Machine Tool Spindle Abnormality Detection
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Solution Overview
Problem
Existing machine tools lack the capability to detect abnormalities caused by the detachment of a spindle, which can lead to decreased machining precision if not addressed by specialists, as current technologies can only predict bearing abnormalities and not spindle detachment.
Innovation Solution
A machine tool equipped with a magnetic encoder sensor that outputs signals with predetermined phase shifts during spindle rotation and halt, allowing a controller to detect abnormalities by monitoring phase differences, and outputs a warning through an output device, even when the primary power supply is off, using a secondary power supply for the controller.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a sensor and controller are added to detect spindle detachment, then abnormality detection capability is improved, but device complexity increases
Solution Approach 1:
The magnetic encoder sensor serves dual purposes: it detects both spindle rotation position/speed and spindle detachment abnormalities. The controller also performs multiple functions including normal spindle control and abnormality detection. This multi-functionality approach adds detection capability without requiring completely separate dedicated detection systems, thus improving reliability while limiting the increase in device complexity.
Solution Approach 2:
The system uses its own existing sensor signals (from the magnetic encoder) to detect abnormalities. The controller analyzes phase differences in signals already being generated for normal operation, enabling self-diagnosis capability. This self-service approach allows the system to detect its own abnormalities without requiring external detection equipment, improving reliability while minimizing added complexity.
2Measurement precision
If phase difference detection method is used, then detection precision is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The controller continuously monitors the phase difference between signals from the magnetic encoder and compares it against the expected predetermined phase difference. This feedback mechanism enables precise detection of spindle detachment by identifying when the actual phase relationship deviates from the normal relationship, achieving high detection precision through systematic signal analysis.
Solution Approach 2:
The system establishes the predetermined phase difference relationship during normal operation before any abnormality occurs. By pre-establishing what the normal phase relationship should be, the system can quickly and accurately detect abnormalities by comparing current signals against this predetermined reference, simplifying the detection process while maintaining high precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables the detection of spindle detachment abnormalities, preventing decreased machining precision by alerting maintenance personnel and ensuring only specialists replace critical components, thereby maintaining intrinsic machining precision.
Implementation Method 1
a sensor configured to output an electrical signal according to a rotation angle of the spindle; Preferably, the sensor is a magnetic encoder
Data Source
Figure 1
Figure 2(A)~2(C)
Figure 3
AI summary
A machine tool (100) is provided capable of detecting an abnormality caused by detaching a spindle (42). The machine tool (100) includes: the spindle (42) configured to rotate a workpiece or a tool; a sensor (47) configured to output a signal according to a rotation angle of the spindle (42); and a controller (51) configured to detect occurrence of an abnormality in at least one of the spindle (42) and the sensor (47) when an electrical signal different from the signal output during each of rotation of the spindle (42) and halt of the spindle (42) is output from the sensor (47).