement data items are collected to improve detection accuracy, then the detection accuracy is improved, but the processing time is prolonged
Solution Approach 1:
The patent extracts only the essential measurement data items needed for detecting abnormalities in each working tool type, eliminating unnecessary data collection. This extraction principle directly reduces the amount of data requiring analysis, thereby shortening processing time while preserving detection accuracy
Solution Approach 2:
The patent dynamically adjusts the set of measurement data items collected based on the current working tool type being monitored. This dynamic adaptation allows the system to optimize data collection in real-time, collecting only what is necessary for the current tool, thus reducing processing time without sacrificing detection accuracy
3Quantity of substance
If a larger sample period is used to reduce data collection volume, then the data collection burden is reduced, but the measurement accuracy is lowered
Solution Approach 1:
The patent extracts the essential measurement parameters specific to each working tool type, ensuring that even with a reduced sample period, the collected data maintains high accuracy for detecting tool abnormalities. By focusing on critical parameters rather than comprehensive data collection, the system achieves both reduced volume and maintained precision
Data Source
AI summary
An abnormality detection apparatus for working tools configured to be used in a machining process performed by a machine tool, the abnormality detection apparatus includes a storage portion which previously stores correlations between features of a plurality of operating portions relation to the machining process performed by the machine tool, and a tool condition of each of a plurality of working tool types, and a tool condition determining portion which determines the tool condition of the working tools based on the correlations.