Machine Tool State Identification Using Position and Speed Changes
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Solution Overview
Problem
Current methods for identifying the state of machine tools, such as CNC machines, are complex and require extensive training data and neural networks, making it difficult to accurately determine different normal operating states and analyze unprofitable downtime in detail.
Innovation Solution
A method that records and evaluates the spatial and temporal positions of tools and tool holders using sensors, calculating position changes and speed changes, allowing for the identification of operating states without the need for neural networks or extensive training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks and extensive training data are used for state identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts and analyzes only the most relevant features from sensor data (position changes and speed changes of tool and tool holder) rather than using all available data with complex neural networks. This selective feature extraction maintains identification accuracy while significantly reducing system complexity and computational requirements.
Solution Approach 2:
The patent replaces expensive, complex neural network models with simple, computationally inexpensive evaluation rules that can be executed efficiently. The method uses basic mathematical operations on position and speed data rather than resource-intensive machine learning models, making the system more accessible and easier to implement.
2Measurement precision
If complex evaluation methods are used, then state identification accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically identifies operating states through predefined evaluation rules that assess position and speed changes without requiring manual intervention or complex configuration. The method self-adapts to different machine tools by monitoring fundamental motion parameters, making it easy to operate across various applications.
3Loss of information
If detailed continuous documentation of all events is implemented, then loss of information is reduced, but loss of time increases
Solution Approach 1:
The patent extracts only the essential information needed for state identification (position and speed changes) rather than documenting all machine events continuously. This selective monitoring approach maintains complete documentation of relevant operating states while significantly reducing data processing time and computational burden.
Solution Approach 2:
The method uses partial monitoring of key parameters (position and speed) rather than exhaustive documentation of all machine events. This partial action approach provides sufficient information for accurate state identification without the time cost of complete continuous documentation.
Data Source
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AI summary
The invention relates to a method (100) for the offline and/or online identification of a state of a machine tool (WM), at least one of its tools (WZ) or at least one workpiece (WS) machined therein, wherein the machine tool (WM) has sensors by means of which at least the position of the tool and/or the tool holder can be detected in a spatially and time resolved manner, the method comprising the following steps: a) detecting or providing (102) positions P of the tool and/or a tool holder (WH1, WH2) at a series of points in time i, i=1...n; b) determining (104), for the series of points in time i, b1) a series of position changes Δmi in accordance with formula (I) and b2) a series of speed changes Δvi formula (II) with formula (III) and formula (IV); c) identifying (110) the state c1) of the tool (WZ), c2) of the tool holder (WH1, WH2), c3) of the machine tool (WM) and/or c4) of the workpiece (WS) machined in the machine tool (WM) on the basis of the position changes Δmi and the speed changes Δvi.