Machine Tool Abnormality Detection With Dynamic Motor Thresholds
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Solution Overview
Problem
Existing numerical controllers face challenges in accurately detecting machine tool abnormalities due to varying simulation accuracy, which necessitates broadening the detection threshold, thereby reducing the accuracy of abnormality detection.
Innovation Solution
An abnormality determination system that includes a prereader, anticipated position calculator, interference checker, motor information estimator, detection threshold determination unit, motor measuring unit, and state determination unit, which dynamically adjusts the detection threshold based on the accuracy of the simulation to ensure accurate abnormality detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the detection threshold is broadened to account for low simulation accuracy, then the system can handle cases with limited processing time, but the accuracy of abnormality detection is reduced
Solution Approach 1:
The detection threshold is made dynamic rather than fixed. The threshold determination unit adjusts the threshold based on the actual simulation accuracy calculated from motor estimation information. When simulation accuracy is high, a stricter threshold can be applied; when accuracy is low, the threshold is relaxed accordingly. This dynamic adjustment resolves the contradiction by allowing the system to optimize detection accuracy for each specific simulation case rather than using a conservative fixed threshold.
Solution Approach 2:
The system changes the detection threshold parameter based on simulation accuracy conditions. By calculating motor estimation information from simulation data and comparing it with actual motor values, the system determines simulation accuracy and adjusts the detection threshold parameter accordingly. This parameter change allows the system to maintain high detection accuracy when simulation conditions permit while still functioning with limited processing time when necessary.
2Measurement precision
If the simulation accuracy is increased to improve abnormality detection, then detection accuracy improves, but processing time increases
Solution Approach 1:
The system implements feedback by calculating simulation accuracy through motor information estimation and using this feedback to adjust the detection threshold. The motor information estimator compares simulated motor values with actual motor measurements, calculates accuracy metrics, and feeds this information back to the threshold determination unit. This feedback mechanism allows the system to optimize the balance between simulation accuracy and processing time by adjusting thresholds based on actual performance rather than using fixed conservative settings.
Solution Approach 2:
The system performs preliminary simulation before actual machining operations to establish detection thresholds. By prereading the machining program and performing simulation in advance, the system can calculate anticipated positions and motor information without interfering with real-time processing. This preliminary action allows high-accuracy simulation to be performed when time permits, while the results are stored and used during actual operation, resolving the time-accuracy contradiction.
Data Source
AI summary
Provided are an abnormality determination system and program that make it possible to accurately detect abnormalities at a machine tool. An abnormality determination system that comprises a detection threshold determination unit that determines a detection threshold for detecting abnormalities at the machine tool on the basis of a motor inference information and an inference precision, a motor measurement unit that acquires motor measurement information about a motor, and a state determination unit that determines the state of the machine tool on the basis of the motor measurement information and the detection threshold.