Grinding Machine Axis Monitoring for Wear Detection During Reference Runs
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Solution Overview
Problem
Existing condition monitoring methods for hard finishing machines, such as grinding machines, are inefficient due to infrequent reference runs, leading to prolonged periods without machine checks and potential machine downtime, and fail to accurately detect wear and tear without disrupting production.
Innovation Solution
Implementing a reference run that includes acceleration and deceleration phases, combined with machine learning or deep learning techniques, particularly using autoencoders, to evaluate the machine's reaction to drive signals, allowing for continuous and precise assessment of wear conditions without interrupting production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reference runs are performed frequently to improve wear detection accuracy, then measurement precision is improved, but productivity deteriorates due to machine downtime
Solution Approach 1:
The system performs preliminary identification runs during commissioning to capture reference signals representing new machine state. These reference signals are stored and used for continuous comparison during operation, enabling wear detection without requiring frequent full reference runs that would cause downtime.
Solution Approach 2:
The monitoring system operates continuously by comparing current axis responses against stored reference signals from identification runs. This continuous monitoring approach replaces periodic interruptive reference runs, maintaining both high measurement precision for wear detection and full production productivity.
2Productivity
If reference runs are performed with long intervals to maintain productivity, then machine capacity is preserved, but reliability deteriorates due to prolonged undetected wear
Solution Approach 1:
The patent replaces mechanical periodic reference runs with an electronic signal processing system that continuously monitors axis responses using spectral analysis and pattern recognition. This substitution enables continuous reliability monitoring without mechanical interruption of production.
Solution Approach 2:
The system introduces intermediary reference signals captured during identification runs as a benchmark for continuous comparison. These reference signals act as a mediator between the operating machine and the monitoring system, enabling continuous reliability assessment without requiring the machine to stop for comparison measurements.
3Measurement precision
If long reference runs are used to improve assessment accuracy, then measurement precision is improved, but temperature effects worsen due to thermal expansion
Solution Approach 1:
The system uses partial identification runs that capture essential frequency characteristics without requiring complete long-duration runs. By identifying critical vibration frequencies and patterns in shortened tests, the system achieves sufficient measurement precision while minimizing thermal effects from prolonged operation.
Solution Approach 2:
Temperature compensation data and thermal expansion characteristics are determined preliminarily during commissioning and stored as reference information. This preliminary characterization allows the system to compensate for thermal effects during operation without requiring additional long reference runs that would exacerbate heating.
4Measurement precision
If spectral analysis is used to detect periodic excitations, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential frequency characteristics and spectral features from the axis response signals that are relevant for wear detection. By focusing on specific frequency bands and spectral patterns associated with mechanical wear, the system achieves high measurement precision while simplifying the processing requirements compared to analyzing the complete frequency spectrum.
Data Source
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AI summary
The invention relates to a method for monitoring the condition of a hard finishing machine, in particular a grinding machine, wherein the hard finishing machine has a number of NC-controlled axes that are actuated during the machining of a workpiece, wherein at least one reference run is carried out on at least one axis for the assessment of the condition of the hard finishing machine, in which a reaction of the axis to a drive signal is measured and evaluated.To enable improved condition monitoring of the hard finishing machine, the invention provides that the reference run includes a phase in which the axis is accelerated and/or decelerated, wherein, to assess the wear condition of the hard finishing machine, the response of the axis to the drive signal is measured and the measured response is compared with expected signal curves that were present when the axis was in proper condition, whereby a statement is derived from the comparison as to whether the axis is in proper condition.